Update render script and Makefile

terminal/computervision

@@ -1,5 +1,5 @@
 
-                Awesome Computer Vision: !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
+      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)
@@ -99,12 +99,13 @@
 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
+⟡ 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
+⟡ 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
@@ -264,18 +265,20 @@
  ⟡ 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))
+ ⟡ 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)
+ ⟡ 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
+ ⟡ 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)
@@ -370,19 +373,22 @@
  ⟡ 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 1                  
-0. MIT Press, 2011                                                                                                                                                                                                                   
+* 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 P                                      
+rocessing, 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.
+* 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
+* 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 2569-258            
+2, 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).
+* 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-Scien           
+ce - 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 July 2004.                                                                                                                                                                                                                   
+* 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 July 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)
@@ -485,7 +491,8 @@
  ⟡ 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/)
+ ⟡ 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/)
@@ -519,7 +526,8 @@
  ⟡ 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)
+ ⟡ 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: