Awesome

Awesome People in Computer Vision

Please feel free to send pull requests to add new links or correct wrong ones.

Sources:

• Computer Vision Genealogy Project - Jia-Bin Huang and Wen-Sheng Chu
• The Mathematics Geneology Project

==

• Takeo Kanade (CMU)

• Edward Adelson (MIT)

  • William T. Freeman (MIT)
    • Hyun Sung Chang
    • Taeg Sang ("Tim") Cho (Wilmer Hale)
    • Roger Grosse (University of Toronto)
    • Biliana Kaneva (MIT)
    • Ce Liu (Google Research)
    • Michael Rubinstein (Google Research)
    • Bryan Russell (Adobe Research)
    • Barun Singh
    • Erik B. Sudderth (Brown University)
    • Marshall Tappen (Amazon)
  • Eero Simoncelli (NYU)
  • Yair Weiss (The Hebrew University of Jerusalem)
    • Anat Levin (The Weizmann Institute of Science)
  • Ron Dror (Stanford University)
  • Roland Fleming (University of Giessen)
  • Marshall Tappen (Amazon)
  • Josh McDermott (MIT)
  • Yuanzhen Li
  • Lavanya Sharan (MIT)
  • Ce Liu (Google Research)
  • Nathaniel Twarog

• Thomas Binford (Stanford University)

  • Rodney Brooks (MIT)
  • David Lowe (University of British Columbia)
  • David Kriegman (UCSD)
  • Jitendra Malik (UC Berkeley)
    • Paul Kube (UCSD)
    • Pietro Perona (Caltech)
    • Clark F. Olson (University of Washington, Bothell)
    • Ruth Rosenholtz (MIT)
    • Paul Debevec (USC)
    • Christoph Bregler (Facebook/Oculus)
    • Jianbo Shi (University of Pennsylvania)
    • Yizhou Yu (The University of Hong Kong)
    • Serge J. Belongie (Cornell Tech)
      • Piotr Dollár (Facebook AI Research)
      • Tsung-Yi Lin - (Cornell Tech)
    • Alexei A. Efros (UC Berkeley)
      • Derek Hoiem (UIUC)
      • James Hays (Brown University)
      • Jean-François Lalonde (Laval University)
      • Tomasz Malisiewicz (VISION.AI, LLC)
      • Santosh Divvala (Allen Institute for Artificial Intelligence)
    • Greg Mori (Simon Fraser University)
    • Charless C. Fowlkes (UC Irvine)
    • Arie E. Kaufman (Stonybrook University)
    • Xiaofeng Ren (Amazon)
    • Subhransu Maji (University of Massachusetts, Amherst)
    • Lubomir Bourdev (Facebook AI Research)
  • Michael Kass (Pixar Research Group)
  • Sheng-Jyh Wang (National Chiao Tung University)

• Azriel Rosenfeld (University of Maryland)

  • Mohamed Abdel-Mottaleb (University of Miami)
  • Narendra Ahuja (UIUC)
    • Ming-Hsuan Yang (UC Merced)
  • Rama Chellappa (University of Maryland)
  • Larry S. Davis (University of Maryland)
  • Sven Dickinson (University of Toronto)
  • David S. Doermann (University of Maryland)
  • Zoran Duric (George Mason University)
  • Charles R. Dyer (University of Wisconsin - Madison)
    • Steven Seitz (University of Washington)
      • Noah Snavely (Cornell University/Google)
      • Li Zhang (Google)
      • Jovan Popović (Adobe Research)
      • Daniel Goldman (Adobe)
  • Jan-Olof Eklundh (KTH - Royal Institute of Technology)
  • Shmuel Peleg (The Hebrew University of Jerusalem)
    • Haim Schweitzer (The University of Texas at Dallas)
    • Michael Werman (The Hebrew University of Jerusalem)
    • Daniel Keren (Haifa University)
    • Yacov Hel-Or (The Interdisciplinary Center)
    • Michal Irani (The Weizmann Institute of Science)
      • Eli Shechtman (Adobe Research)
    • Hagit Zabrodsky (Hel-Or) (University of Haifa)
    • Moshe Ben-Ezra (MIT Media Lab)
    • Yael Pritch (Google [X])
  • Matti Pietikäinen (University of Oulu)
  • Zygmunt Pizlo (Purdue University)

• Antonio Torralba (MIT)

• Berthold K.P. Horn (MIT)

• Eric Grimson (MIT)

• Frédo Durand (MIT)

• Olivier Faugeras (INRIA Sophia Antipolis)

  • Zhengyou Zhang (Microsoft Research)
  • Pascal Fua (EPFL)
  • Martial Hebert (CMU)
    • Andrew Johnson (JPL)
  • Michael Leventon (D. E. Shaw & Co.)
  • Jonathan Touboul (Collège de France and INRIA Paris-Rocquencourt)
  • Jean Ponce (Ecole Normale Supérieure)
    • Yasutaka Furukawa (Washington University in St. Louis)
    • Oliver Whyte (Microsoft)
    • Y-Lan Boureau (NYU)
    • Olivier Duchenne (Inter Korea)
    • Armand Joulin (Facebook AI Research)
    • Svetlana Lazebnik (UIUC)
      • Joseph Tighe (Amazon)
      • Yunchao Gong (Facebook AI Research)

• Jan J. Koenderink (Delft University of Technology and Katholieke Universiteit Leuven)

• David Mumford (Harvard University)

  • Peter N. Belhumeur (Columbia University/Dropbox)
    • Todd Zickler (Harvard University)
      • Ayan Chakrabarti (Toyota Technological Institute at Chicago)
      • Sanjeev Koppal (University of Florida)
    • Neeraj Kumar (Dropbox)

Alex Pentland (MIT)

• Trevor Darrell (UC Berkeley)
  • Gregory Shakhnarovich (Toyota Technological Institute at Chicago)
  • Kristen Grauman (UT Austin)
    • Adriana Kovashka (University of Pittsburgh)
    • Sung Ju Hwang (Ulsan National Institute of Science and Technology)
    • Jaechul Kim (Amazon Research)
    • Yong Jae Lee (UC Davis)
    • Sudheendra Vijayanarasimhan (Google Research)
  • Kate Saenko (UMass Lowell)
• Irfan Essa (Georgia Institute of Technology)
• Tony Jebara (Columbia University)
• Baback Moghaddam (JPL)
• Rosalind Picard (MIT Media Lab)
• Stanley Sclaroff (Boston University)
• Thad Starner (Georgia Institute of Technology)
• Matthew Turk (University of California, Santa Barbara)

Marc Levoy (Google X)

• Vaibhav Vaish
• David Koller
• Billy Chen
• Gaurav Garg
• Natasha Gelfand (Nokia Research)
• Augusto Roman (Flux Factory)
• Li-Yi Wei (The University of Hong Kong)
• Szymon Rusinkeiwicz (Princeton University)
• Brian Curless (University of Washington)
  • Li Zhang (Google)
  • Yung-Yu Chuang (National Taiwan University)
  • Daniel Goldman (Adobe)
• Philippe Lacroute (Stanford University)

• Awesome Computer Vision:

A curated list of awesome computer vision resources, inspired by awesome-php.

For a list people in computer vision listed with their academic genealogy, please visit here

Contributing

Please feel free to send me pull requests or email (jbhuang1@illinois.edu) to add links.

Table of Contents

• Books
• Courses
• Papers
• Software
• Datasets
• Tutorials and Talks
• Resources for students
• Blogs
• Links
• Songs

Books

Computer Vision

• Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012
• Computer Vision: Theory and Application - Rick Szeliski 2010
• Computer Vision: A Modern Approach (2nd edition) - David Forsyth and Jean Ponce 2011
• Multiple View Geometry in Computer Vision - Richard Hartley and Andrew Zisserman 2004
• Computer Vision - Linda G. Shapiro 2001
• Vision Science: Photons to Phenomenology - Stephen E. Palmer 1999
• Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
• Computer Vision for Visual Effects - Richard J. Radke, 2012
• High dynamic range imaging: acquisition, display, and image-based lighting - Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010

OpenCV Programming

• Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler
• Practical Python and OpenCV - Adrian Rosebrock
• 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 - Christopher M. Bishop 2007
• Neural Networks for Pattern Recognition - Christopher M. Bishop 1995
• Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman 2009
• Pattern Classification - Peter E. Hart, David G. Stork, and Richard O. Duda 2000
• Machine Learning - Tom M. Mitchell 1997
• Gaussian processes for machine learning - Carl Edward Rasmussen and Christopher K. I. Williams 2005
• Learning From Data- Yaser S. Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin 2012
• Neural Networks and Deep Learning - Michael Nielsen 2014
• Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012

Fundamentals

• Linear Algebra and Its Applications - Gilbert Strang 1995

Courses

Computer Vision

• EENG 512 / CSCI 512 - Computer Vision - William Hoff (Colorado School of Mines)
• Visual Object and Activity Recognition - Alexei A. Efros and Trevor Darrell (UC Berkeley)
• Computer Vision - Steve Seitz (University of Washington)
• Visual Recognition - Kristen Grauman (UT Austin)
• Language and Vision - Tamara Berg (UNC Chapel Hill)
• Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
• Computer Vision - Rob Fergus (NYU)
• Computer Vision - Derek Hoiem (UIUC)
• Computer Vision: Foundations and Applications - Kalanit Grill-Spector and Fei-Fei Li (Stanford University)
• High-Level Vision: Behaviors, Neurons and Computational Models - Fei-Fei Li (Stanford University)
• Advances in Computer Vision - Antonio Torralba and Bill Freeman (MIT)
• Computer Vision - Bastian Leibe (RWTH Aachen University)
• Computer Vision 2 - Bastian Leibe (RWTH Aachen University)

Computational Photography

• Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
• Computational Photography - Alexei A. Efros (CMU)
• Computational Photography - Derek Hoiem (UIUC)
• Computational Photography - James Hays (Brown University)
• Digital & Computational Photography - Fredo Durand (MIT)
• Computational Camera and Photography - Ramesh Raskar (MIT Media Lab)
• Computational Photography - Irfan Essa (Georgia Tech)
• Courses in Graphics - Stanford University
• Computational Photography - Rob Fergus (NYU)
• Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
• Computational Photography - Kyros Kutulakos (University of Toronto)
• Computer Vision for Visual Effects - Rich Radke (Rensselaer Polytechnic Institute)
• Introduction to Image Processing - Rich Radke (Rensselaer Polytechnic Institute)

Machine Learning and Statistical Learning

• Machine Learning - Andrew Ng (Stanford University)
• Learning from Data - Yaser S. Abu-Mostafa (Caltech)
• Statistical Learning - Trevor Hastie and Rob Tibshirani (Stanford University)
• Statistical Learning Theory and Applications - Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT)
• Statistical Learning - Genevera Allen (Rice University)
• Practical Machine Learning - Michael Jordan (UC Berkeley)
• Course on Information Theory, Pattern Recognition, and Neural Networks - David MacKay (University of Cambridge)
• Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
• Machine Learning - Andrew Zisserman (University of Oxford)

Optimization

• Convex Optimization I - Stephen Boyd (Stanford University)
• Convex Optimization II - Stephen Boyd (Stanford University)
• Convex Optimization - Stephen Boyd (Stanford University)
• Optimization at MIT - (MIT)
• Convex Optimization - Ryan Tibshirani (CMU)

Papers

Conference papers on the web

• CVPapers - Computer vision papers on the web
• SIGGRAPH Paper on the web - Graphics papers on the web
• NIPS Proceedings - NIPS papers on the web
• Computer Vision Foundation open access
• Annotated Computer Vision Bibliography - Keith Price (USC)
• Calendar of Computer Image Analysis, Computer Vision Conferences - (USC)

Survey Papers

• Visionbib Survey Paper List
• Foundations and Trends® in Computer Graphics and Vision
• Computer Vision: A Reference Guide

Tutorials and talks

Computer Vision

• Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
• The Three R's of Computer Vision - Jitendra Malik (UC Berkeley) 2013
• Applications to Machine Vision - Andrew Blake (Microsoft Research) 2008
• The Future of Image Search - Jitendra Malik (UC Berkeley) 2008
• Should I do a PhD in Computer Vision? - Fatih Porikli (Australian National University)

• Graduate Summer School 2013: Computer Vision - IPAM, 2013

Recent Conference Talks

• CVPR 2015 - Jun 2015
• ECCV 2014 - Sep 2014
• CVPR 2014 - Jun 2014
• ICCV 2013 - Dec 2013
• ICML 2013 - Jul 2013
• CVPR 2013 - Jun 2013
• ECCV 2012 - Oct 2012
• ICML 2012 - Jun 2012
• CVPR 2012 - Jun 2012

3D Computer Vision

• 3D Computer Vision: Past, Present, and Future - Steve Seitz (University of Washington) 2011
• Reconstructing the World from Photos on the Internet - Steve Seitz (University of Washington) 2013

Internet Vision

• The Distributed Camera - Noah Snavely (Cornell University) 2011
• Planet-Scale Visual Understanding - Noah Snavely (Cornell University) 2014
• A Trillion Photos - Steve Seitz (University of Washington) 2013

Computational Photography

• Reflections on Image-Based Modeling and Rendering - Richard Szeliski (Microsoft Research) 2013
• Photographing Events over Time - William T. Freeman (MIT) 2011
• Old and New algorithm for Blind Deconvolution - Yair Weiss (The Hebrew University of Jerusalem) 2011
• A Tour of Modern "Image Processing" - Peyman Milanfar (UC Santa Cruz/Google) 2010
• Topics in image and video processing Andrew Blake (Microsoft Research) 2007
• Computational Photography - William T. Freeman (MIT) 2012
• Revealing the Invisible - Frédo Durand (MIT) 2012
• Overview of Computer Vision and Visual Effects - Rich Radke (Rensselaer Polytechnic Institute) 2014

Learning and Vision

• Where machine vision needs help from machine learning - William T. Freeman (MIT) 2011
• Learning in Computer Vision - Simon Lucey (CMU) 2008
• Learning and Inference in Low-Level Vision - Yair Weiss (The Hebrew University of Jerusalem) 2009

Object Recognition

• Object Recognition - Larry Zitnick (Microsoft Research)
• Generative Models for Visual Objects and Object Recognition via Bayesian Inference - Fei-Fei Li (Stanford University)

Graphical Models

• Graphical Models for Computer Vision - Pedro Felzenszwalb (Brown University) 2012
• Graphical Models - Zoubin Ghahramani (University of Cambridge) 2009
• Machine Learning, Probability and Graphical Models - Sam Roweis (NYU) 2006
• Graphical Models and Applications - Yair Weiss (The Hebrew University of Jerusalem) 2009

Machine Learning

• A Gentle Tutorial of the EM Algorithm - Jeff A. Bilmes (UC Berkeley) 1998
• Introduction To Bayesian Inference - Christopher Bishop (Microsoft Research) 2009
• Support Vector Machines - Chih-Jen Lin (National Taiwan University) 2006
• Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)

Optimization

• Optimization Algorithms in Machine Learning - Stephen J. Wright (University of Wisconsin-Madison)
• Convex Optimization - Lieven Vandenberghe (University of California, Los Angeles)
• Continuous Optimization in Computer Vision - Andrew Fitzgibbon (Microsoft Research)
• Beyond stochastic gradient descent for large-scale machine learning - Francis Bach (INRIA)
• Variational Methods for Computer Vision - Daniel Cremers (Technische Universität München) (lecture 18 missing from playlist)

