Computer Vision
This is a mirrored zone from the jbhuang0604/awesome-computer-vision repository. Part of the Awesome list collection.
Contents_Index
- AWESOME LISTS53
- COMPUTER VISION12
- OPENCV PROGRAMMING3
- MACHINE LEARNING9
- FUNDAMENTALS1
- COMPUTER VISION16
- COMPUTATIONAL PHOTOGRAPHY13
- MACHINE LEARNING AND STATISTICAL LEARNING13
- OPTIMIZATION5
- CONFERENCE PAPERS ON THE WEB6
- SURVEY PAPERS3
- PRE-TRAINED COMPUTER VISION MODELS1
- COMPUTER VISION6
- RECENT CONFERENCE TALKS9
- 3D COMPUTER VISION2
- INTERNET VISION3
- COMPUTATIONAL PHOTOGRAPHY8
- LEARNING AND VISION3
- OBJECT RECOGNITION2
- GRAPHICAL MODELS4
- MACHINE LEARNING4
- OPTIMIZATION5
- DEEP LEARNING10
- ANNOTATION TOOLS4
- EXTERNAL RESOURCE LINKS4
- GENERAL PURPOSE COMPUTER VISION LIBRARY10
- MULTIPLE-VIEW COMPUTER VISION13
- FEATURE DETECTION AND EXTRACTION8
- HIGH DYNAMIC RANGE IMAGING1
- SEMANTIC SEGMENTATION1
- STEREO VISION4
- OPTICAL FLOW7
- SUPER-RESOLUTION11
- IMAGE DEBLURRING21
- IMAGE COMPLETION4
- IMAGE RETARGETING1
- ALPHA MATTING5
- IMAGE PYRAMID2
- EDGE-PRESERVING IMAGE PROCESSING9
- INTRINSIC IMAGES2
- CONTOUR DETECTION AND IMAGE SEGMENTATION16
- INTERACTIVE IMAGE SEGMENTATION6
- VIDEO SEGMENTATION4
- CAMERA CALIBRATION3
- SLAM COMMUNITY:2
- TRACKING/ODOMETRY:11
- GRAPH OPTIMIZATION:2
- LOOP CLOSURE:2
- LOCALIZATION & MAPPING:3
- SINGLE-VIEW SPATIAL UNDERSTANDING4
- OBJECT DETECTION9
- GENERAL PURPOSE NEAREST NEIGHBOR SEARCH3
- NEAREST NEIGHBOR FIELD ESTIMATION5
- VISUAL TRACKING16
- IMAGE CAPTIONING1
- OPTIMIZATION4
- DEEP LEARNING1
- MACHINE LEARNING3
- EXTERNAL DATASET LINK COLLECTION8
- STEREO VISION4
- OPTICAL FLOW4
- VIDEO OBJECT SEGMENTATION2
- CHANGE DETECTION2
- IMAGE SUPER-RESOLUTIONS1
- INTRINSIC IMAGES3
- MATERIAL RECOGNITION3
- MULTI-VIEW RECONSTURCTION1
- VISUAL TRACKING5
- VISUAL SURVEILLANCE2
- CHANGE DETECTION1
- IMAGE CLASSIFICATION2
- SELF-SUPERVISED LEARNING1
- SCENE RECOGNITION2
- OBJECT DETECTION3
- SEMANTIC LABELING4
- MULTI-VIEW OBJECT DETECTION6
- FINE-GRAINED VISUAL RECOGNITION2
- PEDESTRIAN DETECTION2
- VIDEO-BASED2
- IMAGE DEBLURRING2
- IMAGE CAPTIONING3
- RESOURCE LINK COLLECTION5
- WRITING14
- PRESENTATION3
- RESEARCH9
- TIME MANAGEMENT1
- BLOGS7
- LINKS6
- SONGS3
Awesome Lists
53_ENTRIESComputer Vision
12_ENTRIES- 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
- Image Processing and Analysis
Stan Birchfield 2018
- Computer Vision, From 3D Reconstruction to Recognition
Silvio Savarese 2018
OpenCV Programming
3_ENTRIES- 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
9_ENTRIES- 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
1_ENTRIES- Linear Algebra and Its Applications
Gilbert Strang 1995
Computer Vision
16_ENTRIES- Visual Recognition Spring 2016, Fall 2016 - Kristen Grauman (UT Austin)
- 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)
- 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)
- Computer Vision
Pascal Fua (EPFL):
- Computer Vision 1
Carsten Rother (TU Dresden):
- Computer Vision 2
Carsten Rother (TU Dresden):
- Multiple View Geometry
Daniel Cremers (TU Munich):
Computational Photography
13_ENTRIES- 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
13_ENTRIES- 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)
- Intro to Machine Learning
Sebastian Thrun (Stanford University)
- Machine Learning
Charles Isbell, Michael Littman (Georgia Tech)
- (Convolutional) Neural Networks for Visual Recognition
Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
- Machine Learning for Computer Vision
Rudolph Triebel (TU Munich)
Optimization
5_ENTRIES- 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)
Conference papers on the web
6_ENTRIES- CVPapers
Computer vision papers on the web
- SIGGRAPH Paper on the web
Graphics papers on the web
- NIPS Proceedings
NIPS papers on the web
- Annotated Computer Vision Bibliography
Keith Price (USC)
Survey Papers
3_ENTRIESPre-trained Computer Vision Models
1_ENTRIES- List of Computer Vision models
These models are trained on custom objects
Computer Vision
6_ENTRIES- 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)
Recent Conference Talks
9_ENTRIES3D Computer Vision
2_ENTRIES- 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
