awesome-awesomeness/terminal/computervision

                             Awesome Computer Vision: !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
A curated list of awesome computer vision resources, inspired by awesome-php (https://github.com/ziadoz/awesome-php).

For a list people in computer vision listed with their academic genealogy, please visit here (https://github.com/jbhuang0604/awesome-computer-vision/blob/master/people.md)

Contributing
Please feel free to send me pull requests (https://github.com/jbhuang0604/awesome-computer-vision/pulls) or email (jbhuang@vt.edu) to add links.

Table of Contents

 - Awesome Lists (#awesome-lists)
 - Books (#books)
 - Courses (#courses)
 - Papers (#papers)
 - Software (#software)
 - Datasets (#datasets)
 - Pre-trained Computer Vision Models (#Pre-trained-Computer-Vision-Models)
 - Tutorials and Talks (#tutorials-and-talks)
 - Resources for students (#resources-for-students)
 - Blogs (#blogs)
 - Links (#links)
 - Songs (#songs)

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

Books

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

OpenCV Programming
⟡ Learning OpenCV: Computer Vision with the OpenCV Library (http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134) - Gary Bradski and Adrian Kaehler
⟡ Practical Python and OpenCV (https://www.pyimagesearch.com/practical-python-opencv/) - Adrian Rosebrock
⟡ OpenCV Essentials (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#) - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia
Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia

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

Fundamentals
 ⟡ Linear Algebra and Its Applications (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

Courses

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




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

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



Optimization
 ⟡ Convex Optimization I (http://stanford.edu/class/ee364a/) - Stephen Boyd (Stanford University)
 ⟡ Convex Optimization II (http://stanford.edu/class/ee364b/) - Stephen Boyd (Stanford University)
 ⟡ Convex Optimization (https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/about) - Stephen Boyd (Stanford University)
 ⟡ Optimization at MIT (http://optimization.mit.edu/classes.php) - (MIT)
 ⟡ Convex Optimization (http://www.stat.cmu.edu/~ryantibs/convexopt/) - Ryan Tibshirani (CMU)

Papers

Conference papers on the web
 ⟡ CVPapers (http://www.cvpapers.com/) - Computer vision papers on the web
 ⟡ SIGGRAPH Paper on the web (http://kesen.realtimerendering.com/) - Graphics papers on the web
 ⟡ NIPS Proceedings (http://papers.nips.cc/) - NIPS papers on the web
 ⟡ Computer Vision Foundation open access (http://www.cv-foundation.org/openaccess/menu.py)
 ⟡ Annotated Computer Vision Bibliography (http://iris.usc.edu/Vision-Notes/bibliography/contents.html) - Keith Price (USC)
 ⟡ Calendar of Computer Image Analysis, Computer Vision Conferences (http://iris.usc.edu/Information/Iris-Conferences.html) - (USC)

Survey Papers
 ⟡ Visionbib Survey Paper List (http://surveys.visionbib.com/index.html)
 ⟡ Foundations and Trends® in Computer Graphics and Vision (http://www.nowpublishers.com/CGV)
 ⟡ Computer Vision: A Reference Guide (http://link.springer.com/book/10.1007/978-0-387-31439-6)

 ## Pre-trained Computer Vision Models
 ⟡ List of Computer Vision models (https://github.com/shubham-shahh/Open-Source-Models) These models are trained on custom objects

Tutorials and talks

Computer Vision
 ⟡ Computer Vision Talks (http://www.computervisiontalks.com/) - Lectures, keynotes, panel discussions on computer vision
 ⟡ The Three R's of Computer Vision (https://www.youtube.com/watch?v=Mqg6eorYRIQ) - Jitendra Malik (UC Berkeley) 2013
 ⟡ Applications to Machine Vision (http://videolectures.net/epsrcws08_blake_amv/) - Andrew Blake (Microsoft Research) 2008
 ⟡ The Future of Image Search (http://videolectures.net/kdd08_malik_fis/?q=image) - Jitendra Malik (UC Berkeley) 2008
 ⟡ Should I do a PhD in Computer Vision? (https://www.youtube.com/watch?v=M17oGxh3Ny8) - Fatih Porikli (Australian National University)
 - Graduate Summer School 2013: Computer Vision (http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/?tab=schedule) - IPAM, 2013

Recent Conference Talks
- CVPR 2015 (http://www.pamitc.org/cvpr15/) - Jun 2015
- ECCV 2014 (http://videolectures.net/eccv2014_zurich/) - Sep 2014
- CVPR 2014 (http://techtalks.tv/cvpr-2014-oral-talks/) - Jun 2014
- ICCV 2013 (http://techtalks.tv/iccv2013/) - Dec 2013
- ICML 2013 (http://techtalks.tv/icml/2013/) - Jul 2013
- CVPR 2013 (http://techtalks.tv/cvpr2013/) - Jun 2013
- ECCV 2012 (http://videolectures.net/eccv2012_firenze/) - Oct 2012
- ICML 2012 (http://techtalks.tv/icml/2012/orals/) - Jun 2012
- CVPR 2012 (http://techtalks.tv/cvpr2012webcast/) - Jun 2012

3D Computer Vision
 ⟡ 3D Computer Vision: Past, Present, and Future (https://www.youtube.com/watch?v=kyIzMr917Rc) - Steve Seitz (University of Washington) 2011
 ⟡ Reconstructing the World from Photos on the Internet (https://www.youtube.com/watch?v=04Kgg3QEXFI) - Steve Seitz (University of Washington) 2013

Internet Vision
 ⟡ The Distributed Camera (http://www.technologyreview.com/video/426265/meet-2011-tr35-winner-noah-snavely/) - Noah Snavely (Cornell University) 2011
 ⟡ Planet-Scale Visual Understanding (https://www.youtube.com/watch?v=UHkCa9-Z1Ps) - Noah Snavely (Cornell University) 2014
 ⟡ A Trillion Photos (https://www.youtube.com/watch?v=6MWEfpKUfRc) - Steve Seitz (University of Washington) 2013

