
[38;5;12m                             [39m[38;2;255;187;0m[1m[4mAwesome Computer Vision: [0m[38;5;14m[1m[4m![0m[38;2;255;187;0m[1m[4mAwesome[0m[38;5;14m[1m[4m (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;2;255;187;0m[1m[4m (https://github.com/sindresorhus/awesome)[0m
[38;5;12mA curated list of awesome computer vision resources, inspired by [39m[38;5;14m[1mawesome-php[0m[38;5;12m (https://github.com/ziadoz/awesome-php).[39m

[38;5;12mFor a list people in computer vision listed with their academic genealogy, please visit [39m[38;5;14m[1mhere[0m[38;5;12m (https://github.com/jbhuang0604/awesome-computer-vision/blob/master/people.md)[39m

[38;2;255;187;0m[4mContributing[0m
[38;5;12mPlease feel free to send me [39m[38;5;14m[1mpull requests[0m[38;5;12m (https://github.com/jbhuang0604/awesome-computer-vision/pulls) or email (jbhuang@vt.edu) to add links.[39m

[38;2;255;187;0m[4mTable of Contents[0m

[38;5;12m - [39m[38;5;14m[1mAwesome Lists[0m[38;5;12m (#awesome-lists)[39m
[38;5;12m - [39m[38;5;14m[1mBooks[0m[38;5;12m (#books)[39m
[38;5;12m - [39m[38;5;14m[1mCourses[0m[38;5;12m (#courses)[39m
[38;5;12m - [39m[38;5;14m[1mPapers[0m[38;5;12m (#papers)[39m
[38;5;12m - [39m[38;5;14m[1mSoftware[0m[38;5;12m (#software)[39m
[38;5;12m - [39m[38;5;14m[1mDatasets[0m[38;5;12m (#datasets)[39m
[38;5;12m - [39m[38;5;14m[1mPre-trained Computer Vision Models[0m[38;5;12m (#Pre-trained-Computer-Vision-Models)[39m
[38;5;12m - [39m[38;5;14m[1mTutorials and Talks[0m[38;5;12m (#tutorials-and-talks)[39m
[38;5;12m - [39m[38;5;14m[1mResources for students[0m[38;5;12m (#resources-for-students)[39m
[38;5;12m - [39m[38;5;14m[1mBlogs[0m[38;5;12m (#blogs)[39m
[38;5;12m - [39m[38;5;14m[1mLinks[0m[38;5;12m (#links)[39m
[38;5;12m - [39m[38;5;14m[1mSongs[0m[38;5;12m (#songs)[39m

[38;2;255;187;0m[4mAwesome Lists[0m
[38;5;12m - [39m[38;5;14m[1mAwesome Machine Learning[0m[38;5;12m (https://github.com/josephmisiti/awesome-machine-learning)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Deep Vision[0m[38;5;12m (https://github.com/kjw0612/awesome-deep-vision)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Domain Adaptation[0m[38;5;12m (https://github.com/zhaoxin94/awesome-domain-adaptation)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Object Detection[0m[38;5;12m (https://github.com/amusi/awesome-object-detection)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome 3D Machine Learning[0m[38;5;12m (https://github.com/timzhang642/3D-Machine-Learning)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Action Recognition[0m[38;5;12m (https://github.com/jinwchoi/awesome-action-recognition)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Scene Understanding[0m[38;5;12m (https://github.com/bertjiazheng/awesome-scene-understanding)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Adversarial Machine Learning[0m[38;5;12m (https://github.com/yenchenlin/awesome-adversarial-machine-learning)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Adversarial Deep Learning[0m[38;5;12m (https://github.com/chbrian/awesome-adversarial-examples-dl)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Face[0m[38;5;12m (https://github.com/polarisZhao/awesome-face)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Face Recognition[0m[38;5;12m (https://github.com/ChanChiChoi/awesome-Face_Recognition)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Human Pose Estimation[0m[38;5;12m (https://github.com/wangzheallen/awesome-human-pose-estimation)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome medical imaging[0m[38;5;12m (https://github.com/fepegar/awesome-medical-imaging)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Images[0m[38;5;12m (https://github.com/heyalexej/awesome-images)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Graphics[0m[38;5;12m (https://github.com/ericjang/awesome-graphics)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Neural Radiance Fields[0m[38;5;12m (https://github.com/yenchenlin/awesome-NeRF)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Implicit Neural Representations[0m[38;5;12m (https://github.com/vsitzmann/awesome-implicit-representations)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Neural Rendering[0m[38;5;12m (https://github.com/weihaox/awesome-neural-rendering)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Public Datasets[0m[38;5;12m (https://github.com/awesomedata/awesome-public-datasets)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Dataset Tools[0m[38;5;12m (https://github.com/jsbroks/awesome-dataset-tools)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Robotics Datasets[0m[38;5;12m (https://github.com/sunglok/awesome-robotics-datasets)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Mobile Machine Learning[0m[38;5;12m (https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Explainable AI[0m[38;5;12m (https://github.com/wangyongjie-ntu/Awesome-explainable-AI)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Fairness in AI[0m[38;5;12m (https://github.com/datamllab/awesome-fairness-in-ai)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Machine Learning Interpretability[0m[38;5;12m (https://github.com/jphall663/awesome-machine-learning-interpretability)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Production Machine Learning[0m[38;5;12m (https://github.com/EthicalML/awesome-production-machine-learning)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Video Text Retrieval[0m[38;5;12m (https://github.com/danieljf24/awesome-video-text-retrieval)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Image-to-Image Translation[0m[38;5;12m (https://github.com/weihaox/awesome-image-translation)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Image Inpainting[0m[38;5;12m (https://github.com/1900zyh/Awesome-Image-Inpainting)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Deep HDR[0m[38;5;12m (https://github.com/vinthony/awesome-deep-hdr)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Video Generation[0m[38;5;12m (https://github.com/matthewvowels1/Awesome-Video-Generation)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome GAN applications[0m[38;5;12m (https://github.com/nashory/gans-awesome-applications)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Generative Modeling[0m[38;5;12m (https://github.com/zhoubolei/awesome-generative-modeling)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Image Classification[0m[38;5;12m (https://github.com/weiaicunzai/awesome-image-classification)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Deep Learning[0m[38;5;12m (https://github.com/ChristosChristofidis/awesome-deep-learning)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Machine Learning in Biomedical(Healthcare) Imaging[0m[38;5;12m (https://github.com/XindiWu/Awesome-Machine-Learning-in-Biomedical-Healthcare-Imaging)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Deep Learning for Tracking and Detection[0m[38;5;12m (https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Human Pose Estimation[0m[38;5;12m (https://github.com/wangzheallen/awesome-human-pose-estimation)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Deep Learning for Video Analysis[0m[38;5;12m (https://github.com/HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Vision + Language[0m[38;5;12m (https://github.com/yuewang-cuhk/awesome-vision-language-pretraining-papers)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Robotics[0m[38;5;12m (https://github.com/kiloreux/awesome-robotics)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Visual Transformer[0m[38;5;12m (https://github.com/dk-liang/Awesome-Visual-Transformer)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Embodied Vision[0m[38;5;12m (https://github.com/ChanganVR/awesome-embodied-vision)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Anomaly Detection[0m[38;5;12m (https://github.com/hoya012/awesome-anomaly-detection)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Makeup Transfer[0m[38;5;12m (https://github.com/thaoshibe/awesome-makeup-transfer)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Learning with Label Noise[0m[38;5;12m (https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Deblurring[0m[38;5;12m (https://github.com/subeeshvasu/Awesome-Deblurring)[39m
[38;5;12m - [39m[38;5;14m[1mAwsome Deep Geometry Learning[0m[38;5;12m (https://github.com/subeeshvasu/Awsome_Deep_Geometry_Learning)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Image Distortion Correction[0m[38;5;12m (https://github.com/subeeshvasu/Awesome-Image-Distortion-Correction)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Neuron Segmentation in EM Images[0m[38;5;12m (https://github.com/subeeshvasu/Awesome-Neuron-Segmentation-in-EM-Images)[39m
[38;5;12m - [39m[38;5;14m[1mAwsome Delineation[0m[38;5;12m (https://github.com/subeeshvasu/Awsome_Delineation)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome ImageHarmonization[0m[38;5;12m (https://github.com/subeeshvasu/Awesome-ImageHarmonization)[39m
[38;5;12m - [39m[38;5;14m[1mAwsome GAN Training[0m[38;5;12m (https://github.com/subeeshvasu/Awsome-GAN-Training)[39m
[38;5;12m - [39m[38;5;14m[1mAwesome Document Understanding[0m[38;5;12m (https://github.com/tstanislawek/awesome-document-understanding)[39m
[38;5;12m [39m

[38;2;255;187;0m[4mBooks[0m

[38;2;255;187;0m[4mComputer Vision[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision:  Models, Learning, and Inference[0m[38;5;12m (http://www.computervisionmodels.com/) - Simon J. D. Prince 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision: Theory and Application[0m[38;5;12m (http://szeliski.org/Book/) - Rick Szeliski 2010[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision: A Modern Approach (2nd edition)[0m[38;5;12m (http://www.amazon.com/Computer-Vision-Modern-Approach-2nd/dp/013608592X/ref=dp_ob_title_bk) - David Forsyth and Jean Ponce 2011[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMultiple View Geometry in Computer Vision[0m[38;5;12m (http://www.robots.ox.ac.uk/~vgg/hzbook/) - Richard Hartley and Andrew Zisserman 2004[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision[0m[38;5;12m (http://www.amazon.com/Computer-Vision-Linda-G-Shapiro/dp/0130307963) - Linda G. Shapiro 2001[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVision Science: Photons to Phenomenology[0m[38;5;12m (http://www.amazon.com/Vision-Science-Phenomenology-Stephen-Palmer/dp/0262161834/) - Stephen E. Palmer 1999[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisual Object Recognition synthesis lecture[0m[38;5;12m (http://www.morganclaypool.com/doi/abs/10.2200/S00332ED1V01Y201103AIM011) - Kristen Grauman and Bastian Leibe 2011[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision for Visual Effects[0m[38;5;12m (http://cvfxbook.com/) - Richard J. Radke, 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHigh dynamic range imaging: acquisition, display, and image-based lighting[0m[38;5;12m (http://www.amazon.com/High-Dynamic-Range-Imaging-Second/dp/012374914X) - Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNumerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics[0m[38;5;12m (https://people.csail.mit.edu/jsolomon/share/book/numerical_book.pdf) - Justin Solomon 2015[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImage Processing and Analysis[0m[38;5;12m (https://www.amazon.com/Processing-Analysis-Activate-Learning-Engineering/dp/1285179528) - Stan Birchfield 2018[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision, From 3D Reconstruction to Recognition[0m[38;5;12m (http://web.stanford.edu/class/cs231a/) - Silvio Savarese 2018[39m