Deep Learning

• A tutorial on Deep Learning - Geoffrey E. Hinton (University of Toronto)
• Deep Learning - Ruslan Salakhutdinov (University of Toronto)
• Scaling up Deep Learning - Yoshua Bengio (University of Montreal)
• ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky (University of Toronto)
• The Unreasonable Effectivness Of Deep Learning Yann LeCun (NYU/Facebook Research) 2014
• Deep Learning for Computer Vision - Rob Fergus (NYU/Facebook Research)
• High-dimensional learning with deep network contractions - Stéphane Mallat (Ecole Normale Superieure)
• Graduate Summer School 2012: Deep Learning, Feature Learning - IPAM, 2012
• Workshop on Big Data and Statistical Machine Learning
• Machine Learning Summer School - Reykjavik, Iceland 2014
  • Deep Learning Session 1 - Yoshua Bengio (Universtiy of Montreal)
  • Deep Learning Session 2 - Yoshua Bengio (University of Montreal)
  • Deep Learning Session 3 - Yoshua Bengio (University of Montreal)

Software

External Resource Links

• Computer Vision Resources - Jia-Bin Huang (UIUC)
• Computer Vision Algorithm Implementations - CVPapers
• Source Code Collection for Reproducible Research - Xin Li (West Virginia University)
• CMU Computer Vision Page

General Purpose Computer Vision Library

• Open CV
• mexopencv
• SimpleCV
• Open source Python module for computer vision
• ccv: A Modern Computer Vision Library
• VLFeat
• Matlab Computer Vision System Toolbox
• Piotr's Computer Vision Matlab Toolbox
• PCL: Point Cloud Library
• ImageUtilities

Multiple-view Computer Vision

• MATLAB Functions for Multiple View Geometry
• Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
• OpenGV - geometric computer vision algorithms
• MinimalSolvers - Minimal problems solver
• Multi-View Environment
• Visual SFM
• Bundler SFM
• openMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwares
• Patch-based Multi-view Stereo V2
• Clustering Views for Multi-view Stereo
• Floating Scale Surface Reconstruction
• Large-Scale Texturing of 3D Reconstructions

Feature Detection and Extraction

• VLFeat
• SIFT
  • David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
• SIFT++
• BRISK
  • Stefan Leutenegger, Margarita Chli and Roland Siegwart, "BRISK: Binary Robust Invariant Scalable Keypoints", ICCV 2011
• 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
  • A. Alahi, R. Ortiz, and P. Vandergheynst, "FREAK: Fast Retina Keypoint", CVPR 2012
• AKAZE
  • Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", ECCV 2012
• Local Binary Patterns

High Dynamic Range Imaging

• HDR_Toolbox

Low-level Vision

Stereo Vision

• Middlebury Stereo Vision
• The KITTI Vision Benchmark Suite
• LIBELAS: Library for Efficient Large-scale Stereo Matching
• Ground Truth Stixel Dataset

Optical Flow

• Middlebury Optical Flow Evaluation
• MPI-Sintel Optical Flow Dataset and Evaluation
• The KITTI Vision Benchmark Suite
• HCI Challenge
• Coarse2Fine Optical Flow - Ce Liu (MIT)
• Secrets of Optical Flow Estimation and Their Principles
• C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)
• Parallel Robust Optical Flow by Sánchez Pérez et al.

Image Denoising

BM3D, KSVD,

Super-resolution

• Multi-frame image super-resolution
  • Pickup, L. C. Machine Learning in Multi-frame Image Super-resolution, PhD thesis 2008
• Markov Random Fields for Super-Resolution
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Yu Zhu, Yanning Zhang and Alan Yuille, Single Image Super-resolution using Deformable Patches, CVPR 2014
• SRCNN
  • 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
  • R. Timofte, V. De Smet, and L. Van Gool. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution, ACCV 2014
• Transformed Self-Exemplars
  • 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
• Handling Outliers in Non-blind Image Deconvolution
• Hyper-Laplacian Priors
• From Learning Models of Natural Image Patches to Whole Image Restoration
• Deep Convolutional Neural Network for Image Deconvolution
• Neural Deconvolution