Computational Photography
 ⟡ Reflections on Image-Based Modeling and Rendering (https://www.youtube.com/watch?v=j90_0Ndk7XM) - Richard Szeliski (Microsoft Research) 2013
 ⟡ Photographing Events over Time (https://www.youtube.com/watch?v=ZvPaHZZVPRk) - William T. Freeman (MIT) 2011
 ⟡ Old and New algorithm for Blind Deconvolution (http://videolectures.net/nipsworkshops2011_weiss_deconvolution/) -  Yair Weiss (The Hebrew University of Jerusalem) 2011
 ⟡ A Tour of Modern "Image Processing" (http://videolectures.net/nipsworkshops2010_milanfar_tmi/) -  Peyman Milanfar (UC Santa Cruz/Google) 2010
 ⟡ Topics in image and video processing (http://videolectures.net/mlss07_blake_tiivp/) Andrew Blake (Microsoft Research) 2007
 ⟡ Computational Photography (https://www.youtube.com/watch?v=HJVNI0mkmqk) - William T. Freeman (MIT) 2012
 ⟡ Revealing the Invisible (https://www.youtube.com/watch?v=_BWnIQY_X98) - Frédo Durand (MIT) 2012
 ⟡ Overview of Computer Vision and Visual Effects (https://www.youtube.com/watch?v=rE-hVtytT-I) - Rich Radke (Rensselaer Polytechnic Institute) 2014

Learning and Vision
 ⟡ Where machine vision needs help from machine learning (http://videolectures.net/colt2011_freeman_help/?q=computer%20vision) - William T. Freeman (MIT) 2011
 ⟡ Learning in Computer Vision (http://videolectures.net/mlss08au_lucey_linv/) - Simon Lucey (CMU) 2008
 ⟡ Learning and Inference in Low-Level Vision (http://videolectures.net/nips09_weiss_lil/?q=computer%20vision) - Yair Weiss (The Hebrew University of Jerusalem) 2009

Object Recognition
 ⟡ Object Recognition (http://research.microsoft.com/apps/video/dl.aspx?id=231358) - Larry Zitnick (Microsoft Research)
 ⟡ Generative Models for Visual Objects and Object Recognition via Bayesian Inference (http://videolectures.net/mlas06_li_gmvoo/?q=Fei-Fei%20Li) - Fei-Fei Li (Stanford University)

Graphical Models
 ⟡ Graphical Models for Computer Vision (http://videolectures.net/uai2012_felzenszwalb_computer_vision/?q=computer%20vision) - Pedro Felzenszwalb (Brown University) 2012
 ⟡ Graphical Models (http://videolectures.net/mlss09uk_ghahramani_gm/) - Zoubin Ghahramani (University of Cambridge) 2009
 ⟡ Machine Learning, Probability and Graphical Models (http://videolectures.net/mlss06tw_roweis_mlpgm/) - Sam Roweis (NYU) 2006
 ⟡ Graphical Models and Applications (http://videolectures.net/mlss09us_weiss_gma/?q=Graphical%20Models) -  Yair Weiss (The Hebrew University of Jerusalem) 2009

Machine Learning
 ⟡ A Gentle Tutorial of the EM Algorithm (https://nikola-rt.ee.washington.edu/people/bulyko/papers/em.pdf) - Jeff A. Bilmes (UC Berkeley) 1998
 ⟡ Introduction To Bayesian Inference (http://videolectures.net/mlss09uk_bishop_ibi/) - Christopher Bishop (Microsoft Research) 2009
 ⟡ Support Vector Machines (http://videolectures.net/mlss06tw_lin_svm/) - Chih-Jen Lin (National Taiwan University) 2006
 ⟡ Bayesian or Frequentist, Which Are You?  (http://videolectures.net/mlss09uk_jordan_bfway/) - Michael I. Jordan (UC Berkeley)

Optimization
 ⟡ Optimization Algorithms in Machine Learning (http://videolectures.net/nips2010_wright_oaml/) - Stephen J. Wright (University of Wisconsin-Madison)
 ⟡ Convex Optimization (http://videolectures.net/mlss07_vandenberghe_copt/?q=convex%20optimization) - Lieven Vandenberghe (University of California, Los Angeles)
 ⟡ Continuous Optimization in Computer Vision (https://www.youtube.com/watch?v=oZqoWozVDVg) - Andrew Fitzgibbon (Microsoft Research)
 ⟡ Beyond stochastic gradient descent for large-scale machine learning (http://videolectures.net/sahd2014_bach_stochastic_gradient/) - Francis Bach (INRIA)
 ⟡ Variational Methods for Computer Vision (https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) - Daniel Cremers (Technische Universität München) (lecture 18 missing from playlist 
(https://www.youtube.com/watch?v=GgcbVPNd3SI))

Deep Learning
 ⟡ A tutorial on Deep Learning (http://videolectures.net/jul09_hinton_deeplearn/) - Geoffrey E. Hinton (University of Toronto)
 ⟡ Deep Learning (http://videolectures.net/kdd2014_salakhutdinov_deep_learning/?q=Hidden%20Markov%20model#) -  Ruslan Salakhutdinov (University of Toronto)
 ⟡ Scaling up Deep Learning (http://videolectures.net/kdd2014_bengio_deep_learning/) - Yoshua Bengio (University of Montreal)
 ⟡ ImageNet Classification with Deep Convolutional Neural Networks (http://videolectures.net/machine_krizhevsky_imagenet_classification/?q=deep%20learning) -  Alex Krizhevsky (University of Toronto)
 ⟡ The Unreasonable Effectivness Of Deep Learning (http://videolectures.net/sahd2014_lecun_deep_learning/) Yann LeCun (NYU/Facebook Research) 2014
 ⟡ Deep Learning for Computer Vision (https://www.youtube.com/watch?v=qgx57X0fBdA) - Rob Fergus (NYU/Facebook Research)
 ⟡ High-dimensional learning with deep network contractions (http://videolectures.net/sahd2014_mallat_dimensional_learning/) - Stéphane Mallat (Ecole Normale Superieure)
 ⟡ Graduate Summer School 2012: Deep Learning, Feature Learning (http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule) - IPAM, 2012
 ⟡ Workshop on Big Data and Statistical Machine Learning (http://www.fields.utoronto.ca/programs/scientific/14-15/bigdata/machine/)
 ⟡ Machine Learning Summer School (https://www.youtube.com/channel/UC3ywjSv5OsDiDAnOP8C1NiQ) - Reykjavik, Iceland 2014
* **Deep Learning Session 1** (https://www.youtube.com/watch?v=JuimBuvEWBg) - Yoshua Bengio (Universtiy of Montreal)
* **Deep Learning Session 2** (https://www.youtube.com/watch?v=Fl-W7_z3w3o) - Yoshua Bengio (University of Montreal)
* **Deep Learning Session 3** (https://www.youtube.com/watch?v=_cohR7LAgWA) - Yoshua Bengio (University of Montreal)