[38;2;255;187;0m[4mOpenCV Programming[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLearning OpenCV: Computer Vision with the OpenCV Library[0m[38;5;12m (http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134) - Gary Bradski and Adrian Kaehler[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPractical Python and OpenCV[0m[38;5;12m (https://www.pyimagesearch.com/practical-python-opencv/) - Adrian Rosebrock[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenCV[0m[38;5;14m[1m [0m[38;5;14m[1mEssentials[0m[38;5;12m [39m[38;5;12m(http://www.amazon.com/OpenCV-Essentials-Oscar-Deniz-Suarez/dp/1783984244/ref=sr_1_1?s=books&ie=UTF8&qid=1424594237&sr=1-1&keywords=opencv+essentials#)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mOscar[39m[38;5;12m [39m[38;5;12mDeniz[39m[38;5;12m [39m[38;5;12mSuarez,[39m[38;5;12m [39m[38;5;12mMª[39m[38;5;12m [39m[38;5;12mdel[39m[38;5;12m [39m[38;5;12mMilagro[39m[38;5;12m [39m[38;5;12mFernandez[39m[38;5;12m [39m[38;5;12mCarrobles,[39m[38;5;12m [39m[38;5;12mNoelia[39m
[38;5;12mVallez[39m[38;5;12m [39m[38;5;12mEnano,[39m[38;5;12m [39m[38;5;12mGloria[39m[38;5;12m [39m[38;5;12mBueno[39m[38;5;12m [39m[38;5;12mGarcia,[39m[38;5;12m [39m[38;5;12mIsmael[39m[38;5;12m [39m[38;5;12mSerrano[39m[38;5;12m [39m[38;5;12mGracia[39m

[38;2;255;187;0m[4mMachine Learning[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPattern Recognition and Machine Learning[0m[38;5;12m (http://research.microsoft.com/en-us/um/people/cmbishop/prml/index.htm) - Christopher M. Bishop 2007[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeural Networks for Pattern Recognition[0m[38;5;12m (http://www.engineering.upm.ro/master-ie/sacpi/mat_did/info068/docum/Neural%20Networks%20for%20Pattern%20Recognition.pdf) - Christopher M. Bishop 1995[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mProbabilistic Graphical Models: Principles and Techniques[0m[38;5;12m (http://pgm.stanford.edu/) - Daphne Koller and Nir Friedman 2009[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPattern Classification[0m[38;5;12m (http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693) - Peter E. Hart, David G. Stork, and Richard O. Duda 2000[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning[0m[38;5;12m (http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077/) - Tom M. Mitchell 1997[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGaussian processes for machine learning[0m[38;5;12m (http://www.gaussianprocess.org/gpml/) - Carl Edward Rasmussen and Christopher K. I. Williams 2005[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLearning From Data[0m[38;5;12m (https://work.caltech.edu/telecourse.html)- Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeural Networks and Deep Learning[0m[38;5;12m (http://neuralnetworksanddeeplearning.com/) - Michael Nielsen 2014[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBayesian Reasoning and Machine Learning[0m[38;5;12m (http://www.cs.ucl.ac.uk/staff/d.barber/brml/) - David Barber, Cambridge University Press, 2012[39m

[38;2;255;187;0m[4mFundamentals[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLinear Algebra and Its Applications[0m[38;5;12m (http://www.amazon.com/Linear-Algebra-Its-Applications-4th/dp/0030105676/ref=sr_1_4?ie=UTF8&qid=1421433773&sr=8-4&keywords=Linear+Algebra+and+Its+Applications) - Gilbert Strang 1995[39m

[38;2;255;187;0m[4mCourses[0m

[38;2;255;187;0m[4mComputer Vision[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEENG 512 / CSCI 512 - Computer Vision[0m[38;5;12m (http://inside.mines.edu/~whoff/courses/EENG512/) - William Hoff (Colorado School of Mines)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisual Object and Activity Recognition[0m[38;5;12m (https://sites.google.com/site/ucbcs29443/) - Alexei A. Efros and Trevor Darrell (UC Berkeley)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision[0m[38;5;12m (http://courses.cs.washington.edu/courses/cse455/12wi/) - Steve Seitz (University of Washington)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mVisual Recognition [39m[38;5;14m[1mSpring 2016[0m[38;5;12m (http://vision.cs.utexas.edu/381V-spring2016/), [39m[38;5;14m[1mFall 2016[0m[38;5;12m (http://vision.cs.utexas.edu/381V-fall2016/) - Kristen Grauman (UT Austin)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLanguage and Vision[0m[38;5;12m (http://www.tamaraberg.com/teaching/Spring_15/) - Tamara Berg (UNC Chapel Hill)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mConvolutional Neural Networks for Visual Recognition[0m[38;5;12m (http://vision.stanford.edu/teaching/cs231n/) - Fei-Fei Li and Andrej Karpathy (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision[0m[38;5;12m (http://cs.nyu.edu/~fergus/teaching/vision/index.html) - Rob Fergus (NYU)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision[0m[38;5;12m (https://courses.engr.illinois.edu/cs543/sp2015/) - Derek Hoiem (UIUC)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision: Foundations and Applications[0m[38;5;12m (http://vision.stanford.edu/teaching/cs131_fall1415/index.html) - Kalanit Grill-Spector and Fei-Fei Li (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHigh-Level Vision: Behaviors, Neurons and Computational Models[0m[38;5;12m (http://vision.stanford.edu/teaching/cs431_spring1314/) - Fei-Fei Li (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAdvances in Computer Vision[0m[38;5;12m (http://6.869.csail.mit.edu/fa15/) - Antonio Torralba and Bill Freeman (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision[0m[38;5;12m (http://www.vision.rwth-aachen.de/course/11/) - Bastian Leibe (RWTH Aachen University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision 2[0m[38;5;12m (http://www.vision.rwth-aachen.de/course/9/) - Bastian Leibe (RWTH Aachen University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision[0m[38;5;12m (http://klewel.com/conferences/epfl-computer-vision/) Pascal Fua (EPFL):[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision 1[0m[38;5;12m (http://cvlab-dresden.de/courses/computer-vision-1/) Carsten Rother (TU Dresden):[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision 2[0m[38;5;12m (http://cvlab-dresden.de/courses/CV2/) Carsten Rother (TU Dresden):[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMultiple View Geometry[0m[38;5;12m (https://youtu.be/RDkwklFGMfo?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) Daniel Cremers (TU Munich):[39m




[38;2;255;187;0m[4mComputational Photography[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImage Manipulation and Computational Photography[0m[38;5;12m (http://inst.eecs.berkeley.edu/~cs194-26/fa14/) - Alexei A. Efros (UC Berkeley)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Photography[0m[38;5;12m (http://graphics.cs.cmu.edu/courses/15-463/2012_fall/463.html) - Alexei A. Efros (CMU)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Photography[0m[38;5;12m (https://courses.engr.illinois.edu/cs498dh3/) - Derek Hoiem (UIUC)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Photography[0m[38;5;12m (http://cs.brown.edu/courses/csci1290/) - James Hays (Brown University)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDigital & Computational Photography[0m[38;5;12m (http://stellar.mit.edu/S/course/6/sp12/6.815/) - Fredo Durand (MIT)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Camera and Photography[0m[38;5;12m (http://ocw.mit.edu/courses/media-arts-and-sciences/mas-531-computational-camera-and-photography-fall-2009/) - Ramesh Raskar (MIT Media Lab)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Photography[0m[38;5;12m (https://www.udacity.com/course/computational-photography--ud955) - Irfan Essa (Georgia Tech)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCourses in Graphics[0m[38;5;12m (http://graphics.stanford.edu/courses/) - Stanford University[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Photography[0m[38;5;12m (http://cs.nyu.edu/~fergus/teaching/comp_photo/index.html) - Rob Fergus (NYU)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntroduction to Visual Computing[0m[38;5;12m (http://www.cs.toronto.edu/~kyros/courses/320/) - Kyros Kutulakos (University of Toronto)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Photography[0m[38;5;12m (http://www.cs.toronto.edu/~kyros/courses/2530/) - Kyros Kutulakos (University of Toronto)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision for Visual Effects[0m[38;5;12m (https://www.ecse.rpi.edu/~rjradke/cvfxcourse.html) - Rich Radke (Rensselaer Polytechnic Institute)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntroduction to Image Processing[0m[38;5;12m (https://www.ecse.rpi.edu/~rjradke/improccourse.html) - Rich Radke (Rensselaer Polytechnic Institute)[39m

[38;2;255;187;0m[4mMachine Learning and Statistical Learning[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning[0m[38;5;12m (https://www.coursera.org/learn/machine-learning) - Andrew Ng (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLearning from Data[0m[38;5;12m (https://work.caltech.edu/telecourse.html) - Yaser S. Abu-Mostafa (Caltech)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStatistical Learning[0m[38;5;12m (https://class.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about) - Trevor Hastie and Rob Tibshirani (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStatistical Learning Theory and Applications[0m[38;5;12m (http://www.mit.edu/~9.520/fall14/) - Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStatistical Learning[0m[38;5;12m (http://www.stat.rice.edu/~gallen/stat640.html) - Genevera Allen (Rice University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPractical Machine Learning[0m[38;5;12m (http://www.cs.berkeley.edu/~jordan/courses/294-fall09/) - Michael Jordan (UC Berkeley)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCourse on Information Theory, Pattern Recognition, and Neural Networks[0m[38;5;12m (http://videolectures.net/course_information_theory_pattern_recognition/) - David MacKay (University of Cambridge)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMethods for Applied Statistics: Unsupervised Learning[0m[38;5;12m (http://web.stanford.edu/~lmackey/stats306b/) - Lester Mackey (Stanford)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning[0m[38;5;12m (http://www.robots.ox.ac.uk/~az/lectures/ml/index.html) - Andrew Zisserman (University of Oxford)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntro to Machine Learning[0m[38;5;12m (https://www.udacity.com/course/intro-to-machine-learning--ud120) - Sebastian Thrun (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning[0m[38;5;12m (https://www.udacity.com/course/machine-learning--ud262) - Charles Isbell, Michael Littman (Georgia Tech)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1m(Convolutional) Neural Networks for Visual Recognition[0m[38;5;12m (https://cs231n.github.io/) - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning for Computer Vision[0m[38;5;12m (https://youtu.be/QZmZFeZxEKI?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) - Rudolph Triebel (TU Munich)[39m