Blind deconvolution

• Removing Camera Shake From A Single Photograph
• High-quality motion deblurring from a single image
• Two-Phase Kernel Estimation for Robust Motion Deblurring
• Blur kernel estimation using the radon transform
• Fast motion deblurring
• Blind Deconvolution Using a Normalized Sparsity Measure
• Blur-kernel estimation from spectral irregularities
• Efficient marginal likelihood optimization in blind deconvolution
• Unnatural L0 Sparse Representation for Natural Image Deblurring
• Edge-based Blur Kernel Estimation Using Patch Priors
• Blind Deblurring Using Internal Patch Recurrence

Non-uniform Deblurring

• Non-uniform Deblurring for Shaken Images
• Single Image Deblurring Using Motion Density Functions
• Image Deblurring using Inertial Measurement Sensors
• Fast Removal of Non-uniform Camera Shake

Image Completion

• GIMP Resynthesizer
• Priority BP
• ImageMelding
• PlanarStructureCompletion

Image Retargeting

• RetargetMe

Alpha Matting

• Alpha Matting Evaluation
• Closed-form image matting
• Spectral Matting
• Learning-based Matting
• Improving Image Matting using Comprehensive Sampling Sets

Image Pyramid

• The Steerable Pyramid
• CurveLab

Edge-preserving image processing

• Fast Bilateral Filter
• O(1) Bilateral Filter
• Recursive Bilateral Filtering
• Rolling Guidance Filter
• Relative Total Variation
• L0 Gradient Optimization
• Domain Transform
• Adaptive Manifold
• Guided image filtering

Intrinsic Images

• Recovering Intrinsic Images with a global Sparsity Prior on Reflectance
• Intrinsic Images by Clustering

Contour Detection and Image Segmentation

• Mean Shift Segmentation
• Graph-based Segmentation
• Normalized Cut
• Grab Cut
• Contour Detection and Image Segmentation
• Structured Edge Detection
• Pointwise Mutual Information
• SLIC Super-pixel
• QuickShift
• TurboPixels
• Entropy Rate Superpixel
• Contour Relaxed Superpixels
• SEEDS
• SEEDS Revised
• Multiscale Combinatorial Grouping
• Fast Edge Detection Using Structured Forests

Interactive Image Segmentation

• Random Walker
• Geodesic Segmentation
• Lazy Snapping
• Power Watershed
• Geodesic Graph Cut
• Segmentation by Transduction

Video Segmentation

• Video Segmentation with Superpixels
• Efficient hierarchical graph-based video segmentation
• Object segmentation in video
• Streaming hierarchical video segmentation

Camera calibration

• Camera Calibration Toolbox for Matlab
• Camera calibration With OpenCV
• Multiple Camera Calibration Toolbox

Simultaneous localization and mapping

SLAM community:

• openSLAM
• Kitti Odometry: benchmark for outdoor visual odometry (codes may be available)

Tracking/Odometry:

• LIBVISO2: C++ Library for Visual Odometry 2
• PTAM: Parallel tracking and mapping
• KFusion: Implementation of KinectFusion
• kinfu_remake: Lightweight, reworked and optimized version of Kinfu.
• LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction
• InfiniTAM: Implementation of multi-platform large-scale depth tracking and fusion
• VoxelHashing: Large-scale KinectFusion
• SLAMBench: Multiple-implementation of KinectFusion
• SVO: Semi-direct visual odometry
• DVO: dense visual odometry
• FOVIS: RGB-D visual odometry

Graph Optimization:

• GTSAM: General smoothing and mapping library for Robotics and SFM -- Georgia Institute of Technology
• G2O: General framework for graph optomization

Loop Closure:

• FabMap: appearance-based loop closure system - also available in OpenCV2.4.11
• DBoW2: binary bag-of-words loop detection system

Localization & Mapping:

• RatSLAM
• LSD-SLAM
• ORB-SLAM

Single-view Spatial Understanding

• Geometric Context - Derek Hoiem (CMU)
• Recovering Spatial Layout - Varsha Hedau (UIUC)
• Geometric Reasoning - David C. Lee (CMU)
• RGBD2Full3D - Ruiqi Guo (UIUC)

