awesome-computer-vision

A curated list of awesome computer vision resources.
https://github.com/wpsliu123/awesome-computer-vision

Last synced: 9 days ago
JSON representation

Links
Software
- Awesome Deep Vision
- Ceres Solver - Nonlinear least-square problem and unconstrained optimization solver
- LIBSVM -- A Library for Support Vector Machines
- SimpleCV
- Large-Scale Texturing of 3D Reconstructions
- ANN: A Library for Approximate Nearest Neighbor Searching
- FLANN - Fast Library for Approximate Nearest Neighbors
- Computer Vision Algorithm Implementations - CVPapers
- Source Code Collection for Reproducible Research - Xin Li (West Virginia University)
- CMU Computer Vision Page
- Piotr's Computer Vision Matlab Toolbox
- ImageUtilities
- Visual SFM
- Bundler SFM
- openMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwares
- Floating Scale Surface Reconstruction
- SIFT++
- BRISK
- FREAK
- AKAZE
- C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)
- 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
- Spatially variant non-blind deconvolution
- Handling Outliers in Non-blind Image Deconvolution
- From Learning Models of Natural Image Patches to Whole Image Restoration
- Deep Convolutional Neural Network for Image Deconvolution
- Neural Deconvolution
- 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
- Efficient marginal likelihood optimization in blind deconvolution
- Unnatural L0 Sparse Representation for Natural Image Deblurring
- Single Image Deblurring Using Motion Density Functions
- Image Deblurring using Inertial Measurement Sensors
- Fast Removal of Non-uniform Camera Shake
- GIMP Resynthesizer
- Priority BP
- RetargetMe
- Alpha Matting Evaluation
- Closed-form image matting
- Improving Image Matting using Comprehensive Sampling Sets
- The Steerable Pyramid
- CurveLab
- Fast Bilateral Filter
- O(1) Bilateral Filter
- Recursive Bilateral Filtering
- Rolling Guidance Filter
- Relative Total Variation
- L0 Gradient Optimization
- Guided image filtering
- Recovering Intrinsic Images with a global Sparsity Prior on Reflectance
- Intrinsic Images by Clustering
- Mean Shift Segmentation
- Contour Detection and Image Segmentation
- Structured Edge Detection
- Pointwise Mutual Information
- TurboPixels
- Multiscale Combinatorial Grouping
- Random Walker
- Geodesic Segmentation
- Lazy Snapping
- Video Segmentation with Superpixels
- Camera Calibration Toolbox for Matlab
- Camera calibration With OpenCV
- Multiple Camera Calibration Toolbox
- openSLAM
- LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction
- SLAMBench: Multiple-implementation of KinectFusion
- GTSAM: General smoothing and mapping library for Robotics and SFM - - Georgia Institute of Technology
- DBoW2: binary bag-of-words loop detection system
- Geometric Context - Derek Hoiem (CMU)
- Recovering Spatial Layout - Varsha Hedau (UIUC)
- Geometric Reasoning - David C. Lee (CMU)
- INRIA Object Detection and Localization Toolkit
- Discriminatively trained deformable part models
- Histograms of Sparse Codes for Object Detection
- ReInspect
- Coherency Sensitive Hashing
- TreeCANN
- Extended Lucas-Kanade Tracking
- Online-boosting Tracking
- Spatio-Temporal Context Learning
- Locality Sensitive Histograms
- TLD: Tracking - Learning - Detection
- Kernelized Correlation Filters
- Multiple Experts using Entropy Minimization
- TGPR
- NeuralTalk
- GTSAM - Factor graph based lease-square optimization solver
- Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
- MinimalSolvers - Minimal problems solver
- Multi-View Environment
- List of Semantic Segmentation algorithms
- SLIC Super-pixel
- Contour Relaxed Superpixels
- Modular Tracking Framework
- Graph-based Segmentation
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Unnatural L0 Sparse Representation for Natural Image Deblurring
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Blur-kernel estimation from spectral irregularities
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Alpha Matting Evaluation
- Improving Image Matting using Comprehensive Sampling Sets
- CF2: Hierarchical Convolutional Features for Visual Tracking
- ccv: A Modern Computer Vision Library
- G2O: General framework for graph optomization
- Open source Python module for computer vision
- LSD-SLAM
- Awesome 3D reconstruction list
- ORB-SLAM
- SVO: Semi-direct visual odometry
- DVO: dense visual odometry
- R-CNN: Regions with Convolutional Neural Network Features
- KFusion: Implementation of KinectFusion
- HDR_Toolbox
- Fast Edge Detection Using Structured Forests
- SEEDS Revised
- VOC-DPM
- Open CV
- SimpleCV
- Visual SFM
- openMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwares
- mexopencv
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Algorithm Implementations - CVPapers
- CMU Computer Vision Page
- Matlab Computer Vision System Toolbox
- AKAZE
- Local Binary Patterns
- MPI-Sintel Optical Flow Dataset and Evaluation
- Parallel Robust Optical Flow by Sánchez Pérez et al.