[38;2;255;187;0m[4mOptimization[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mConvex Optimization I[0m[38;5;12m (http://stanford.edu/class/ee364a/) - Stephen Boyd (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mConvex Optimization II[0m[38;5;12m (http://stanford.edu/class/ee364b/) - Stephen Boyd (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mConvex Optimization[0m[38;5;12m (https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about) - Stephen Boyd (Stanford University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOptimization at MIT[0m[38;5;12m (http://optimization.mit.edu/classes.php) - (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mConvex Optimization[0m[38;5;12m (http://www.stat.cmu.edu/~ryantibs/convexopt/) - Ryan Tibshirani (CMU)[39m

[38;2;255;187;0m[4mPapers[0m

[38;2;255;187;0m[4mConference papers on the web[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCVPapers[0m[38;5;12m (http://www.cvpapers.com/) - Computer vision papers on the web[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSIGGRAPH Paper on the web[0m[38;5;12m (http://kesen.realtimerendering.com/) - Graphics papers on the web[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNIPS Proceedings[0m[38;5;12m (http://papers.nips.cc/) - NIPS papers on the web[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision Foundation open access[0m[38;5;12m (http://www.cv-foundation.org/openaccess/menu.py)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAnnotated Computer Vision Bibliography[0m[38;5;12m (http://iris.usc.edu/Vision-Notes/bibliography/contents.html) - Keith Price (USC)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCalendar of Computer Image Analysis, Computer Vision Conferences[0m[38;5;12m (http://iris.usc.edu/Information/Iris-Conferences.html) - (USC)[39m

[38;2;255;187;0m[4mSurvey Papers[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisionbib Survey Paper List[0m[38;5;12m (http://surveys.visionbib.com/index.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFoundations and Trends® in Computer Graphics and Vision[0m[38;5;12m (http://www.nowpublishers.com/CGV)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision: A Reference Guide[0m[38;5;12m (http://link.springer.com/book/10.1007/978-0-387-31439-6)[39m

[38;5;12m ## Pre-trained Computer Vision Models[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mList of Computer Vision models[0m[38;5;12m (https://github.com/shubham-shahh/Open-Source-Models) These models are trained on custom objects[39m

[38;2;255;187;0m[4mTutorials and talks[0m

[38;2;255;187;0m[4mComputer Vision[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision Talks[0m[38;5;12m (http://www.computervisiontalks.com/) - Lectures, keynotes, panel discussions on computer vision[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Three R's of Computer Vision[0m[38;5;12m (https://www.youtube.com/watch?v=Mqg6eorYRIQ) - Jitendra Malik (UC Berkeley) 2013[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mApplications to Machine Vision[0m[38;5;12m (http://videolectures.net/epsrcws08_blake_amv/) - Andrew Blake (Microsoft Research) 2008[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Future of Image Search[0m[38;5;12m (http://videolectures.net/kdd08_malik_fis/?q=image) - Jitendra Malik (UC Berkeley) 2008[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mShould I do a PhD in Computer Vision?[0m[38;5;12m (https://www.youtube.com/watch?v=M17oGxh3Ny8) - Fatih Porikli (Australian National University)[39m
[38;5;12m - [39m[38;5;14m[1mGraduate Summer School 2013: Computer Vision[0m[38;5;12m (http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/?tab=schedule) - IPAM, 2013[39m

[38;2;255;187;0m[4mRecent Conference Talks[0m
[38;5;12m- [39m[38;5;14m[1mCVPR 2015[0m[38;5;12m (http://www.pamitc.org/cvpr15/) - Jun 2015[39m
[38;5;12m- [39m[38;5;14m[1mECCV 2014[0m[38;5;12m (http://videolectures.net/eccv2014_zurich/) - Sep 2014[39m
[38;5;12m- [39m[38;5;14m[1mCVPR 2014[0m[38;5;12m (http://techtalks.tv/cvpr-2014-oral-talks/) - Jun 2014[39m
[38;5;12m- [39m[38;5;14m[1mICCV 2013[0m[38;5;12m (http://techtalks.tv/iccv2013/) - Dec 2013[39m
[38;5;12m- [39m[38;5;14m[1mICML 2013[0m[38;5;12m (http://techtalks.tv/icml/2013/) - Jul 2013[39m
[38;5;12m- [39m[38;5;14m[1mCVPR 2013[0m[38;5;12m (http://techtalks.tv/cvpr2013/) - Jun 2013[39m
[38;5;12m- [39m[38;5;14m[1mECCV 2012[0m[38;5;12m (http://videolectures.net/eccv2012_firenze/) - Oct 2012[39m
[38;5;12m- [39m[38;5;14m[1mICML 2012[0m[38;5;12m (http://techtalks.tv/icml/2012/orals/) - Jun 2012[39m
[38;5;12m- [39m[38;5;14m[1mCVPR 2012[0m[38;5;12m (http://techtalks.tv/cvpr2012webcast/) - Jun 2012[39m

[38;2;255;187;0m[4m3D Computer Vision[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1m3D Computer Vision: Past, Present, and Future[0m[38;5;12m (https://www.youtube.com/watch?v=kyIzMr917Rc) - Steve Seitz (University of Washington) 2011[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mReconstructing the World from Photos on the Internet[0m[38;5;12m (https://www.youtube.com/watch?v=04Kgg3QEXFI) - Steve Seitz (University of Washington) 2013[39m

[38;2;255;187;0m[4mInternet Vision[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Distributed Camera[0m[38;5;12m (http://www.technologyreview.com/video/426265/meet-2011-tr35-winner-noah-snavely/) - Noah Snavely (Cornell University) 2011[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPlanet-Scale Visual Understanding[0m[38;5;12m (https://www.youtube.com/watch?v=UHkCa9-Z1Ps) - Noah Snavely (Cornell University) 2014[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mA Trillion Photos[0m[38;5;12m (https://www.youtube.com/watch?v=6MWEfpKUfRc) - Steve Seitz (University of Washington) 2013[39m

[38;2;255;187;0m[4mComputational Photography[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mReflections on Image-Based Modeling and Rendering[0m[38;5;12m (https://www.youtube.com/watch?v=j90_0Ndk7XM) - Richard Szeliski (Microsoft Research) 2013[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPhotographing Events over Time[0m[38;5;12m (https://www.youtube.com/watch?v=ZvPaHZZVPRk) - William T. Freeman (MIT) 2011[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOld and New algorithm for Blind Deconvolution[0m[38;5;12m (http://videolectures.net/nipsworkshops2011_weiss_deconvolution/) -  Yair Weiss (The Hebrew University of Jerusalem) 2011[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mA Tour of Modern "Image Processing"[0m[38;5;12m (http://videolectures.net/nipsworkshops2010_milanfar_tmi/) -  Peyman Milanfar (UC Santa Cruz/Google) 2010[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTopics in image and video processing[0m[38;5;12m (http://videolectures.net/mlss07_blake_tiivp/) Andrew Blake (Microsoft Research) 2007[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputational Photography[0m[38;5;12m (https://www.youtube.com/watch?v=HJVNI0mkmqk) - William T. Freeman (MIT) 2012[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRevealing the Invisible[0m[38;5;12m (https://www.youtube.com/watch?v=_BWnIQY_X98) - Frédo Durand (MIT) 2012[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOverview of Computer Vision and Visual Effects[0m[38;5;12m (https://www.youtube.com/watch?v=rE-hVtytT-I) - Rich Radke (Rensselaer Polytechnic Institute) 2014[39m

[38;2;255;187;0m[4mLearning and Vision[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWhere machine vision needs help from machine learning[0m[38;5;12m (http://videolectures.net/colt2011_freeman_help/?q=computer%20vision) - William T. Freeman (MIT) 2011[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLearning in Computer Vision[0m[38;5;12m (http://videolectures.net/mlss08au_lucey_linv/) - Simon Lucey (CMU) 2008[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLearning and Inference in Low-Level Vision[0m[38;5;12m (http://videolectures.net/nips09_weiss_lil/?q=computer%20vision) - Yair Weiss (The Hebrew University of Jerusalem) 2009[39m

[38;2;255;187;0m[4mObject Recognition[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mObject Recognition[0m[38;5;12m (http://research.microsoft.com/apps/video/dl.aspx?id=231358) - Larry Zitnick (Microsoft Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGenerative Models for Visual Objects and Object Recognition via Bayesian Inference[0m[38;5;12m (http://videolectures.net/mlas06_li_gmvoo/?q=Fei-Fei%20Li) - Fei-Fei Li (Stanford University)[39m

[38;2;255;187;0m[4mGraphical Models[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraphical Models for Computer Vision[0m[38;5;12m (http://videolectures.net/uai2012_felzenszwalb_computer_vision/?q=computer%20vision) - Pedro Felzenszwalb (Brown University) 2012[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraphical Models[0m[38;5;12m (http://videolectures.net/mlss09uk_ghahramani_gm/) - Zoubin Ghahramani (University of Cambridge) 2009[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning, Probability and Graphical Models[0m[38;5;12m (http://videolectures.net/mlss06tw_roweis_mlpgm/) - Sam Roweis (NYU) 2006[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraphical Models and Applications[0m[38;5;12m (http://videolectures.net/mlss09us_weiss_gma/?q=Graphical%20Models) -  Yair Weiss (The Hebrew University of Jerusalem) 2009[39m

[38;2;255;187;0m[4mMachine Learning[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mA Gentle Tutorial of the EM Algorithm[0m[38;5;12m (https://nikola-rt.ee.washington.edu/people/bulyko/papers/em.pdf) - Jeff A. Bilmes (UC Berkeley) 1998[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntroduction To Bayesian Inference[0m[38;5;12m (http://videolectures.net/mlss09uk_bishop_ibi/) - Christopher Bishop (Microsoft Research) 2009[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSupport Vector Machines[0m[38;5;12m (http://videolectures.net/mlss06tw_lin_svm/) - Chih-Jen Lin (National Taiwan University) 2006[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBayesian or Frequentist, Which Are You? [0m[38;5;12m (http://videolectures.net/mlss09uk_jordan_bfway/) - Michael I. Jordan (UC Berkeley)[39m

