A curated list of awesome computer vision resources, inspired by awesome-php.
Please feel free to send me pull requests or email (jbhuang1@illinois.edu) to add links.
- 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
- Linear Algebra and Its Applications - Gilbert Strang 1995
- Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler
- 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] (http://www.gaussianprocess.org/gpml/) - 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
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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
- 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
- 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
- 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 - Larry Zitnick (Microsoft Research)
- Generative Models for Visual Objects and Object Recognition via Bayesian Inference - Fei-Fei Li (Stanford University)
- 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
- 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 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)
- 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)
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Algorithm Implementations - CVPapers
- Source Code Collection for Reproducible Research
- CMU Computer Vision Page
- Open CV
- SimpleCV
- Matlab Computer Vision System Toolbox
- VLFeat
- Piotr's Computer Vision Matlab Toolbox
- http://www.robots.ox.ac.uk/~vgg/hzbook/code/
- Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
- OpenGV - geometric computer vision algorithms
- Patch-based Multi-view Stereo V2
- Clustering Views for Multi-view Stereo
- Multi-View Environment
- Visual SFM
- Bundler SFM
- openMVG: open Multiple View Geometry
- Floating Scale Surface Reconstruction
- Large-Scale Texturing of 3D Reconstructions
- Middlebury Stereo Vision
- The KITTI Vision Benchmark Suite
- Ground Truth Stixel Dataset
- LIBELAS: Library for Efficient Large-scale Stereo Matching
- 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
BM3D, KSVD,
- Multi-frame image super-resolution
- Markov Random Fields for Super-Resolution
- Sparse regression and natural image prior
- Single-Image Super Resolution via a Statistical Model
- Sparse Coding for Super-Resolution
- Patch-wise Sparse Recovery
- Neighbor embedding
- Deformable Patches
- SRCNN
- A+: Adjusted Anchored Neighborhood Regression
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
- Alpha Matting Evaluation
- Closed-form image matting
- Spectral Matting
- Learning-based Matting
- Improving Image Matting using Comprehensive Sampling Sets
- 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
- Mean Shift Segmentation
- Graph-based Segmentation
- Normalized Cut
- Contour Detection and Image Segmentation
- Structured Edge Detection
- Pointwise Mutual Information
- SLIC Super-pixel
- QuickShift
- TurboPixels
- SEEDS
- Video Segmentation with Superpixels
- Efficient hierarchical graph-based video segmentation
- Object segmentation in video
- Streaming hierarchical video segmentation
- Camera Calibration Toolbox for Matlab
- Camera calibration With OpenCV
- Multiple Camera Calibration Toolbox
- Geometric Context - Derek Hoiem (CMU)
- Recovering Spatial Layout - Varsha Hedau (UIUC)
- Geometric Reasoning - David C. Lee (CMU)
- RGBD2Full3D - Ruiqi Guo (UIUC)
- 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
- ANN: A Library for Approximate Nearest Neighbor Searching
- FLANN - Fast Library for Approximate Nearest Neighbors
- Fast k nearest neighbor search using GPU
- PatchMatch
- Generalized PatchMatch
- Coherency Sensitive Hashing
- PMBP: PatchMatch Belief Propagation
- TreeCANN
- Visual Tracker Benchmark
- Visual Tracking Challenge
- Kanade-Lucas-Tomasi Feature Tracker
- Online-boosting Tracking
- Ceres Solver - Nonlinear optimization
- 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: Flow, Stereo, Matting,
- Retargeting
- Super-resolution, deblurring
- Ground-truth dataset and baseline evaluations for intrinsic image algorithms
- Intrinsic Images in the Wild
- 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)
- 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)
- 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
- 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)
- Time Management - Randy Pausch (CMU)
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