- Deformable Patches
- SRCNN
- Transformed Self-Exemplars
- High-quality motion deblurring from a single image
- Two-Phase Kernel Estimation for Robust Motion Deblurring
- Fast motion deblurring
- Image Deblurring using Inertial Measurement Sensors
- Priority BP
- ImageMelding
- PlanarStructureCompletion
- Alpha Matting Evaluation
- Learning-based Matting
- Improving Image Matting using Comprehensive Sampling Sets
- Rolling Guidance Filter
- Relative Total Variation
- L0 Gradient Optimization
- Recovering Intrinsic Images with a global Sparsity Prior on Reflectance
- Intrinsic Images by Clustering
- Structured Edge Detection
- Pointwise Mutual Information
- TurboPixels
- Segmentation by Transduction
- Camera Calibration Toolbox for Matlab
- Multiple Camera Calibration Toolbox
- kinfu_remake: Lightweight, reworked and optimized version of Kinfu.
- SLAMBench: Multiple-implementation of KinectFusion
- RatSLAM
- Geometric Reasoning - David C. Lee (CMU)
- RGBD2Full3D - Ruiqi Guo (UIUC)
- SPP-Net
- ANN: A Library for Approximate Nearest Neighbor Searching
- Fast k nearest neighbor search using GPU
- Coherency Sensitive Hashing
- PMBP: PatchMatch Belief Propagation
- TreeCANN
- Visual Tracker Benchmark
- Spatio-Temporal Context Learning
- CMT: Clustering of Static-Adaptive Correspondences for Deformable Object Tracking
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Modular Tracking Framework
- Ceres Solver - Nonlinear least-square problem and unconstrained optimization solver
- Bob: a free signal processing and machine learning toolbox for researchers
- OpenGV - geometric computer vision algorithms
- Transformed Self-Exemplars
- Image Deblurring using Inertial Measurement Sensors
- PlanarStructureCompletion
- Closed-form image matting
- Multiple Camera Calibration Toolbox
- Geometric Reasoning - David C. Lee (CMU)
- Fast k nearest neighbor search using GPU
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- HCI Challenge
- Deformable Patches
- From Learning Models of Natural Image Patches to Whole Image Restoration
- OpenGM - Factor graph based discrete optimization and inference solver
- Bob: a free signal processing and machine learning toolbox for researchers
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- QuickShift
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- VLFeat
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Power Watershed
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Coarse2Fine Optical Flow - Ce Liu (MIT)
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- FOVIS: RGB-D visual odometry
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- SEEDS
- Efficient hierarchical graph-based video segmentation
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Geodesic Graph Cut
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- SLIC Super-pixel
- Multiple Camera Calibration Toolbox
- PatchMatch
- Generalized PatchMatch
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Deformable Patches
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Secrets of Optical Flow Estimation and Their Principles
- Transformed Self-Exemplars
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- PlanarStructureCompletion
- Spectral Matting
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- Deformable Patches
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- Visual Tracker Benchmark
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- Efficient marginal likelihood optimization in blind deconvolution
- BING: Objectness Estimation
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- Enhanced adaptive coupled-layer LGTracker++
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Deformable Patches
- PlanarStructureCompletion
- Domain Transform
- Multiple Camera Calibration Toolbox
- Visual Tracker Benchmark
Courses
- Machine Learning - Andrew Ng (Stanford University)
- Learning from Data - Yaser S. Abu-Mostafa (Caltech)
- 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)
- Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
- 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)
- Computational Photography - Alexei A. Efros (CMU)
- Digital & Computational Photography - Fredo Durand (MIT)
- Courses in Graphics - Stanford University
- Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
- Computational Photography - Kyros Kutulakos (University of Toronto)
- 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)
- Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Convex Optimization - Stephen Boyd (Stanford University)
- (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (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)
- Computer Vision
- Computer Vision 1
- Computer Vision 2
- Intro to Machine Learning - Sebastian Thrun (Stanford University)
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Computational Photography - Derek Hoiem (UIUC)
- Computational Photography - James Hays (Brown University)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Computational Photography - Irfan Essa (Georgia Tech)
- Convolutional Neural Networks for Visual Recognition - Fei-Fei Li and Andrej Karpathy (Stanford University)
- 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)
- Computer Vision
- Computational Photography - Alexei A. Efros (CMU)
- Introduction to Visual Computing - Kyros Kutulakos (University of Toronto)
- Computational Photography - Kyros Kutulakos (University of Toronto)
- Statistical Learning Theory and Applications - Tomaso Poggio, Lorenzo Rosasco, Carlo Ciliberto, Charlie Frogner, Georgios Evangelopoulos, Ben Deen (MIT)
- 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 - Charles Isbell, Michael Littman (Georgia Tech)
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Multiple View Geometry
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Optimization at MIT - (MIT)