[38;2;255;187;0m[4mOptimization[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOptimization Algorithms in Machine Learning[0m[38;5;12m (http://videolectures.net/nips2010_wright_oaml/) - Stephen J. Wright (University of Wisconsin-Madison)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mConvex Optimization[0m[38;5;12m (http://videolectures.net/mlss07_vandenberghe_copt/?q=convex%20optimization) - Lieven Vandenberghe (University of California, Los Angeles)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mContinuous Optimization in Computer Vision[0m[38;5;12m (https://www.youtube.com/watch?v=oZqoWozVDVg) - Andrew Fitzgibbon (Microsoft Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBeyond stochastic gradient descent for large-scale machine learning[0m[38;5;12m (http://videolectures.net/sahd2014_bach_stochastic_gradient/) - Francis Bach (INRIA)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVariational[0m[38;5;14m[1m [0m[38;5;14m[1mMethods[0m[38;5;14m[1m [0m[38;5;14m[1mfor[0m[38;5;14m[1m [0m[38;5;14m[1mComputer[0m[38;5;14m[1m [0m[38;5;14m[1mVision[0m[38;5;12m [39m[38;5;12m(https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mDaniel[39m[38;5;12m [39m[38;5;12mCremers[39m[38;5;12m [39m[38;5;12m(Technische[39m[38;5;12m [39m[38;5;12mUniversität[39m[38;5;12m [39m[38;5;12mMünchen)[39m[38;5;12m [39m[38;5;12m([39m[38;5;14m[1mlecture[0m[38;5;14m[1m [0m[38;5;14m[1m18[0m[38;5;14m[1m [0m[38;5;14m[1mmissing[0m[38;5;14m[1m [0m[38;5;14m[1mfrom[0m[38;5;14m[1m [0m[38;5;14m[1mplaylist[0m[38;5;12m [39m
[38;5;12m(https://www.youtube.com/watch?v=GgcbVPNd3SI))[39m

[38;2;255;187;0m[4mDeep Learning[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mA tutorial on Deep Learning[0m[38;5;12m (http://videolectures.net/jul09_hinton_deeplearn/) - Geoffrey E. Hinton (University of Toronto)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeep Learning[0m[38;5;12m (http://videolectures.net/kdd2014_salakhutdinov_deep_learning/?q=Hidden%20Markov%20model#) -  Ruslan Salakhutdinov (University of Toronto)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mScaling up Deep Learning[0m[38;5;12m (http://videolectures.net/kdd2014_bengio_deep_learning/) - Yoshua Bengio (University of Montreal)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImageNet Classification with Deep Convolutional Neural Networks[0m[38;5;12m (http://videolectures.net/machine_krizhevsky_imagenet_classification/?q=deep%20learning) -  Alex Krizhevsky (University of Toronto)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Unreasonable Effectivness Of Deep Learning[0m[38;5;12m (http://videolectures.net/sahd2014_lecun_deep_learning/) Yann LeCun (NYU/Facebook Research) 2014[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeep Learning for Computer Vision[0m[38;5;12m (https://www.youtube.com/watch?v=qgx57X0fBdA) - Rob Fergus (NYU/Facebook Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHigh-dimensional learning with deep network contractions[0m[38;5;12m (http://videolectures.net/sahd2014_mallat_dimensional_learning/) - Stéphane Mallat (Ecole Normale Superieure)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraduate Summer School 2012: Deep Learning, Feature Learning[0m[38;5;12m (http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule) - IPAM, 2012[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWorkshop on Big Data and Statistical Machine Learning[0m[38;5;12m (http://www.fields.utoronto.ca/programs/scientific/14-15/bigdata/machine/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning Summer School[0m[38;5;12m (https://www.youtube.com/channel/UC3ywjSv5OsDiDAnOP8C1NiQ) - Reykjavik, Iceland 2014[39m
[48;5;235m[38;5;249m* **Deep Learning Session 1** (https://www.youtube.com/watch?v=JuimBuvEWBg) - Yoshua Bengio (Universtiy of Montreal)[49m[39m
[48;5;235m[38;5;249m* **Deep Learning Session 2** (https://www.youtube.com/watch?v=Fl-W7_z3w3o) - Yoshua Bengio (University of Montreal)[49m[39m
[48;5;235m[38;5;249m* **Deep Learning Session 3** (https://www.youtube.com/watch?v=_cohR7LAgWA) - Yoshua Bengio (University of Montreal)[49m[39m

[38;2;255;187;0m[4mSoftware[0m

[38;2;255;187;0m[4mAnnotation tools[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComma Coloring[0m[38;5;12m (http://commacoloring.herokuapp.com/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAnnotorious[0m[38;5;12m (https://annotorious.github.io/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLabelME[0m[38;5;12m (http://labelme.csail.mit.edu/Release3.0/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mgtmaker[0m[38;5;12m (https://github.com/sanko-shoko/gtmaker)[39m

[38;2;255;187;0m[4mExternal Resource Links[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision Resources[0m[38;5;12m (https://sites.google.com/site/jbhuang0604/resources/vision) - Jia-Bin Huang (UIUC)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision Algorithm Implementations[0m[38;5;12m (http://www.cvpapers.com/rr.html) - CVPapers[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSource Code Collection for Reproducible Research[0m[38;5;12m (http://www.csee.wvu.edu/~xinl/reproducible_research.html) - Xin Li (West Virginia University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCMU Computer Vision Page[0m[38;5;12m (http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/v-source.html)[39m

[38;2;255;187;0m[4mGeneral Purpose Computer Vision Library[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpen CV[0m[38;5;12m (http://opencv.org/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mmexopencv[0m[38;5;12m (http://kyamagu.github.io/mexopencv/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSimpleCV[0m[38;5;12m (http://simplecv.org/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpen source Python module for computer vision[0m[38;5;12m (https://github.com/jesolem/PCV)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mccv: A Modern Computer Vision Library[0m[38;5;12m (https://github.com/liuliu/ccv)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVLFeat[0m[38;5;12m (http://www.vlfeat.org/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMatlab Computer Vision System Toolbox[0m[38;5;12m (http://www.mathworks.com/products/computer-vision/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPiotr's Computer Vision Matlab Toolbox[0m[38;5;12m (http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPCL: Point Cloud Library[0m[38;5;12m (http://pointclouds.org/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImageUtilities[0m[38;5;12m (https://gitorious.org/imageutilities)[39m

[38;2;255;187;0m[4mMultiple-view Computer Vision[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMATLAB Functions for Multiple View Geometry[0m[38;5;12m (http://www.robots.ox.ac.uk/~vgg/hzbook/code/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPeter Kovesi's Matlab Functions for Computer Vision and Image Analysis[0m[38;5;12m (http://staffhome.ecm.uwa.edu.au/~00011811/Research/MatlabFns/index.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenGV [0m[38;5;12m (http://laurentkneip.github.io/opengv/) - geometric computer vision algorithms[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMinimalSolvers[0m[38;5;12m (http://cmp.felk.cvut.cz/mini/) - Minimal problems solver[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMulti-View Environment[0m[38;5;12m (http://www.gcc.tu-darmstadt.de/home/proj/mve/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisual SFM[0m[38;5;12m (http://ccwu.me/vsfm/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBundler SFM[0m[38;5;12m (http://www.cs.cornell.edu/~snavely/bundler/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mopenMVG: open Multiple View Geometry[0m[38;5;12m (http://imagine.enpc.fr/~moulonp/openMVG/) - Multiple View Geometry; Structure from Motion library & softwares[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPatch-based Multi-view Stereo V2[0m[38;5;12m (http://www.di.ens.fr/pmvs/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mClustering Views for Multi-view Stereo[0m[38;5;12m (http://www.di.ens.fr/cmvs/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFloating Scale Surface Reconstruction[0m[38;5;12m (http://www.gris.informatik.tu-darmstadt.de/projects/floating-scale-surface-recon/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLarge-Scale Texturing of 3D Reconstructions[0m[38;5;12m (http://www.gcc.tu-darmstadt.de/home/proj/texrecon/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome 3D reconstruction list[0m[38;5;12m (https://github.com/openMVG/awesome_3DReconstruction_list)[39m


[38;2;255;187;0m[4mFeature Detection and Extraction[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVLFeat[0m[38;5;12m (http://www.vlfeat.org/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSIFT[0m[38;5;12m (http://www.cs.ubc.ca/~lowe/keypoints/)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mDavid G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSIFT++[0m[38;5;12m (http://www.robots.ox.ac.uk/~vedaldi/code/siftpp.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBRISK[0m[38;5;12m (http://www.asl.ethz.ch/people/lestefan/personal/BRISK)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mStefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSURF[0m[38;5;12m (http://www.vision.ee.ethz.ch/~surf/)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mHerbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFREAK[0m[38;5;12m (http://www.ivpe.com/freak.htm)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mA. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAKAZE[0m[38;5;12m (http://www.robesafe.com/personal/pablo.alcantarilla/kaze.html)[39m
[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;12mPablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLocal Binary Patterns[0m[38;5;12m (https://github.com/nourani/LBP)[39m

[38;2;255;187;0m[4mHigh Dynamic Range Imaging[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHDR_Toolbox[0m[38;5;12m (https://github.com/banterle/HDR_Toolbox)[39m

[38;2;255;187;0m[4mSemantic Segmentation[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mList of Semantic Segmentation algorithms[0m[38;5;12m (http://www.it-caesar.com/list-of-contemporary-semantic-segmentation-datasets/)[39m

[38;2;255;187;0m[4mLow-level Vision[0m

[38;2;255;187;0m[4mStereo Vision[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMiddlebury Stereo Vision[0m[38;5;12m (http://vision.middlebury.edu/stereo/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe KITTI Vision Benchmark Suite[0m[38;5;12m (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stero)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLIBELAS: Library for Efficient Large-scale Stereo Matching[0m[38;5;12m (http://www.cvlibs.net/software/libelas/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGround Truth Stixel Dataset[0m[38;5;12m (http://www.6d-vision.com/ground-truth-stixel-dataset)[39m

[38;2;255;187;0m[4mOptical Flow[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMiddlebury Optical Flow Evaluation[0m[38;5;12m (http://vision.middlebury.edu/flow/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMPI-Sintel Optical Flow Dataset and Evaluation[0m[38;5;12m (http://sintel.is.tue.mpg.de/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe KITTI Vision Benchmark Suite[0m[38;5;12m (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHCI Challenge[0m[38;5;12m (http://hci.iwr.uni-heidelberg.de/Benchmarks/document/Challenging_Data_for_Stereo_and_Optical_Flow/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCoarse2Fine Optical Flow[0m[38;5;12m (http://people.csail.mit.edu/celiu/OpticalFlow/) - Ce Liu (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSecrets of Optical Flow Estimation and Their Principles[0m[38;5;12m (http://cs.brown.edu/~dqsun/code/cvpr10_flow_code.zip)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mC++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)[0m[38;5;12m (http://people.csail.mit.edu/celiu/OpticalFlow/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mParallel Robust Optical Flow by Sánchez Pérez et al.[0m[38;5;12m (http://www.ctim.es/research_works/parallel_robust_optical_flow/)[39m

[38;2;255;187;0m[4mImage Denoising[0m
[38;5;12mBM3D, KSVD,[39m

[38;2;255;187;0m[4mSuper-resolution[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMulti-frame image super-resolution[0m[38;5;12m (http://www.robots.ox.ac.uk/~vgg/software/SR/)[39m
[48;5;235m[38;5;249m* Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 2008[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMarkov Random Fields for Super-Resolution[0m[38;5;12m (http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html)[39m
[48;5;235m[38;5;249m* W. T Freeman and C. Liu. Markov Random Fields for Super-resolution and Texture Synthesis. In A. Blake, P. Kohli, and C. Rother, eds., Advances in Markov Random Fields for Vision and Image Processing, Chapter 10. MIT Press, 2011[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSparse regression and natural image prior[0m[38;5;12m (https://people.mpi-inf.mpg.de/~kkim/supres/supres.htm)[39m
[48;5;235m[38;5;249m* K. I. Kim and Y. Kwon, "Single-image super-resolution using sparse regression and natural image prior", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010.[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSingle-Image Super Resolution via a Statistical Model[0m[38;5;12m (http://www.cs.technion.ac.il/~elad/Various/SingleImageSR_TIP14_Box.zip)[39m
[48;5;235m[38;5;249m* T. Peleg and M. Elad, A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution, IEEE Transactions on Image Processing, Vol. 23, No. 6, Pages 2569-2582, June 2014[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSparse Coding for Super-Resolution[0m[38;5;12m (http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip)[39m
[48;5;235m[38;5;249m* R. Zeyde, M. Elad, and M. Protter On Single Image Scale-Up using Sparse-Representations, Curves & Surfaces, Avignon-France, June 24-30, 2010 (appears also in Lecture-Notes-on-Computer-Science - LNCS).[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPatch-wise Sparse Recovery[0m[38;5;12m (http://www.ifp.illinois.edu/~jyang29/ScSR.htm)[39m
[48;5;235m[38;5;249m* Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image super-resolution via sparse representation. IEEE Transactions on Image Processing (TIP), vol. 19, issue 11, 2010.[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeighbor embedding[0m[38;5;12m (http://www.jdl.ac.cn/user/hchang/doc/code.rar)[39m
[48;5;235m[38;5;249m* H. Chang, D.Y. Yeung, Y. Xiong. Super-resolution through neighbor embedding. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol.1, pp.275-282, Washington, DC, USA, 27 June - 2 Ju[49m[39m[48;5;235m[38;5;249m        [49m[39m
[48;5;235m[38;5;249mly 2004.[49m[39m[48;5;235m[38;5;249m                                                                                                                                                                                                                                            [49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeformable Patches[0m[38;5;12m (https://sites.google.com/site/yuzhushome/single-image-super-resolution-using-deformable-patches)[39m
[48;5;235m[38;5;249m*  Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSRCNN[0m[38;5;12m (http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)[39m
[48;5;235m[38;5;249m* Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mA+: Adjusted Anchored Neighborhood Regression[0m[38;5;12m (http://www.vision.ee.ethz.ch/~timofter/ACCV2014_ID820_SUPPLEMENTARY/index.html)[39m
[48;5;235m[38;5;249m* R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014[49m[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTransformed Self-Exemplars[0m[38;5;12m (https://sites.google.com/site/jbhuang0604/publications/struct_sr)[39m
[48;5;235m[38;5;249m* Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, Single Image Super-Resolution using Transformed Self-Exemplars, IEEE Conference on Computer Vision and Pattern Recognition, 2015[49m[39m

[38;2;255;187;0m[4mImage Deblurring[0m

[38;5;12mNon-blind deconvolution[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpatially variant non-blind deconvolution[0m[38;5;12m (http://homes.cs.washington.edu/~shanqi/work/spvdeconv/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHandling Outliers in Non-blind Image Deconvolution[0m[38;5;12m (http://cg.postech.ac.kr/research/deconv_outliers/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHyper-Laplacian Priors[0m[38;5;12m (http://cs.nyu.edu/~dilip/research/fast-deconvolution/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFrom Learning Models of Natural Image Patches to Whole Image Restoration[0m[38;5;12m (http://people.csail.mit.edu/danielzoran/epllcode.zip)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDeep Convolutional Neural Network for Image Deconvolution[0m[38;5;12m (http://lxu.me/projects/dcnn/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeural Deconvolution[0m[38;5;12m (http://webdav.is.mpg.de/pixel/neural_deconvolution/)[39m

[38;5;12mBlind deconvolution[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRemoving Camera Shake From A Single Photograph[0m[38;5;12m (http://www.cs.nyu.edu/~fergus/research/deblur.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHigh-quality motion deblurring from a single image[0m[38;5;12m (http://www.cse.cuhk.edu.hk/leojia/projects/motion_deblurring/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTwo-Phase Kernel Estimation for Robust Motion Deblurring[0m[38;5;12m (http://www.cse.cuhk.edu.hk/leojia/projects/robust_deblur/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBlur kernel estimation using the radon transform[0m[38;5;12m (http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFast motion deblurring[0m[38;5;12m (http://cg.postech.ac.kr/research/fast_motion_deblurring/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBlind Deconvolution Using a Normalized Sparsity Measure[0m[38;5;12m (http://cs.nyu.edu//~dilip/research/blind-deconvolution/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBlur-kernel estimation from spectral irregularities[0m[38;5;12m (http://www.cs.huji.ac.il/~raananf/projects/deblur/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEfficient marginal likelihood optimization in blind deconvolution[0m[38;5;12m (http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUnnatural L0 Sparse Representation for Natural Image Deblurring[0m[38;5;12m (http://www.cse.cuhk.edu.hk/leojia/projects/l0deblur/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEdge-based Blur Kernel Estimation Using Patch Priors[0m[38;5;12m (http://cs.brown.edu/~lbsun/deblur2013/deblur2013iccp.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBlind Deblurring Using Internal Patch Recurrence[0m[38;5;12m (http://www.wisdom.weizmann.ac.il/~vision/BlindDeblur.html)[39m

[38;5;12mNon-uniform Deblurring[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNon-uniform Deblurring for Shaken Images[0m[38;5;12m (http://www.di.ens.fr/willow/research/deblurring/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSingle Image Deblurring Using Motion Density Functions[0m[38;5;12m (http://grail.cs.washington.edu/projects/mdf_deblurring/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImage Deblurring using Inertial Measurement Sensors[0m[38;5;12m (http://research.microsoft.com/en-us/um/redmond/groups/ivm/imudeblurring/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFast Removal of Non-uniform Camera Shake[0m[38;5;12m (http://webdav.is.mpg.de/pixel/fast_removal_of_camera_shake/)[39m


[38;2;255;187;0m[4mImage Completion[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGIMP Resynthesizer[0m[38;5;12m (http://registry.gimp.org/node/27986)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPriority BP[0m[38;5;12m (http://lafarren.com/image-completer/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImageMelding[0m[38;5;12m (http://www.ece.ucsb.edu/~psen/melding)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPlanarStructureCompletion[0m[38;5;12m (https://sites.google.com/site/jbhuang0604/publications/struct_completion)[39m

[38;2;255;187;0m[4mImage Retargeting[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRetargetMe[0m[38;5;12m (http://people.csail.mit.edu/mrub/retargetme/)[39m

[38;2;255;187;0m[4mAlpha Matting[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAlpha Matting Evaluation[0m[38;5;12m (http://www.alphamatting.com/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mClosed-form image matting[0m[38;5;12m (http://people.csail.mit.edu/alevin/matting.tar.gz)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpectral Matting[0m[38;5;12m (http://www.vision.huji.ac.il/SpectralMatting/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLearning-based Matting[0m[38;5;12m (http://www.mathworks.com/matlabcentral/fileexchange/31412-learning-based-digital-matting)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImproving Image Matting using Comprehensive Sampling Sets[0m[38;5;12m (http://www.alphamatting.com/ImprovingMattingComprehensiveSamplingSets_CVPR2013.zip)[39m

[38;2;255;187;0m[4mImage Pyramid[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Steerable Pyramid[0m[38;5;12m (http://www.cns.nyu.edu/~eero/steerpyr/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCurveLab[0m[38;5;12m (http://www.curvelet.org/)[39m

[38;2;255;187;0m[4mEdge-preserving image processing[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFast Bilateral Filter[0m[38;5;12m (http://people.csail.mit.edu/sparis/bf/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mO(1) Bilateral Filter[0m[38;5;12m (http://www.cs.cityu.edu.hk/~qiyang/publications/code/qx.cvpr09.ctbf.zip)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRecursive Bilateral Filtering[0m[38;5;12m (http://www.cs.cityu.edu.hk/~qiyang/publications/eccv-12/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRolling Guidance Filter[0m[38;5;12m (http://www.cse.cuhk.edu.hk/leojia/projects/rollguidance/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRelative Total Variation[0m[38;5;12m (http://www.cse.cuhk.edu.hk/leojia/projects/texturesep/index.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mL0 Gradient Optimization[0m[38;5;12m (http://www.cse.cuhk.edu.hk/leojia/projects/L0smoothing/index.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDomain Transform[0m[38;5;12m (http://www.inf.ufrgs.br/~eslgastal/DomainTransform/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAdaptive Manifold[0m[38;5;12m (http://inf.ufrgs.br/~eslgastal/AdaptiveManifolds/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGuided image filtering[0m[38;5;12m (http://research.microsoft.com/en-us/um/people/kahe/eccv10/)[39m

[38;2;255;187;0m[4mIntrinsic Images[0m

[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRecovering Intrinsic Images with a global Sparsity Prior on Reflectance[0m[38;5;12m (http://people.tuebingen.mpg.de/mkiefel/projects/intrinsic/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntrinsic Images by Clustering[0m[38;5;12m (http://giga.cps.unizar.es/~elenag/projects/EGSR2012_intrinsic/)[39m