Software

Annotation tools
⟡ Comma Coloring (http://commacoloring.herokuapp.com/)
⟡ Annotorious (https://annotorious.github.io/)
⟡ LabelME (http://labelme.csail.mit.edu/Release3.0/)
⟡ gtmaker (https://github.com/sanko-shoko/gtmaker)

External Resource Links
 ⟡ Computer Vision Resources (https://sites.google.com/site/jbhuang0604/resources/vision) - Jia-Bin Huang (UIUC)
 ⟡ Computer Vision Algorithm Implementations (http://www.cvpapers.com/rr.html) - CVPapers
 ⟡ Source Code Collection for Reproducible Research (http://www.csee.wvu.edu/~xinl/reproducible_research.html) - Xin Li (West Virginia University)
 ⟡ CMU Computer Vision Page (http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/v-source.html)

General Purpose Computer Vision Library
⟡ Open CV (http://opencv.org/)
⟡ mexopencv (http://kyamagu.github.io/mexopencv/)
⟡ SimpleCV (http://simplecv.org/)
⟡ Open source Python module for computer vision (https://github.com/jesolem/PCV)
⟡ ccv: A Modern Computer Vision Library (https://github.com/liuliu/ccv)
⟡ VLFeat (http://www.vlfeat.org/)
⟡ Matlab Computer Vision System Toolbox (http://www.mathworks.com/products/computer-vision/)
⟡ Piotr's Computer Vision Matlab Toolbox (http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html)
⟡ PCL: Point Cloud Library (http://pointclouds.org/)
⟡ ImageUtilities (https://gitorious.org/imageutilities)

Multiple-view Computer Vision
⟡ MATLAB Functions for Multiple View Geometry (http://www.robots.ox.ac.uk/~vgg/hzbook/code/)
⟡ Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis (http://staffhome.ecm.uwa.edu.au/~00011811/Research/MatlabFns/index.html)
⟡ OpenGV  (http://laurentkneip.github.io/opengv/) - geometric computer vision algorithms
⟡ MinimalSolvers (http://cmp.felk.cvut.cz/mini/) - Minimal problems solver
⟡ Multi-View Environment (http://www.gcc.tu-darmstadt.de/home/proj/mve/)
⟡ Visual SFM (http://ccwu.me/vsfm/)
⟡ Bundler SFM (http://www.cs.cornell.edu/~snavely/bundler/)
⟡ openMVG: open Multiple View Geometry (http://imagine.enpc.fr/~moulonp/openMVG/) - Multiple View Geometry; Structure from Motion library & softwares
⟡ Patch-based Multi-view Stereo V2 (http://www.di.ens.fr/pmvs/)
⟡ Clustering Views for Multi-view Stereo (http://www.di.ens.fr/cmvs/)
⟡ Floating Scale Surface Reconstruction (http://www.gris.informatik.tu-darmstadt.de/projects/floating-scale-surface-recon/)
⟡ Large-Scale Texturing of 3D Reconstructions (http://www.gcc.tu-darmstadt.de/home/proj/texrecon/)
⟡ Awesome 3D reconstruction list (https://github.com/openMVG/awesome_3DReconstruction_list)


Feature Detection and Extraction
⟡ VLFeat (http://www.vlfeat.org/)
⟡ SIFT (http://www.cs.ubc.ca/~lowe/keypoints/)
  ⟡ David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
⟡ SIFT++ (http://www.robots.ox.ac.uk/~vedaldi/code/siftpp.html)
⟡ BRISK (http://www.asl.ethz.ch/people/lestefan/personal/BRISK)
  ⟡ Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011
⟡ SURF (http://www.vision.ee.ethz.ch/~surf/)
  ⟡ Herbert 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
⟡ FREAK (http://www.ivpe.com/freak.htm)
  ⟡ A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012
⟡ AKAZE (http://www.robesafe.com/personal/pablo.alcantarilla/kaze.html)
  ⟡ Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012
⟡ Local Binary Patterns (https://github.com/nourani/LBP)

High Dynamic Range Imaging
⟡ HDR_Toolbox (https://github.com/banterle/HDR_Toolbox)

Semantic Segmentation
⟡ List of Semantic Segmentation algorithms (http://www.it-caesar.com/list-of-contemporary-semantic-segmentation-datasets/)

Low-level Vision

Stereo Vision
 ⟡ Middlebury Stereo Vision (http://vision.middlebury.edu/stereo/)
 ⟡ The KITTI Vision Benchmark Suite (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stero)
 ⟡ LIBELAS: Library for Efficient Large-scale Stereo Matching (http://www.cvlibs.net/software/libelas/)
 ⟡ Ground Truth Stixel Dataset (http://www.6d-vision.com/ground-truth-stixel-dataset)

Optical Flow
 ⟡ Middlebury Optical Flow Evaluation (http://vision.middlebury.edu/flow/)
 ⟡ MPI-Sintel Optical Flow Dataset and Evaluation (http://sintel.is.tue.mpg.de/)
 ⟡ The KITTI Vision Benchmark Suite (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow)
 ⟡ HCI Challenge (http://hci.iwr.uni-heidelberg.de/Benchmarks/document/Challenging_Data_for_Stereo_and_Optical_Flow/)
 ⟡ Coarse2Fine Optical Flow (http://people.csail.mit.edu/celiu/OpticalFlow/) - Ce Liu (MIT)
 ⟡ Secrets of Optical Flow Estimation and Their Principles (http://cs.brown.edu/~dqsun/code/cvpr10_flow_code.zip)
 ⟡ C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.) (http://people.csail.mit.edu/celiu/OpticalFlow/)
 ⟡ Parallel Robust Optical Flow by Sánchez Pérez et al. (http://www.ctim.es/research_works/parallel_robust_optical_flow/)