- Computer Vision - Steve Seitz (University of Washington)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Digital & Computational Photography - Fredo Durand (MIT)
- Practical Machine Learning - Michael Jordan (UC Berkeley)
Books
- Neural Networks and Deep Learning - Michael Nielsen 2014
- Computer Vision: Theory and Application - Rick Szeliski 2010
- Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012
- Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
- 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
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics - Justin Solomon 2015
- Neural Networks and Deep Learning - Michael Nielsen 2014
- Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012
- Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
- Pattern Classification - Peter E. Hart, David G. Stork, and Richard O. Duda 2000
- Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012
- Linear Algebra and Its Applications - Gilbert Strang 1995
- Pattern Recognition and Machine Learning - Christopher M. Bishop 2007
- Computer Vision - Linda G. Shapiro 2001
- OpenCV Essentials - Oscar Deniz Suarez, Mª del Milagro Fernandez Carrobles, Noelia Vallez Enano, Gloria Bueno Garcia, Ismael Serrano Gracia
- Computer Vision: A Modern Approach (2nd edition) - David Forsyth and Jean Ponce 2011
- Vision Science: Photons to Phenomenology - Stephen E. Palmer 1999
- Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler
- Machine Learning - Tom M. Mitchell 1997
- Multiple View Geometry in Computer Vision - Richard Hartley and Andrew Zisserman 2004
- High dynamic range imaging: acquisition, display, and image-based lighting - Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S., Ward, G., Myszkowski, K 2010
Datasets
Blogs
- Andrej Karpathy blog - Andrej Karpathy
- Computer Vision Basics with Python Keras and OpenCV - Jason Chin (University of Western Ontario)
- AI Shack - Utkarsh Sinha
- Computer vision for dummies - Vincent Spruyt
- Computer Vision Talks - Eugene Khvedchenya
Resources for students
- Notes on writing - Frédo Durand (MIT)
- How to Write a Bad Article - Frédo Durand (MIT)
- 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)
- Write Good Papers - 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)
- Writing Research Papers - Aaron Hertzmann (Adobe Research)
- 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)
- Giving a Research Talk - Frédo Durand (MIT)
- How to give a good talk - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
- How to do research - William T. Freeman (MIT)
- Warning Signs of Bogus Progress in Research in an Age of Rich Computation and Information - Yi Ma (UIUC)
- 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)
- How to have a paper get into SIGGRAPH? - Takeo Igarashi (The University of Tokyo)
- Good Writing - Marc H. Raibert (Boston Dynamics, Inc.)
- Seven Warning Signs of Bogus Science - Robert L. Park
- Resources for students - Frédo Durand (MIT)
- How to give a good talk - David Fleet (University of Toronto) and Aaron Hertzmann (Adobe Research)
- Write Good Papers - Frédo Durand (MIT)
Contributing
- pull requests
Papers
- CVPapers - Computer vision papers on the web
- Visionbib Survey Paper List
- Computer Vision: A Reference Guide
- CVPapers - Computer vision papers on the web
- NIPS Proceedings - NIPS papers on the web
- Computer Vision Foundation open access
- Visionbib Survey Paper List
- SIGGRAPH Paper on the web - Graphics papers on the web
- Foundations and Trends® in Computer Graphics and Vision
Tutorials and talks
- 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
- ECCV 2014 - Sep 2014
- ECCV 2012 - Oct 2012
- 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
- 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
- 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 in Computer Vision - Simon Lucey (CMU) 2008
- Graphical Models - Zoubin Ghahramani (University of Cambridge) 2009
- Machine Learning, Probability and Graphical Models - Sam Roweis (NYU) 2006
- 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)
- Continuous Optimization in Computer Vision - Andrew Fitzgibbon (Microsoft Research)
- Beyond stochastic gradient descent for large-scale machine learning - Francis Bach (INRIA)
- A tutorial on Deep Learning - Geoffrey E. Hinton (University of Toronto)
- Scaling up Deep Learning - Yoshua Bengio (University of Montreal)
- The Unreasonable Effectivness Of Deep Learning
- 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
- 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)
- Object Recognition - Larry Zitnick (Microsoft Research)
- A Gentle Tutorial of the EM Algorithm - Jeff A. Bilmes (UC Berkeley) 1998
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Applications to Machine Vision - Andrew Blake (Microsoft Research) 2008
- The Future of Image Search - Jitendra Malik (UC Berkeley) 2008
- Graduate Summer School 2013: Computer Vision - IPAM, 2013
- ECCV 2014 - Sep 2014
- ECCV 2012 - Oct 2012
- 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
- 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
- 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
- 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)
- Beyond stochastic gradient descent for large-scale machine learning - Francis Bach (INRIA)
- 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
- 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
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Object Recognition - Larry Zitnick (Microsoft Research)
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- ICML 2013 - Jul 2013
- CVPR 2013 - Jun 2013
- CVPR 2012 - Jun 2012
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- Computer Vision Talks - Lectures, keynotes, panel discussions on computer vision
- CVPR 2014 - Jun 2014
- ICCV 2013 - Dec 2013
- ICML 2012 - Jun 2012
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