[38;2;255;187;0m[4mContour Detection and Image Segmentation[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMean Shift Segmentation[0m[38;5;12m (http://coewww.rutgers.edu/riul/research/code/EDISON/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraph-based Segmentation[0m[38;5;12m (http://cs.brown.edu/~pff/segment/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNormalized Cut[0m[38;5;12m (http://www.cis.upenn.edu/~jshi/software/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGrab Cut[0m[38;5;12m (http://grabcut.weebly.com/background--algorithm.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mContour Detection and Image Segmentation[0m[38;5;12m (http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStructured Edge Detection[0m[38;5;12m (http://research.microsoft.com/en-us/downloads/389109f6-b4e8-404c-84bf-239f7cbf4e3d/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPointwise Mutual Information[0m[38;5;12m (http://web.mit.edu/phillipi/pmi-boundaries/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSLIC Super-pixel[0m[38;5;12m (http://ivrl.epfl.ch/research/superpixels)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mQuickShift[0m[38;5;12m (http://www.vlfeat.org/overview/quickshift.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTurboPixels[0m[38;5;12m (http://www.cs.toronto.edu/~babalex/research.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEntropy Rate Superpixel[0m[38;5;12m (http://mingyuliu.net/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mContour Relaxed Superpixels[0m[38;5;12m (http://www.vsi.cs.uni-frankfurt.de/research/current-projects/research/superpixel-segmentation/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSEEDS[0m[38;5;12m (http://www.mvdblive.org/seeds/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSEEDS Revised[0m[38;5;12m (https://github.com/davidstutz/seeds-revised)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMultiscale Combinatorial Grouping[0m[38;5;12m (http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFast Edge Detection Using Structured Forests[0m[38;5;12m (https://github.com/pdollar/edges)[39m

[38;2;255;187;0m[4mInteractive Image Segmentation[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRandom Walker[0m[38;5;12m (http://cns.bu.edu/~lgrady/software.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGeodesic Segmentation[0m[38;5;12m (http://www.tc.umn.edu/~baixx015/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLazy Snapping[0m[38;5;12m (http://research.microsoft.com/apps/pubs/default.aspx?id=69040)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPower Watershed[0m[38;5;12m (http://powerwatershed.sourceforge.net/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGeodesic Graph Cut[0m[38;5;12m (http://www.adobe.com/technology/people/san-jose/brian-price.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSegmentation by Transduction[0m[38;5;12m (http://www.cs.cmu.edu/~olivierd/)[39m

[38;2;255;187;0m[4mVideo Segmentation[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVideo Segmentation with Superpixels[0m[38;5;12m (http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/image-and-video-segmentation/video-segmentation-with-superpixels/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEfficient hierarchical graph-based video segmentation[0m[38;5;12m (http://www.cc.gatech.edu/cpl/projects/videosegmentation/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mObject segmentation in video[0m[38;5;12m (http://lmb.informatik.uni-freiburg.de/Publications/2011/OB11/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStreaming hierarchical video segmentation[0m[38;5;12m (http://www.cse.buffalo.edu/~jcorso/r/supervoxels/)[39m

[38;2;255;187;0m[4mCamera calibration[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCamera Calibration Toolbox for Matlab[0m[38;5;12m (http://www.vision.caltech.edu/bouguetj/calib_doc/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCamera calibration With OpenCV[0m[38;5;12m (http://docs.opencv.org/trunk/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMultiple Camera Calibration Toolbox[0m[38;5;12m (https://sites.google.com/site/prclibo/toolbox)[39m

[38;2;255;187;0m[4mSimultaneous localization and mapping[0m

[38;2;255;187;0m[4mSLAM community:[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mopenSLAM[0m[38;5;12m (https://www.openslam.org/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKitti Odometry: benchmark for outdoor visual odometry (codes may be available)[0m[38;5;12m (http://www.cvlibs.net/datasets/kitti/eval_odometry.php)[39m

[38;2;255;187;0m[4mTracking/Odometry:[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLIBVISO2: C++ Library for Visual Odometry 2[0m[38;5;12m (http://www.cvlibs.net/software/libviso/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPTAM: Parallel tracking and mapping[0m[38;5;12m (http://www.robots.ox.ac.uk/~gk/PTAM/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKFusion: Implementation of KinectFusion[0m[38;5;12m (https://github.com/GerhardR/kfusion)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mkinfu_remake: Lightweight, reworked and optimized version of Kinfu.[0m[38;5;12m (https://github.com/Nerei/kinfu_remake)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction[0m[38;5;12m (http://las-vegas.uni-osnabrueck.de/related-projects/lvr-kinfu/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mInfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion[0m[38;5;12m (http://www.robots.ox.ac.uk/~victor/infinitam/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVoxelHashing: Large-scale KinectFusion[0m[38;5;12m (https://github.com/nachtmar/VoxelHashing)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSLAMBench: Multiple-implementation of KinectFusion[0m[38;5;12m (http://apt.cs.manchester.ac.uk/projects/PAMELA/tools/SLAMBench/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSVO: Semi-direct visual odometry[0m[38;5;12m (https://github.com/uzh-rpg/rpg_svo)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDVO: dense visual odometry[0m[38;5;12m (https://github.com/tum-vision/dvo_slam)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFOVIS: RGB-D visual odometry[0m[38;5;12m (https://code.google.com/p/fovis/)[39m

[38;2;255;187;0m[4mGraph Optimization:[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGTSAM: General smoothing and mapping library for Robotics and SFM[0m[38;5;12m (https://collab.cc.gatech.edu/borg/gtsam?destination=node%2F299) -- Georgia Institute of Technology[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mG2O: General framework for graph optomization[0m[38;5;12m (https://github.com/RainerKuemmerle/g2o)[39m

[38;2;255;187;0m[4mLoop Closure:[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFabMap: appearance-based loop closure system[0m[38;5;12m (http://www.robots.ox.ac.uk/~mjc/Software.htm) - also available in [39m[38;5;14m[1mOpenCV2.4.11[0m[38;5;12m (http://docs.opencv.org/2.4/modules/contrib/doc/openfabmap.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDBoW2: binary bag-of-words loop detection system[0m[38;5;12m (http://webdiis.unizar.es/~dorian/index.php?p=32)[39m

[38;2;255;187;0m[4mLocalization & Mapping:[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRatSLAM[0m[38;5;12m (https://code.google.com/p/ratslam/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLSD-SLAM[0m[38;5;12m (https://github.com/tum-vision/lsd_slam)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mORB-SLAM[0m[38;5;12m (https://github.com/raulmur/ORB_SLAM)[39m

[38;2;255;187;0m[4mSingle-view Spatial Understanding[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGeometric Context[0m[38;5;12m (http://web.engr.illinois.edu/~dhoiem/projects/software.html) - Derek Hoiem (CMU)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRecovering Spatial Layout[0m[38;5;12m (http://web.engr.illinois.edu/~dhoiem/software/counter.php?Down=varsha_spatialLayout.zip) - Varsha Hedau (UIUC)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGeometric Reasoning[0m[38;5;12m (http://www.cs.cmu.edu/~./dclee/code/index.html) - David C. Lee (CMU)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRGBD2Full3D[0m[38;5;12m (https://github.com/arron2003/rgbd2full3d) - Ruiqi Guo (UIUC)[39m

[38;2;255;187;0m[4mObject Detection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mINRIA Object Detection and Localization Toolkit[0m[38;5;12m (http://pascal.inrialpes.fr/soft/olt/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDiscriminatively trained deformable part models[0m[38;5;12m (http://www.cs.berkeley.edu/~rbg/latent/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVOC-DPM[0m[38;5;12m (https://github.com/rbgirshick/voc-dpm)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHistograms of Sparse Codes for Object Detection[0m[38;5;12m (http://www.ics.uci.edu/~dramanan/software/sparse/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mR-CNN: Regions with Convolutional Neural Network Features[0m[38;5;12m (https://github.com/rbgirshick/rcnn)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSPP-Net[0m[38;5;12m (https://github.com/ShaoqingRen/SPP_net)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBING: Objectness Estimation[0m[38;5;12m (http://mmcheng.net/bing/comment-page-9/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEdge Boxes[0m[38;5;12m (https://github.com/pdollar/edges)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mReInspect[0m[38;5;12m (https://github.com/Russell91/ReInspect)[39m

[38;2;255;187;0m[4mNearest Neighbor Search[0m

[38;2;255;187;0m[4mGeneral purpose nearest neighbor search[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mANN: A Library for Approximate Nearest Neighbor Searching[0m[38;5;12m (http://www.cs.umd.edu/~mount/ANN/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFLANN - Fast Library for Approximate Nearest Neighbors[0m[38;5;12m (http://www.cs.ubc.ca/research/flann/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFast k nearest neighbor search using GPU[0m[38;5;12m (http://vincentfpgarcia.github.io/kNN-CUDA/)[39m

[38;2;255;187;0m[4mNearest Neighbor Field Estimation[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPatchMatch[0m[38;5;12m (http://gfx.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/index.php)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGeneralized PatchMatch[0m[38;5;12m (http://gfx.cs.princeton.edu/pubs/Barnes_2010_TGP/index.php)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCoherency Sensitive Hashing[0m[38;5;12m (http://www.eng.tau.ac.il/~simonk/CSH/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPMBP: PatchMatch Belief Propagation[0m[38;5;12m (https://github.com/fbesse/pmbp)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTreeCANN[0m[38;5;12m (http://www.eng.tau.ac.il/~avidan/papers/TreeCANN_code_20121022.rar)[39m

[38;2;255;187;0m[4mVisual Tracking[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisual Tracker Benchmark[0m[38;5;12m (https://sites.google.com/site/trackerbenchmark/benchmarks/v10)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisual Tracking Challenge[0m[38;5;12m (http://www.votchallenge.net/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKanade-Lucas-Tomasi Feature Tracker[0m[38;5;12m (http://www.ces.clemson.edu/~stb/klt/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mExtended Lucas-Kanade Tracking[0m[38;5;12m (http://www.eng.tau.ac.il/~oron/ELK/ELK.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOnline-boosting Tracking[0m[38;5;12m (http://www.vision.ee.ethz.ch/boostingTrackers/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSpatio-Temporal Context Learning[0m[38;5;12m (http://www4.comp.polyu.edu.hk/~cslzhang/STC/STC.htm)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLocality Sensitive Histograms[0m[38;5;12m (http://www.shengfenghe.com/visual-tracking-via-locality-sensitive-histograms.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEnhanced adaptive coupled-layer LGTracker++[0m[38;5;12m (http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W03/papers/Xiao_An_Enhanced_Adaptive_2013_ICCV_paper.pdf)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTLD: Tracking - Learning - Detection[0m[38;5;12m (http://personal.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCMT: Clustering of Static-Adaptive Correspondences for Deformable Object Tracking[0m[38;5;12m (http://www.gnebehay.com/cmt/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKernelized Correlation Filters[0m[38;5;12m (http://home.isr.uc.pt/~henriques/circulant/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAccurate Scale Estimation for Robust Visual Tracking[0m[38;5;12m (http://www.cvl.isy.liu.se/en/research/objrec/visualtracking/scalvistrack/index.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMultiple Experts using Entropy Minimization[0m[38;5;12m (http://cs-people.bu.edu/jmzhang/MEEM/MEEM.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTGPR[0m[38;5;12m (http://www.dabi.temple.edu/~hbling/code/TGPR.htm)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCF2: Hierarchical Convolutional Features for Visual Tracking[0m[38;5;12m (https://sites.google.com/site/jbhuang0604/publications/cf2)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mModular Tracking Framework[0m[38;5;12m (http://webdocs.cs.ualberta.ca/~vis/mtf/index.html)[39m