Object Detection

• INRIA Object Detection and Localization Toolkit
• Discriminatively trained deformable part models
• VOC-DPM
• Histograms of Sparse Codes for Object Detection
• R-CNN: Regions with Convolutional Neural Network Features
• SPP-Net
• BING: Objectness Estimation
• Edge Boxes
• ReInspect

Nearest Neighbor Search

General purpose nearest neighbor search

• ANN: A Library for Approximate Nearest Neighbor Searching
• FLANN - Fast Library for Approximate Nearest Neighbors
• Fast k nearest neighbor search using GPU

Nearest Neighbor Field Estimation

• PatchMatch
• Generalized PatchMatch
• Coherency Sensitive Hashing
• PMBP: PatchMatch Belief Propagation
• TreeCANN

Visual Tracking

• Visual Tracker Benchmark
• Visual Tracking Challenge
• Kanade-Lucas-Tomasi Feature Tracker
• Extended Lucas-Kanade Tracking
• Online-boosting Tracking
• Spatio-Temporal Context Learning
• Locality Sensitive Histograms
• Enhanced adaptive coupled-layer LGTracker++
• TLD: Tracking - Learning - Detection
• CMT: Clustering of Static-Adaptive Correspondences for Deformable Object Tracking
• Kernelized Correlation Filters
• Accurate Scale Estimation for Robust Visual Tracking
• Multiple Experts using Entropy Minimization
• TGPR
• CF2: Hierarchical Convolutional Features for Visual Tracking

Saliency Detection

Attributes

Action Reconition

Egocentric cameras

Human-in-the-loop systems

Image Captioning

• NeuralTalk -

Optimization

• Ceres Solver - Nonlinear least-square problem and unconstrained optimization solver
• NLopt- Nonlinear least-square problem and unconstrained optimization solver
• OpenGM - Factor graph based discrete optimization and inference solver
• GTSAM - Factor graph based lease-square optimization solver

Deep Learning

• Awesome Deep Vision

Machine Learning

• Awesome Machine Learning
• Bob: a free signal processing and machine learning toolbox for researchers
• LIBSVM -- A Library for Support Vector Machines

Datasets

External Dataset Link Collection

• CV Datasets on the web - CVPapers
• Are we there yet? - Which paper provides the best results on standard dataset X?
• Computer Vision Dataset on the web
• Yet Another Computer Vision Index To Datasets
• ComputerVisionOnline Datasets
• CVOnline Dataset
• CV datasets
• visionbib

Low-level Vision

Stereo Vision

• Middlebury Stereo Vision
• The KITTI Vision Benchmark Suite
• LIBELAS: Library for Efficient Large-scale Stereo Matching
• Ground Truth Stixel Dataset

Optical Flow

• Middlebury Optical Flow Evaluation
• MPI-Sintel Optical Flow Dataset and Evaluation
• The KITTI Vision Benchmark Suite
• HCI Challenge

Image Super-resolutions

• Single-Image Super-Resolution: A Benchmark

Intrinsic Images

• Ground-truth dataset and baseline evaluations for intrinsic image algorithms
• Intrinsic Images in the Wild
• Intrinsic Image Evaluation on Synthetic Complex Scenes

Material Recognition

• OpenSurface
• Flickr Material Database
• Materials in Context Dataset

Multi-view Reconsturction

• Multi-View Stereo Reconstruction

Saliency Detection

Visual Tracking

• Visual Tracker Benchmark
• Visual Tracker Benchmark v1.1
• VOT Challenge
• Princeton Tracking Benchmark

Visual Surveillance

• VIRAT
• CAM2

Saliency Detection

Change detection

• ChangeDetection.net

Visual Recognition

Image Classification

• The PASCAL Visual Object Classes
• ImageNet Large Scale Visual Recognition Challenge

Scene Recognition

• SUN Database
• Place Dataset

Object Detection

• The PASCAL Visual Object Classes
• ImageNet Object Detection Challenge
• Microsoft COCO