Image Denoising
BM3D, KSVD,

Super-resolution
 ⟡ Multi-frame image super-resolution (http://www.robots.ox.ac.uk/~vgg/software/SR/)
* Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 2008
 ⟡ Markov Random Fields for Super-Resolution (http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution.html)
* 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
 ⟡ Sparse regression and natural image prior (https://people.mpi-inf.mpg.de/~kkim/supres/supres.htm)
* 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.
 ⟡ Single-Image Super Resolution via a Statistical Model (http://www.cs.technion.ac.il/~elad/Various/SingleImageSR_TIP14_Box.zip)
* 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
 ⟡ Sparse Coding for Super-Resolution (http://www.cs.technion.ac.il/~elad/Various/Single_Image_SR.zip)
* 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).
 ⟡ Patch-wise Sparse Recovery (http://www.ifp.illinois.edu/~jyang29/ScSR.htm)
* 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.
 ⟡ Neighbor embedding (http://www.jdl.ac.cn/user/hchang/doc/code.rar)
* 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        
ly 2004.                                                                                                                                                                                                                                            
 ⟡ Deformable Patches (https://sites.google.com/site/yuzhushome/single-image-super-resolution-using-deformable-patches)
*  Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014
 ⟡ SRCNN (http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)
* Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014
 ⟡ A+: Adjusted Anchored Neighborhood Regression (http://www.vision.ee.ethz.ch/~timofter/ACCV2014_ID820_SUPPLEMENTARY/index.html)
* R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014
 ⟡ Transformed Self-Exemplars (https://sites.google.com/site/jbhuang0604/publications/struct_sr)
* 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

Image Deblurring

Non-blind deconvolution
 ⟡ Spatially variant non-blind deconvolution (http://homes.cs.washington.edu/~shanqi/work/spvdeconv/)
 ⟡ Handling Outliers in Non-blind Image Deconvolution (http://cg.postech.ac.kr/research/deconv_outliers/)
 ⟡ Hyper-Laplacian Priors (http://cs.nyu.edu/~dilip/research/fast-deconvolution/)
 ⟡ From Learning Models of Natural Image Patches to Whole Image Restoration (http://people.csail.mit.edu/danielzoran/epllcode.zip)
 ⟡ Deep Convolutional Neural Network for Image Deconvolution (http://lxu.me/projects/dcnn/)
 ⟡ Neural Deconvolution (http://webdav.is.mpg.de/pixel/neural_deconvolution/)

Blind deconvolution
 ⟡ Removing Camera Shake From A Single Photograph (http://www.cs.nyu.edu/~fergus/research/deblur.html)
 ⟡ High-quality motion deblurring from a single image (http://www.cse.cuhk.edu.hk/leojia/projects/motion_deblurring/)
 ⟡ Two-Phase Kernel Estimation for Robust Motion Deblurring (http://www.cse.cuhk.edu.hk/leojia/projects/robust_deblur/)
 ⟡ Blur kernel estimation using the radon transform (http://people.csail.mit.edu/taegsang/Documents/RadonDeblurringCode.zip)
 ⟡ Fast motion deblurring (http://cg.postech.ac.kr/research/fast_motion_deblurring/)
 ⟡ Blind Deconvolution Using a Normalized Sparsity Measure (http://cs.nyu.edu//~dilip/research/blind-deconvolution/)
 ⟡ Blur-kernel estimation from spectral irregularities (http://www.cs.huji.ac.il/~raananf/projects/deblur/)
 ⟡ Efficient marginal likelihood optimization in blind deconvolution (http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR2011Code.zip)
 ⟡ Unnatural L0 Sparse Representation for Natural Image Deblurring (http://www.cse.cuhk.edu.hk/leojia/projects/l0deblur/)
 ⟡ Edge-based Blur Kernel Estimation Using Patch Priors (http://cs.brown.edu/~lbsun/deblur2013/deblur2013iccp.html)
 ⟡ Blind Deblurring Using Internal Patch Recurrence (http://www.wisdom.weizmann.ac.il/~vision/BlindDeblur.html)

Non-uniform Deblurring
 ⟡ Non-uniform Deblurring for Shaken Images (http://www.di.ens.fr/willow/research/deblurring/)
 ⟡ Single Image Deblurring Using Motion Density Functions (http://grail.cs.washington.edu/projects/mdf_deblurring/)
 ⟡ Image Deblurring using Inertial Measurement Sensors (http://research.microsoft.com/en-us/um/redmond/groups/ivm/imudeblurring/)
 ⟡ Fast Removal of Non-uniform Camera Shake (http://webdav.is.mpg.de/pixel/fast_removal_of_camera_shake/)


Image Completion
 ⟡ GIMP Resynthesizer (http://registry.gimp.org/node/27986)
 ⟡ Priority BP (http://lafarren.com/image-completer/)
 ⟡ ImageMelding (http://www.ece.ucsb.edu/~psen/melding)
 ⟡ PlanarStructureCompletion (https://sites.google.com/site/jbhuang0604/publications/struct_completion)

Image Retargeting
 ⟡ RetargetMe (http://people.csail.mit.edu/mrub/retargetme/)

Alpha Matting
 ⟡ Alpha Matting Evaluation (http://www.alphamatting.com/)
 ⟡ Closed-form image matting (http://people.csail.mit.edu/alevin/matting.tar.gz)
 ⟡ Spectral Matting (http://www.vision.huji.ac.il/SpectralMatting/)
 ⟡ Learning-based Matting (http://www.mathworks.com/matlabcentral/fileexchange/31412-learning-based-digital-matting)
 ⟡ Improving Image Matting using Comprehensive Sampling Sets (http://www.alphamatting.com/ImprovingMattingComprehensiveSamplingSets_CVPR2013.zip)

Image Pyramid
⟡ The Steerable Pyramid (http://www.cns.nyu.edu/~eero/steerpyr/)
⟡ CurveLab (http://www.curvelet.org/)

Edge-preserving image processing
 ⟡ Fast Bilateral Filter (http://people.csail.mit.edu/sparis/bf/)
 ⟡ O(1) Bilateral Filter (http://www.cs.cityu.edu.hk/~qiyang/publications/code/qx.cvpr09.ctbf.zip)
 ⟡ Recursive Bilateral Filtering (http://www.cs.cityu.edu.hk/~qiyang/publications/eccv-12/)
 ⟡ Rolling Guidance Filter (http://www.cse.cuhk.edu.hk/leojia/projects/rollguidance/)
 ⟡ Relative Total Variation (http://www.cse.cuhk.edu.hk/leojia/projects/texturesep/index.html)
 ⟡ L0 Gradient Optimization (http://www.cse.cuhk.edu.hk/leojia/projects/L0smoothing/index.html)
 ⟡ Domain Transform (http://www.inf.ufrgs.br/~eslgastal/DomainTransform/)
 ⟡ Adaptive Manifold (http://inf.ufrgs.br/~eslgastal/AdaptiveManifolds/)
 ⟡ Guided image filtering (http://research.microsoft.com/en-us/um/people/kahe/eccv10/)

Intrinsic Images

⟡ Recovering Intrinsic Images with a global Sparsity Prior on Reflectance (http://people.tuebingen.mpg.de/mkiefel/projects/intrinsic/)
⟡ Intrinsic Images by Clustering (http://giga.cps.unizar.es/~elenag/projects/EGSR2012_intrinsic/)

Contour Detection and Image Segmentation
 ⟡ Mean Shift Segmentation (http://coewww.rutgers.edu/riul/research/code/EDISON/)
 ⟡ Graph-based Segmentation (http://cs.brown.edu/~pff/segment/)
 ⟡ Normalized Cut (http://www.cis.upenn.edu/~jshi/software/)
 ⟡ Grab Cut (http://grabcut.weebly.com/background--algorithm.html)
 ⟡ Contour Detection and Image Segmentation (http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html)
 ⟡ Structured Edge Detection (http://research.microsoft.com/en-us/downloads/389109f6-b4e8-404c-84bf-239f7cbf4e3d/)
 ⟡ Pointwise Mutual Information (http://web.mit.edu/phillipi/pmi-boundaries/)
 ⟡ SLIC Super-pixel (http://ivrl.epfl.ch/research/superpixels)
 ⟡ QuickShift (http://www.vlfeat.org/overview/quickshift.html)
 ⟡ TurboPixels (http://www.cs.toronto.edu/~babalex/research.html)
 ⟡ Entropy Rate Superpixel (http://mingyuliu.net/)
 ⟡ Contour Relaxed Superpixels (http://www.vsi.cs.uni-frankfurt.de/research/current-projects/research/superpixel-segmentation/)
 ⟡ SEEDS (http://www.mvdblive.org/seeds/)
 ⟡ SEEDS Revised (https://github.com/davidstutz/seeds-revised)
 ⟡ Multiscale Combinatorial Grouping (http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/)
 ⟡ Fast Edge Detection Using Structured Forests (https://github.com/pdollar/edges)

Interactive Image Segmentation
 ⟡ Random Walker (http://cns.bu.edu/~lgrady/software.html)
 ⟡ Geodesic Segmentation (http://www.tc.umn.edu/~baixx015/)
 ⟡ Lazy Snapping (http://research.microsoft.com/apps/pubs/default.aspx?id=69040)
 ⟡ Power Watershed (http://powerwatershed.sourceforge.net/)
 ⟡ Geodesic Graph Cut (http://www.adobe.com/technology/people/san-jose/brian-price.html)
 ⟡ Segmentation by Transduction (http://www.cs.cmu.edu/~olivierd/)

Video Segmentation
 ⟡ Video Segmentation with Superpixels (http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/image-and-video-segmentation/video-segmentation-with-superpixels/)
 ⟡ Efficient hierarchical graph-based video segmentation (http://www.cc.gatech.edu/cpl/projects/videosegmentation/)
 ⟡ Object segmentation in video (http://lmb.informatik.uni-freiburg.de/Publications/2011/OB11/)
 ⟡ Streaming hierarchical video segmentation (http://www.cse.buffalo.edu/~jcorso/r/supervoxels/)

Camera calibration
 ⟡ Camera Calibration Toolbox for Matlab (http://www.vision.caltech.edu/bouguetj/calib_doc/)
 ⟡ Camera calibration With OpenCV (http://docs.opencv.org/trunk/doc/tutorials/calib3d/camera_calibration/camera_calibration.html#)
 ⟡ Multiple Camera Calibration Toolbox (https://sites.google.com/site/prclibo/toolbox)

Simultaneous localization and mapping

SLAM community:
 ⟡ openSLAM (https://www.openslam.org/)
 ⟡ Kitti Odometry: benchmark for outdoor visual odometry (codes may be available) (http://www.cvlibs.net/datasets/kitti/eval_odometry.php)

Tracking/Odometry:
 ⟡ LIBVISO2: C++ Library for Visual Odometry 2 (http://www.cvlibs.net/software/libviso/)
 ⟡ PTAM: Parallel tracking and mapping (http://www.robots.ox.ac.uk/~gk/PTAM/)
 ⟡ KFusion: Implementation of KinectFusion (https://github.com/GerhardR/kfusion)
 ⟡ kinfu_remake: Lightweight, reworked and optimized version of Kinfu. (https://github.com/Nerei/kinfu_remake)
 ⟡ LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction (http://las-vegas.uni-osnabrueck.de/related-projects/lvr-kinfu/)
 ⟡ InfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion (http://www.robots.ox.ac.uk/~victor/infinitam/)
 ⟡ VoxelHashing: Large-scale KinectFusion (https://github.com/nachtmar/VoxelHashing)
 ⟡ SLAMBench: Multiple-implementation of KinectFusion (http://apt.cs.manchester.ac.uk/projects/PAMELA/tools/SLAMBench/)
 ⟡ SVO: Semi-direct visual odometry (https://github.com/uzh-rpg/rpg_svo)
 ⟡ DVO: dense visual odometry (https://github.com/tum-vision/dvo_slam)
 ⟡ FOVIS: RGB-D visual odometry (https://code.google.com/p/fovis/)