[38;2;255;187;0m[4mSaliency Detection[0m

[38;2;255;187;0m[4mAttributes[0m

[38;2;255;187;0m[4mAction Reconition[0m

[38;2;255;187;0m[4mEgocentric cameras[0m

[38;2;255;187;0m[4mHuman-in-the-loop systems[0m

[38;2;255;187;0m[4mImage Captioning[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeuralTalk[0m[38;5;12m (https://github.com/karpathy/neuraltalk﻿) -[39m

[38;2;255;187;0m[4mOptimization[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCeres Solver[0m[38;5;12m (http://ceres-solver.org/) - Nonlinear least-square problem and unconstrained optimization solver[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNLopt[0m[38;5;12m (http://ab-initio.mit.edu/wiki/index.php/NLopt)- Nonlinear least-square problem and unconstrained optimization solver[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenGM[0m[38;5;12m (http://hci.iwr.uni-heidelberg.de/opengm2/) - Factor graph based discrete optimization and inference solver[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGTSAM[0m[38;5;12m (https://collab.cc.gatech.edu/borg/gtsam/) - Factor graph based lease-square optimization solver[39m

[38;2;255;187;0m[4mDeep Learning[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Deep Vision[0m[38;5;12m (https://github.com/kjw0612/awesome-deep-vision)[39m

[38;2;255;187;0m[4mMachine Learning[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAwesome Machine Learning[0m[38;5;12m (https://github.com/josephmisiti/awesome-machine-learning)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBob: a free signal processing and machine learning toolbox for researchers[0m[38;5;12m (http://idiap.github.io/bob/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLIBSVM -- A Library for Support Vector Machines[0m[38;5;12m (https://www.csie.ntu.edu.tw/~cjlin/libsvm/)[39m

[38;2;255;187;0m[4mDatasets[0m

[38;2;255;187;0m[4mExternal Dataset Link Collection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCV Datasets on the web[0m[38;5;12m (http://www.cvpapers.com/datasets.html) - CVPapers[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAre we there yet?[0m[38;5;12m (http://rodrigob.github.io/are_we_there_yet/build/) - Which paper provides the best results on standard dataset X?[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision Dataset on the web[0m[38;5;12m (http://www.cvpapers.com/datasets.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mYet Another Computer Vision Index To Datasets[0m[38;5;12m (http://riemenschneider.hayko.at/vision/dataset/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputerVisionOnline Datasets[0m[38;5;12m (http://www.computervisiononline.com/datasets)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCVOnline Dataset[0m[38;5;12m (http://homepages.inf.ed.ac.uk/cgi/rbf/CVONLINE/entries.pl?TAG363)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCV datasets[0m[38;5;12m (http://clickdamage.com/sourcecode/cv_datasets.php)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mvisionbib[0m[38;5;12m (http://datasets.visionbib.com/info-index.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisualData[0m[38;5;12m (http://www.visualdata.io/)[39m

[38;2;255;187;0m[4mLow-level Vision[0m

[38;2;255;187;0m[4mStereo Vision[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMiddlebury Stereo Vision[0m[38;5;12m (http://vision.middlebury.edu/stereo/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe KITTI Vision Benchmark Suite[0m[38;5;12m (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stero)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLIBELAS: Library for Efficient Large-scale Stereo Matching[0m[38;5;12m (http://www.cvlibs.net/software/libelas/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGround Truth Stixel Dataset[0m[38;5;12m (http://www.6d-vision.com/ground-truth-stixel-dataset)[39m

[38;2;255;187;0m[4mOptical Flow[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMiddlebury Optical Flow Evaluation[0m[38;5;12m (http://vision.middlebury.edu/flow/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMPI-Sintel Optical Flow Dataset and Evaluation[0m[38;5;12m (http://sintel.is.tue.mpg.de/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe KITTI Vision Benchmark Suite[0m[38;5;12m (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHCI Challenge[0m[38;5;12m (http://hci.iwr.uni-heidelberg.de/Benchmarks/document/Challenging_Data_for_Stereo_and_Optical_Flow/)[39m

[38;2;255;187;0m[4mVideo Object Segmentation[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDAVIS: Densely Annotated VIdeo Segmentation[0m[38;5;12m (http://davischallenge.org/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSegTrack v2[0m[38;5;12m (http://web.engr.oregonstate.edu/~lif/SegTrack2/dataset.html)[39m


[38;2;255;187;0m[4mChange Detection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLabeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms[0m[38;5;12m (http://www.gti.ssr.upm.es/data/LASIESTA)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mChangeDetection.net[0m[38;5;12m (http://www.changedetection.net/)[39m

[38;2;255;187;0m[4mImage Super-resolutions[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSingle-Image Super-Resolution: A Benchmark[0m[38;5;12m (https://eng.ucmerced.edu/people/cyang35/ECCV14/ECCV14.html)[39m

[38;2;255;187;0m[4mIntrinsic Images[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGround-truth dataset and baseline evaluations for intrinsic image algorithms[0m[38;5;12m (http://www.mit.edu/~kimo/publications/intrinsic/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntrinsic Images in the Wild[0m[38;5;12m (http://opensurfaces.cs.cornell.edu/intrinsic/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIntrinsic Image Evaluation on Synthetic Complex Scenes[0m[38;5;12m (http://www.cic.uab.cat/Datasets/synthetic_intrinsic_image_dataset/)[39m

[38;2;255;187;0m[4mMaterial Recognition[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mOpenSurface[0m[38;5;12m (http://opensurfaces.cs.cornell.edu/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFlickr Material Database[0m[38;5;12m (http://people.csail.mit.edu/celiu/CVPR2010/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMaterials in Context Dataset[0m[38;5;12m (http://opensurfaces.cs.cornell.edu/publications/minc/)[39m

[38;2;255;187;0m[4mMulti-view Reconsturction[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMulti-View Stereo Reconstruction[0m[38;5;12m (http://vision.middlebury.edu/mview/)[39m

[38;2;255;187;0m[4mSaliency Detection[0m

[38;2;255;187;0m[4mVisual Tracking[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisual Tracker Benchmark[0m[38;5;12m (https://sites.google.com/site/trackerbenchmark/benchmarks/v10)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVisual Tracker Benchmark v1.1[0m[38;5;12m (https://sites.google.com/site/benchmarkpami/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVOT Challenge[0m[38;5;12m (http://www.votchallenge.net/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPrinceton Tracking Benchmark[0m[38;5;12m (http://tracking.cs.princeton.edu/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTracking Manipulation Tasks (TMT)[0m[38;5;12m (http://webdocs.cs.ualberta.ca/~vis/trackDB/)[39m

[38;2;255;187;0m[4mVisual Surveillance[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mVIRAT[0m[38;5;12m (http://www.viratdata.org/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCAM2[0m[38;5;12m (https://cam2.ecn.purdue.edu/)[39m

[38;2;255;187;0m[4mSaliency Detection[0m

[38;2;255;187;0m[4mChange detection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mChangeDetection.net[0m[38;5;12m (http://changedetection.net/)[39m

[38;2;255;187;0m[4mVisual Recognition[0m

[38;2;255;187;0m[4mImage Classification[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe PASCAL Visual Object Classes[0m[38;5;12m (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImageNet Large Scale Visual Recognition Challenge[0m[38;5;12m (http://www.image-net.org/challenges/LSVRC/2014/)[39m

[38;2;255;187;0m[4mSelf-supervised Learning[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPASS: An An ImageNet replacement for self-supervised pretraining without humans[0m[38;5;12m (https://github.com/yukimasano/PASS)[39m

[38;2;255;187;0m[4mScene Recognition[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSUN Database[0m[38;5;12m (http://groups.csail.mit.edu/vision/SUN/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPlace Dataset[0m[38;5;12m (http://places.csail.mit.edu/)[39m

[38;2;255;187;0m[4mObject Detection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe PASCAL Visual Object Classes[0m[38;5;12m (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mImageNet Object Detection Challenge[0m[38;5;12m (http://www.image-net.org/challenges/LSVRC/2014/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMicrosoft COCO[0m[38;5;12m (http://mscoco.org/)[39m

[38;2;255;187;0m[4mSemantic labeling[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mStanford background dataset[0m[38;5;12m (http://dags.stanford.edu/projects/scenedataset.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCamVid[0m[38;5;12m (http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mBarcelona Dataset[0m[38;5;12m (http://www.cs.unc.edu/~jtighe/Papers/ECCV10/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSIFT Flow Dataset[0m[38;5;12m (http://www.cs.unc.edu/~jtighe/Papers/ECCV10/siftflow/SiftFlowDataset.zip)[39m

[38;2;255;187;0m[4mMulti-view Object Detection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1m3D Object Dataset[0m[38;5;12m (http://cvgl.stanford.edu/resources.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mEPFL Car Dataset[0m[38;5;12m (http://cvlab.epfl.ch/data/pose)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKTTI Dection Dataset[0m[38;5;12m (http://www.cvlibs.net/datasets/kitti/eval_object.php)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSUN 3D Dataset[0m[38;5;12m (http://sun3d.cs.princeton.edu/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mPASCAL 3D+[0m[38;5;12m (http://cvgl.stanford.edu/projects/pascal3d.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNYU Car Dataset[0m[38;5;12m (http://nyc3d.cs.cornell.edu/)[39m

[38;2;255;187;0m[4mFine-grained Visual Recognition[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFine-grained Classification Challenge[0m[38;5;12m (https://sites.google.com/site/fgcomp2013/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCaltech-UCSD Birds 200[0m[38;5;12m (http://www.vision.caltech.edu/visipedia/CUB-200.html)[39m

[38;2;255;187;0m[4mPedestrian Detection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCaltech Pedestrian Detection Benchmark[0m[38;5;12m (http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mETHZ Pedestrian Detection[0m[38;5;12m (https://data.vision.ee.ethz.ch/cvl/aess/dataset/)[39m