Semantic labeling

• Stanford background dataset
• CamVid
• Barcelona Dataset
• SIFT Flow Dataset

Multi-view Object Detection

• 3D Object Dataset
• EPFL Car Dataset
• KTTI Dection Dataset
• SUN 3D Dataset
• PASCAL 3D+
• NYU Car Dataset

Fine-grained Visual Recognition

• Fine-grained Classification Challenge
• Caltech-UCSD Birds 200

Pedestrian Detection

• Caltech Pedestrian Detection Benchmark
• ETHZ Pedestrian Detection

Action Recognition

Image-based

Video-based

• HOLLYWOOD2 Dataset
• UCF Sports Action Data Set

Image Deblurring

• Sun dataset
• Levin dataset

Image Captioning

• Flickr 8K
• Flickr 30K
• Microsoft COCO

Scene Understanding

SUN RGB-D - A RGB-D Scene Understanding Benchmark Suite

NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images

Resources for students

Resource link collection

• Resources for students - Frédo Durand (MIT)
• Advice for Graduate Students - Aaron Hertzmann (Adobe Research)
• Graduate Skills Seminars - Yashar Ganjali, Aaron Hertzmann (University of Toronto)
• Research Skills - Simon Peyton Jones (Microsoft Research)
• Resource collection - Tao Xie (UIUC) and Yuan Xie (UCSB)

Writing

• Write Good Papers - Frédo Durand (MIT)
• Notes on writing - Frédo Durand (MIT)
• How to Write a Bad Article - Frédo Durand (MIT)
• How to write a good CVPR submission - William T. Freeman (MIT)
• How to write a great research paper - Simon Peyton Jones (Microsoft Research)
• How to write a SIGGRAPH paper - SIGGRAPH ASIA 2011 Course
• Writing Research Papers - Aaron Hertzmann (Adobe Research)
• How to Write a Paper for SIGGRAPH - Jim Blinn
• How to Get Your SIGGRAPH Paper Rejected - Jim Kajiya (Microsoft Research)
• How to write a SIGGRAPH paper - Li-Yi Wei (The University of Hong Kong)
• How to Write a Great Paper - Martin Martin Hering Hering--Bertram (Hochschule Bremen University of Applied Sciences)
• How to have a paper get into SIGGRAPH? - Takeo Igarashi (The University of Tokyo)
• Good Writing - Marc H. Raibert (Boston Dynamics, Inc.)
• How to Write a Computer Vision Paper - Derek Hoiem (UIUC)
• Common mistakes in technical writing - Wojciech Jarosz (Dartmouth College)

Presentation

• Giving a Research Talk - Frédo Durand (MIT)
• How to give a good talk - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
• Designing conference posters - Colin Purrington

Research

• How to do research - William T. Freeman (MIT)
• You and Your Research - Richard Hamming
• Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information - Yi Ma (UIUC)
• Seven Warning Signs of Bogus Science - Robert L. Park
• Five Principles for Choosing Research Problems in Computer Graphics - Thomas Funkhouser (Cornell University)
• How To Do Research In the MIT AI Lab - David Chapman (MIT)
• Recent Advances in Computer Vision - Ming-Hsuan Yang (UC Merced)
• How to Come Up with Research Ideas in Computer Vision? - Jia-Bin Huang (UIUC)
• How to Read Academic Papers - Jia-Bin Huang (UIUC)

Time Management

• Time Management - Randy Pausch (CMU)

Blogs

• Learn OpenCV - Satya Mallick
• Tombone's Computer Vision Blog - Tomasz Malisiewicz
• Computer vision for dummies - Vincent Spruyt
• Andrej Karpathy blog - Andrej Karpathy
• AI Shack - Utkarsh Sinha
• Computer Vision Talks - Eugene Khvedchenya

Links

• The Computer Vision Industry - David Lowe
• German Computer Vision Research Groups & Companies
• awesome-deep-learning
• awesome-maching-learning
• Cat Paper Collection

Songs

• The Fundamental Matrix Song

• The RANSAC Song

• Machine Learning A Cappella - Overfitting Thriller