Graph Optimization:
 ⟡ GTSAM: General smoothing and mapping library for Robotics and SFM (https://collab.cc.gatech.edu/borg/gtsam?destination=node%2F299) -- Georgia Institute of Technology
 ⟡ G2O: General framework for graph optomization (https://github.com/RainerKuemmerle/g2o)

Loop Closure:
 ⟡ FabMap: appearance-based loop closure system (http://www.robots.ox.ac.uk/~mjc/Software.htm) - also available in OpenCV2.4.11 (http://docs.opencv.org/2.4/modules/contrib/doc/openfabmap.html)
 ⟡ DBoW2: binary bag-of-words loop detection system (http://webdiis.unizar.es/~dorian/index.php?p=32)

Localization & Mapping:
 ⟡ RatSLAM (https://code.google.com/p/ratslam/)
 ⟡ LSD-SLAM (https://github.com/tum-vision/lsd_slam)
 ⟡ ORB-SLAM (https://github.com/raulmur/ORB_SLAM)

Single-view Spatial Understanding
 ⟡ Geometric Context (http://web.engr.illinois.edu/~dhoiem/projects/software.html) - Derek Hoiem (CMU)
 ⟡ Recovering Spatial Layout (http://web.engr.illinois.edu/~dhoiem/software/counter.php?Down=varsha_spatialLayout.zip) - Varsha Hedau (UIUC)
 ⟡ Geometric Reasoning (http://www.cs.cmu.edu/~./dclee/code/index.html) - David C. Lee (CMU)
 ⟡ RGBD2Full3D (https://github.com/arron2003/rgbd2full3d) - Ruiqi Guo (UIUC)

Object Detection
 ⟡ INRIA Object Detection and Localization Toolkit (http://pascal.inrialpes.fr/soft/olt/)
 ⟡ Discriminatively trained deformable part models (http://www.cs.berkeley.edu/~rbg/latent/)
 ⟡ VOC-DPM (https://github.com/rbgirshick/voc-dpm)
 ⟡ Histograms of Sparse Codes for Object Detection (http://www.ics.uci.edu/~dramanan/software/sparse/)
 ⟡ R-CNN: Regions with Convolutional Neural Network Features (https://github.com/rbgirshick/rcnn)
 ⟡ SPP-Net (https://github.com/ShaoqingRen/SPP_net)
 ⟡ BING: Objectness Estimation (http://mmcheng.net/bing/comment-page-9/)
 ⟡ Edge Boxes (https://github.com/pdollar/edges)
 ⟡ ReInspect (https://github.com/Russell91/ReInspect)

Nearest Neighbor Search

General purpose nearest neighbor search
 ⟡ ANN: A Library for Approximate Nearest Neighbor Searching (http://www.cs.umd.edu/~mount/ANN/)
 ⟡ FLANN - Fast Library for Approximate Nearest Neighbors (http://www.cs.ubc.ca/research/flann/)
 ⟡ Fast k nearest neighbor search using GPU (http://vincentfpgarcia.github.io/kNN-CUDA/)

Nearest Neighbor Field Estimation
 ⟡ PatchMatch (http://gfx.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/index.php)
 ⟡ Generalized PatchMatch (http://gfx.cs.princeton.edu/pubs/Barnes_2010_TGP/index.php)
 ⟡ Coherency Sensitive Hashing (http://www.eng.tau.ac.il/~simonk/CSH/)
 ⟡ PMBP: PatchMatch Belief Propagation (https://github.com/fbesse/pmbp)
 ⟡ TreeCANN (http://www.eng.tau.ac.il/~avidan/papers/TreeCANN_code_20121022.rar)

Visual Tracking
⟡ Visual Tracker Benchmark (https://sites.google.com/site/trackerbenchmark/benchmarks/v10)
⟡ Visual Tracking Challenge (http://www.votchallenge.net/)
⟡ Kanade-Lucas-Tomasi Feature Tracker (http://www.ces.clemson.edu/~stb/klt/)
⟡ Extended Lucas-Kanade Tracking (http://www.eng.tau.ac.il/~oron/ELK/ELK.html)
⟡ Online-boosting Tracking (http://www.vision.ee.ethz.ch/boostingTrackers/)
⟡ Spatio-Temporal Context Learning (http://www4.comp.polyu.edu.hk/~cslzhang/STC/STC.htm)
⟡ Locality Sensitive Histograms (http://www.shengfenghe.com/visual-tracking-via-locality-sensitive-histograms.html)
⟡ Enhanced adaptive coupled-layer LGTracker++ (http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W03/papers/Xiao_An_Enhanced_Adaptive_2013_ICCV_paper.pdf)
⟡ TLD: Tracking - Learning - Detection (http://personal.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html)
⟡ CMT: Clustering of Static-Adaptive Correspondences for Deformable Object Tracking (http://www.gnebehay.com/cmt/)
⟡ Kernelized Correlation Filters (http://home.isr.uc.pt/~henriques/circulant/)
⟡ Accurate Scale Estimation for Robust Visual Tracking (http://www.cvl.isy.liu.se/en/research/objrec/visualtracking/scalvistrack/index.html)
⟡ Multiple Experts using Entropy Minimization (http://cs-people.bu.edu/jmzhang/MEEM/MEEM.html)
⟡ TGPR (http://www.dabi.temple.edu/~hbling/code/TGPR.htm)
⟡ CF2: Hierarchical Convolutional Features for Visual Tracking (https://sites.google.com/site/jbhuang0604/publications/cf2)
⟡ Modular Tracking Framework (http://webdocs.cs.ualberta.ca/~vis/mtf/index.html)