[38;2;255;187;0m[4mAction Recognition[0m

[38;2;255;187;0m[4mImage-based[0m

[38;2;255;187;0m[4mVideo-based[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHOLLYWOOD2 Dataset[0m[38;5;12m (http://www.di.ens.fr/~laptev/actions/hollywood2/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUCF Sports Action Data Set[0m[38;5;12m (http://crcv.ucf.edu/data/UCF_Sports_Action.php)[39m

[38;2;255;187;0m[4mImage Deblurring[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSun dataset[0m[38;5;12m (http://cs.brown.edu/~lbsun/deblur2013/deblur2013iccp.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLevin dataset[0m[38;5;12m (http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR09Data.rar)[39m

[38;2;255;187;0m[4mImage Captioning[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFlickr 8K[0m[38;5;12m (http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFlickr 30K[0m[38;5;12m (http://shannon.cs.illinois.edu/DenotationGraph/)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMicrosoft COCO[0m[38;5;12m (http://mscoco.org/)[39m

[38;2;255;187;0m[4mScene Understanding[0m
[38;5;12m # [39m[38;5;14m[1mSUN RGB-D[0m[38;5;12m (http://rgbd.cs.princeton.edu/) - A RGB-D Scene Understanding Benchmark Suite[39m
[38;5;12m # [39m[38;5;14m[1mNYU depth v2[0m[38;5;12m (http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) - Indoor Segmentation and Support Inference from RGBD Images[39m

[38;2;255;187;0m[4mAerial images[0m
[38;5;12m # [39m[38;5;14m[1mAerial Image Segmentation[0m[38;5;12m (https://zenodo.org/record/1154821#.WmN9kHWnHIp) - Learning Aerial Image Segmentation From Online Maps[39m


[38;2;255;187;0m[4mResources for students[0m

[38;2;255;187;0m[4mResource link collection[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mResources for students[0m[38;5;12m (http://people.csail.mit.edu/fredo/student.html) - Frédo Durand (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAdvice for Graduate Students[0m[38;5;12m (http://www.dgp.toronto.edu/~hertzman/advice/) - Aaron Hertzmann (Adobe Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGraduate Skills Seminars[0m[38;5;12m (http://www.dgp.toronto.edu/~hertzman/courses/gradSkills/2010/) - Yashar Ganjali, Aaron Hertzmann (University of Toronto)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mResearch Skills[0m[38;5;12m (http://research.microsoft.com/en-us/um/people/simonpj/papers/giving-a-talk/giving-a-talk.htm) - Simon Peyton Jones (Microsoft Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mResource collection[0m[38;5;12m (http://web.engr.illinois.edu/~taoxie/advice.htm) - Tao Xie (UIUC) and Yuan Xie (UCSB)[39m

[38;2;255;187;0m[4mWriting[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWrite Good Papers[0m[38;5;12m (http://people.csail.mit.edu/fredo/FredoGoodWriting.pdf) - Frédo Durand (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNotes on writing[0m[38;5;12m (http://people.csail.mit.edu/fredo/PUBLI/writing.pdf) - Frédo Durand (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to Write a Bad Article[0m[38;5;12m (http://people.csail.mit.edu/fredo/FredoBadWriting.pdf) - Frédo Durand (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to write a good CVPR submission[0m[38;5;12m (http://billf.mit.edu/sites/default/files/documents/cvprPapers.pdf) - William T. Freeman (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to write a great research paper[0m[38;5;12m (https://www.youtube.com/watch?v=g3dkRsTqdDA) - Simon Peyton Jones (Microsoft Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to write a SIGGRAPH paper[0m[38;5;12m (http://www.slideshare.net/jdily/how-to-write-a-siggraph-paper) - SIGGRAPH ASIA 2011 Course[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWriting Research Papers[0m[38;5;12m (http://www.dgp.toronto.edu/~hertzman/advice/writing-technical-papers.pdf) - Aaron Hertzmann (Adobe Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to Write a Paper for SIGGRAPH[0m[38;5;12m (http://www.computer.org/csdl/mags/cg/1987/12/mcg1987120062.pdf) - Jim Blinn[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to Get Your SIGGRAPH Paper Rejected[0m[38;5;12m (http://www.siggraph.org/sites/default/files/kajiya.pdf) - Jim Kajiya (Microsoft Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to write a SIGGRAPH paper[0m[38;5;12m (www.liyiwei.org/courses/how-siga11/liyiwei.pptx) - Li-Yi Wei (The University of Hong Kong)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to Write a Great Paper[0m[38;5;12m (http://www-hagen.informatik.uni-kl.de/~bertram/talks/getpublished.pdf) - Martin Martin Hering Hering--Bertram (Hochschule Bremen University of Applied Sciences)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to have a paper get into SIGGRAPH?[0m[38;5;12m (http://www-ui.is.s.u-tokyo.ac.jp/~takeo/writings/siggraph.html) - Takeo Igarashi (The University of Tokyo)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGood Writing[0m[38;5;12m (http://www.cs.cmu.edu/~pausch/Randy/Randy/raibert.htm) - Marc H. Raibert (Boston Dynamics, Inc.)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to Write a Computer Vision Paper[0m[38;5;12m (http://web.engr.illinois.edu/~dhoiem/presentations/How%20to%20Write%20a%20Computer%20Vison%20Paper.ppt) - Derek Hoiem (UIUC)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCommon mistakes in technical writing[0m[38;5;12m (http://www.cs.dartmouth.edu/~wjarosz/writing.html) - Wojciech Jarosz (Dartmouth College)[39m


[38;2;255;187;0m[4mPresentation[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGiving a Research Talk[0m[38;5;12m (http://people.csail.mit.edu/fredo/TalkAdvice.pdf) - Frédo Durand (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to give a good talk[0m[38;5;12m (http://www.dgp.toronto.edu/~hertzman/courses/gradSkills/2010/GivingGoodTalks.pdf) - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mDesigning conference posters[0m[38;5;12m (http://colinpurrington.com/tips/poster-design) - Colin Purrington[39m

[38;2;255;187;0m[4mResearch[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to do research[0m[38;5;12m (http://people.csail.mit.edu/billf/www/papers/doresearch.pdf) - William T. Freeman (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mYou and Your Research[0m[38;5;12m (http://www.cs.virginia.edu/~robins/YouAndYourResearch.html) - Richard Hamming[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mWarning Signs of Bogus Progress in Research in an Age of Rich Computation and Information[0m[38;5;12m (http://yima.csl.illinois.edu/psfile/bogus.pdf) - Yi Ma (UIUC)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mSeven Warning Signs of Bogus Science[0m[38;5;12m (http://www.quackwatch.com/01QuackeryRelatedTopics/signs.html) - Robert L. Park[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mFive Principles for Choosing Research Problems in Computer Graphics[0m[38;5;12m (https://www.youtube.com/watch?v=v2Qaf8t8I6c) - Thomas Funkhouser (Cornell University)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow To Do Research In the MIT AI Lab[0m[38;5;12m (http://www.cs.indiana.edu/mit.research.how.to.html) - David Chapman (MIT)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRecent Advances in Computer Vision[0m[38;5;12m (http://www.slideshare.net/antiw/recent-advances-in-computer-vision) - Ming-Hsuan Yang (UC Merced)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to Come Up with Research Ideas in Computer Vision?[0m[38;5;12m (http://www.slideshare.net/jbhuang/how-to-come-up-with-new-research-ideas-4005840) - Jia-Bin Huang (UIUC)[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mHow to Read Academic Papers[0m[38;5;12m (http://www.slideshare.net/jbhuang/how-to-read-academic-papers) - Jia-Bin Huang (UIUC)[39m

[38;2;255;187;0m[4mTime Management[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTime Management[0m[38;5;12m (https://www.youtube.com/watch?v=oTugjssqOT0) - Randy Pausch (CMU)[39m

[38;2;255;187;0m[4mBlogs[0m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mLearn OpenCV[0m[38;5;12m (http://www.learnopencv.com/) - Satya Mallick[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mTombone's Computer Vision Blog[0m[38;5;12m (http://www.computervisionblog.com/) - Tomasz Malisiewicz[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer vision for dummies[0m[38;5;12m (http://www.visiondummy.com/) - Vincent Spruyt[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAndrej Karpathy blog[0m[38;5;12m (http://karpathy.github.io/) - Andrej Karpathy[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAI Shack[0m[38;5;12m (http://aishack.in/) - Utkarsh Sinha[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision Talks[0m[38;5;12m (http://computer-vision-talks.com/) - Eugene Khvedchenya[39m
[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision Basics with Python Keras and OpenCV[0m[38;5;12m (https://github.com/jrobchin/Computer-Vision-Basics-with-Python-Keras-and-OpenCV) - Jason Chin (University of Western Ontario)[39m


[38;2;255;187;0m[4mLinks[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Computer Vision Industry[0m[38;5;12m (http://www.cs.ubc.ca/~lowe/vision.html) - David Lowe[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mGerman Computer Vision Research Groups & Companies[0m[38;5;12m (http://hci.iwr.uni-heidelberg.de/Links/German_Vision/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mawesome-deep-learning[0m[38;5;12m (https://github.com/ChristosChristofidis/awesome-deep-learning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mawesome-machine-learning[0m[38;5;12m (https://github.com/josephmisiti/awesome-machine-learning)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCat Paper Collection[0m[38;5;12m (http://www.eecs.berkeley.edu/~junyanz/cat/cat_papers.html)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mComputer Vision News[0m[38;5;12m (http://www.rsipvision.com/computer-vision-news/)[39m
[38;5;12m*[39m
[38;2;255;187;0m[4mSongs[0m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe Fundamental Matrix Song[0m[38;5;12m (http://danielwedge.com/fmatrix/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mThe RANSAC Song[0m[38;5;12m (http://danielwedge.com/ransac/)[39m
[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mMachine Learning A Cappella - Overfitting Thriller[0m[38;5;12m (https://www.youtube.com/watch?v=DQWI1kvmwRg)[39m

[38;2;255;187;0m[4mLicenses[0m
[38;5;12mLicense[39m

[38;5;14m[1m![0m[38;5;12mCC0[39m[38;5;14m[1m (http://i.creativecommons.org/p/zero/1.0/88x31.png)[0m[38;5;12m (http://creativecommons.org/publicdomain/zero/1.0/)[39m

[38;5;12mTo the extent possible under law, [39m[38;5;14m[1mJia-Bin Huang[0m[38;5;12m (www.jiabinhuang.com) has waived all copyright and related or neighboring rights to this work.[39m