Saliency Detection

Attributes

Action Reconition

Egocentric cameras

Human-in-the-loop systems

Image Captioning
 ⟡ NeuralTalk (https://github.com/karpathy/neuraltalk) -

Optimization
 ⟡ Ceres Solver (http://ceres-solver.org/) - Nonlinear least-square problem and unconstrained optimization solver
 ⟡ NLopt (http://ab-initio.mit.edu/wiki/index.php/NLopt)- Nonlinear least-square problem and unconstrained optimization solver
 ⟡ OpenGM (http://hci.iwr.uni-heidelberg.de/opengm2/) - Factor graph based discrete optimization and inference solver
 ⟡ GTSAM (https://collab.cc.gatech.edu/borg/gtsam/) - Factor graph based lease-square optimization solver

Deep Learning
 ⟡ Awesome Deep Vision (https://github.com/kjw0612/awesome-deep-vision)

Machine Learning
 ⟡ Awesome Machine Learning (https://github.com/josephmisiti/awesome-machine-learning)
 ⟡ Bob: a free signal processing and machine learning toolbox for researchers (http://idiap.github.io/bob/)
 ⟡ LIBSVM -- A Library for Support Vector Machines (https://www.csie.ntu.edu.tw/~cjlin/libsvm/)

Datasets

External Dataset Link Collection
 ⟡ CV Datasets on the web (http://www.cvpapers.com/datasets.html) - CVPapers
 ⟡ Are we there yet? (http://rodrigob.github.io/are_we_there_yet/build/) - Which paper provides the best results on standard dataset X?
 ⟡ Computer Vision Dataset on the web (http://www.cvpapers.com/datasets.html)
 ⟡ Yet Another Computer Vision Index To Datasets (http://riemenschneider.hayko.at/vision/dataset/)
 ⟡ ComputerVisionOnline Datasets (http://www.computervisiononline.com/datasets)
 ⟡ CVOnline Dataset (http://homepages.inf.ed.ac.uk/cgi/rbf/CVONLINE/entries.pl?TAG363)
 ⟡ CV datasets (http://clickdamage.com/sourcecode/cv_datasets.php)
 ⟡ visionbib (http://datasets.visionbib.com/info-index.html)
 ⟡ VisualData (http://www.visualdata.io/)

Low-level Vision

Stereo Vision
 ⟡ Middlebury Stereo Vision (http://vision.middlebury.edu/stereo/)
 ⟡ The KITTI Vision Benchmark Suite (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stero)
 ⟡ LIBELAS: Library for Efficient Large-scale Stereo Matching (http://www.cvlibs.net/software/libelas/)
 ⟡ Ground Truth Stixel Dataset (http://www.6d-vision.com/ground-truth-stixel-dataset)

Optical Flow
 ⟡ Middlebury Optical Flow Evaluation (http://vision.middlebury.edu/flow/)
 ⟡ MPI-Sintel Optical Flow Dataset and Evaluation (http://sintel.is.tue.mpg.de/)
 ⟡ The KITTI Vision Benchmark Suite (http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=flow)
 ⟡ HCI Challenge (http://hci.iwr.uni-heidelberg.de/Benchmarks/document/Challenging_Data_for_Stereo_and_Optical_Flow/)

Video Object Segmentation
 ⟡ DAVIS: Densely Annotated VIdeo Segmentation (http://davischallenge.org/)
 ⟡ SegTrack v2 (http://web.engr.oregonstate.edu/~lif/SegTrack2/dataset.html)


Change Detection
 ⟡ Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms (http://www.gti.ssr.upm.es/data/LASIESTA)
 ⟡ ChangeDetection.net (http://www.changedetection.net/)

Image Super-resolutions
 ⟡ Single-Image Super-Resolution: A Benchmark (https://eng.ucmerced.edu/people/cyang35/ECCV14/ECCV14.html)

Intrinsic Images
 ⟡ Ground-truth dataset and baseline evaluations for intrinsic image algorithms (http://www.mit.edu/~kimo/publications/intrinsic/)
 ⟡ Intrinsic Images in the Wild (http://opensurfaces.cs.cornell.edu/intrinsic/)
 ⟡ Intrinsic Image Evaluation on Synthetic Complex Scenes (http://www.cic.uab.cat/Datasets/synthetic_intrinsic_image_dataset/)

Material Recognition
 ⟡ OpenSurface (http://opensurfaces.cs.cornell.edu/)
 ⟡ Flickr Material Database (http://people.csail.mit.edu/celiu/CVPR2010/)
 ⟡ Materials in Context Dataset (http://opensurfaces.cs.cornell.edu/publications/minc/)

Multi-view Reconsturction
⟡ Multi-View Stereo Reconstruction (http://vision.middlebury.edu/mview/)

Saliency Detection

Visual Tracking
 ⟡ Visual Tracker Benchmark (https://sites.google.com/site/trackerbenchmark/benchmarks/v10)
 ⟡ Visual Tracker Benchmark v1.1 (https://sites.google.com/site/benchmarkpami/)
 ⟡ VOT Challenge (http://www.votchallenge.net/)
 ⟡ Princeton Tracking Benchmark (http://tracking.cs.princeton.edu/)
 ⟡ Tracking Manipulation Tasks (TMT) (http://webdocs.cs.ualberta.ca/~vis/trackDB/)

Visual Surveillance
 ⟡ VIRAT (http://www.viratdata.org/)
 ⟡ CAM2 (https://cam2.ecn.purdue.edu/)

Saliency Detection

Change detection
 ⟡ ChangeDetection.net (http://changedetection.net/)

Visual Recognition

Image Classification
 ⟡ The PASCAL Visual Object Classes (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)
 ⟡ ImageNet Large Scale Visual Recognition Challenge (http://www.image-net.org/challenges/LSVRC/2014/)

Self-supervised Learning
⟡ PASS: An An ImageNet replacement for self-supervised pretraining without humans (https://github.com/yukimasano/PASS)

Scene Recognition
 ⟡ SUN Database (http://groups.csail.mit.edu/vision/SUN/)
 ⟡ Place Dataset (http://places.csail.mit.edu/)

Object Detection
 ⟡ The PASCAL Visual Object Classes (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)
 ⟡ ImageNet Object Detection Challenge (http://www.image-net.org/challenges/LSVRC/2014/)
 ⟡ Microsoft COCO (http://mscoco.org/)

Semantic labeling
 ⟡ Stanford background dataset (http://dags.stanford.edu/projects/scenedataset.html)
 ⟡ CamVid (http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/)
 ⟡ Barcelona Dataset (http://www.cs.unc.edu/~jtighe/Papers/ECCV10/)
 ⟡ SIFT Flow Dataset (http://www.cs.unc.edu/~jtighe/Papers/ECCV10/siftflow/SiftFlowDataset.zip)

Multi-view Object Detection
 ⟡ 3D Object Dataset (http://cvgl.stanford.edu/resources.html)
 ⟡ EPFL Car Dataset (http://cvlab.epfl.ch/data/pose)
 ⟡ KTTI Dection Dataset (http://www.cvlibs.net/datasets/kitti/eval_object.php)
 ⟡ SUN 3D Dataset (http://sun3d.cs.princeton.edu/)
 ⟡ PASCAL 3D+ (http://cvgl.stanford.edu/projects/pascal3d.html)
 ⟡ NYU Car Dataset (http://nyc3d.cs.cornell.edu/)

Fine-grained Visual Recognition
 ⟡ Fine-grained Classification Challenge (https://sites.google.com/site/fgcomp2013/)
 ⟡ Caltech-UCSD Birds 200 (http://www.vision.caltech.edu/visipedia/CUB-200.html)

Pedestrian Detection
 ⟡ Caltech Pedestrian Detection Benchmark (http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
 ⟡ ETHZ Pedestrian Detection (https://data.vision.ee.ethz.ch/cvl/aess/dataset/)

Action Recognition

Image-based

Video-based
 ⟡ HOLLYWOOD2 Dataset (http://www.di.ens.fr/~laptev/actions/hollywood2/)
 ⟡ UCF Sports Action Data Set (http://crcv.ucf.edu/data/UCF_Sports_Action.php)

Image Deblurring
 ⟡ Sun dataset (http://cs.brown.edu/~lbsun/deblur2013/deblur2013iccp.html)
 ⟡ Levin dataset (http://www.wisdom.weizmann.ac.il/~levina/papers/LevinEtalCVPR09Data.rar)

Image Captioning
 ⟡ Flickr 8K (http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html)
 ⟡ Flickr 30K (http://shannon.cs.illinois.edu/DenotationGraph/)
 ⟡ Microsoft COCO (http://mscoco.org/)

Scene Understanding
 # SUN RGB-D (http://rgbd.cs.princeton.edu/) - A RGB-D Scene Understanding Benchmark Suite
 # NYU depth v2 (http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) - Indoor Segmentation and Support Inference from RGBD Images

Aerial images
 # Aerial Image Segmentation (https://zenodo.org/record/1154821#.WmN9kHWnHIp) - Learning Aerial Image Segmentation From Online Maps


Resources for students

Resource link collection
 ⟡ Resources for students (http://people.csail.mit.edu/fredo/student.html) - Frédo Durand (MIT)
 ⟡ Advice for Graduate Students (http://www.dgp.toronto.edu/~hertzman/advice/) - Aaron Hertzmann (Adobe Research)
 ⟡ Graduate Skills Seminars (http://www.dgp.toronto.edu/~hertzman/courses/gradSkills/2010/) - Yashar Ganjali, Aaron Hertzmann (University of Toronto)
 ⟡ Research Skills (http://research.microsoft.com/en-us/um/people/simonpj/papers/giving-a-talk/giving-a-talk.htm) - Simon Peyton Jones (Microsoft Research)
 ⟡ Resource collection (http://web.engr.illinois.edu/~taoxie/advice.htm) - Tao Xie (UIUC) and Yuan Xie (UCSB)

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


Presentation
 ⟡ Giving a Research Talk (http://people.csail.mit.edu/fredo/TalkAdvice.pdf) - Frédo Durand (MIT)
 ⟡ How to give a good talk (http://www.dgp.toronto.edu/~hertzman/courses/gradSkills/2010/GivingGoodTalks.pdf) - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
 ⟡ Designing conference posters (http://colinpurrington.com/tips/poster-design) - Colin Purrington

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

Time Management
 ⟡ Time Management (https://www.youtube.com/watch?v=oTugjssqOT0) - Randy Pausch (CMU)

Blogs
 ⟡ Learn OpenCV (http://www.learnopencv.com/) - Satya Mallick
 ⟡ Tombone's Computer Vision Blog (http://www.computervisionblog.com/) - Tomasz Malisiewicz
 ⟡ Computer vision for dummies (http://www.visiondummy.com/) - Vincent Spruyt
 ⟡ Andrej Karpathy blog (http://karpathy.github.io/) - Andrej Karpathy
 ⟡ AI Shack (http://aishack.in/) - Utkarsh Sinha
 ⟡ Computer Vision Talks (http://computer-vision-talks.com/) - Eugene Khvedchenya
 ⟡ Computer Vision Basics with Python Keras and OpenCV (https://github.com/jrobchin/Computer-Vision-Basics-with-Python-Keras-and-OpenCV) - Jason Chin (University of Western Ontario)


Links
⟡ The Computer Vision Industry (http://www.cs.ubc.ca/~lowe/vision.html) - David Lowe
⟡ German Computer Vision Research Groups & Companies (http://hci.iwr.uni-heidelberg.de/Links/German_Vision/)
⟡ awesome-deep-learning (https://github.com/ChristosChristofidis/awesome-deep-learning)
⟡ awesome-machine-learning (https://github.com/josephmisiti/awesome-machine-learning)
⟡ Cat Paper Collection (http://www.eecs.berkeley.edu/~junyanz/cat/cat_papers.html)
⟡ Computer Vision News (http://www.rsipvision.com/computer-vision-news/)
*
Songs
⟡ The Fundamental Matrix Song (http://danielwedge.com/fmatrix/)
⟡ The RANSAC Song (http://danielwedge.com/ransac/)
⟡ Machine Learning A Cappella - Overfitting Thriller (https://www.youtube.com/watch?v=DQWI1kvmwRg)

Licenses
License

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To the extent possible under law, Jia-Bin Huang (www.jiabinhuang.com) has waived all copyright and related or neighboring rights to this work.