3_ENTRIES- 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
8_ENTRIES- 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
3_ENTRIES- 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
2_ENTRIES- Object Recognition
Larry Zitnick (Microsoft Research)
- Generative Models for Visual Objects and Object Recognition via Bayesian Inference
Fei-Fei Li (Stanford University)
Graphical Models
4_ENTRIES- 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
4_ENTRIES- 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
5_ENTRIES- 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
10_ENTRIES- 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)
- Machine Learning Summer School
Reykjavik, Iceland 2014Deep 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)
Annotation tools
4_ENTRIESExternal Resource Links
4_ENTRIES- Computer Vision Resources
Jia-Bin Huang (UIUC)
- Source Code Collection for Reproducible Research
Xin Li (West Virginia University)
General Purpose Computer Vision Library
10_ENTRIESMultiple-view Computer Vision
13_ENTRIES- OpenGV
geometric computer vision algorithms
- MinimalSolvers
Minimal problems solver
- openMVG: open Multiple View Geometry
Multiple View Geometry; Structure from Motion library & softwares
Feature Detection and Extraction
8_ENTRIES- SIFT
David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
- 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
High Dynamic Range Imaging
1_ENTRIESSemantic Segmentation
1_ENTRIESStereo Vision
4_ENTRIESOptical Flow
7_ENTRIESSuper-resolution
11_ENTRIES- 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
21_ENTRIESNon-blind deconvolution
- Neural Deconvolution
Blind deconvolution
- Blind Deblurring Using Internal Patch Recurrence
Non-uniform Deblurring
Image Completion
4_ENTRIESImage Retargeting
1_ENTRIESAlpha Matting
5_ENTRIESImage Pyramid
2_ENTRIESEdge-preserving image processing
9_ENTRIESIntrinsic Images
2_ENTRIESContour Detection and Image Segmentation
16_ENTRIESInteractive Image Segmentation
6_ENTRIESVideo Segmentation
4_ENTRIESCamera calibration
3_ENTRIESSLAM community:
2_ENTRIESTracking/Odometry:
11_ENTRIESGraph Optimization:
2_ENTRIES- GTSAM: General smoothing and mapping library for Robotics and SFM
- Georgia Institute of Technology
Loop Closure:
2_ENTRIES- FabMap: appearance-based loop closure system
also available in OpenCV2.4.11
Localization & Mapping:
3_ENTRIESSingle-view Spatial Understanding
4_ENTRIES- Geometric Context
Derek Hoiem (CMU)
- Recovering Spatial Layout
Varsha Hedau (UIUC)
- Geometric Reasoning
David C. Lee (CMU)
- RGBD2Full3D
Ruiqi Guo (UIUC)
Object Detection
9_ENTRIESGeneral purpose nearest neighbor search
3_ENTRIESNearest Neighbor Field Estimation
5_ENTRIESVisual Tracking
16_ENTRIESImage Captioning
1_ENTRIESOptimization
4_ENTRIES- 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
1_ENTRIESMachine Learning
3_ENTRIESExternal Dataset Link Collection
8_ENTRIES- Are we there yet?
Which paper provides the best results on standard dataset X?
Stereo Vision
4_ENTRIESOptical Flow
4_ENTRIESVideo Object Segmentation
2_ENTRIESChange Detection
2_ENTRIESImage Super-resolutions
1_ENTRIESIntrinsic Images
3_ENTRIESMaterial Recognition
3_ENTRIESMulti-view Reconsturction
1_ENTRIESVisual Tracking
5_ENTRIESVisual Surveillance
2_ENTRIESChange detection
1_ENTRIESImage Classification
2_ENTRIESSelf-supervised Learning
1_ENTRIESScene Recognition
2_ENTRIESObject Detection
3_ENTRIESSemantic labeling
4_ENTRIESMulti-view Object Detection
6_ENTRIESFine-grained Visual Recognition
2_ENTRIESPedestrian Detection
2_ENTRIESVideo-based
2_ENTRIESImage Deblurring
2_ENTRIESImage Captioning
3_ENTRIESResource link collection
5_ENTRIES- 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
14_ENTRIES- 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 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
3_ENTRIES- 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
9_ENTRIES- How to do research
William T. Freeman (MIT)
- You and Your Research
Richard Hamming
- 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
1_ENTRIES- Time Management
Randy Pausch (CMU)
Blogs
7_ENTRIES- 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
- Computer Vision Basics with Python Keras and OpenCV
Jason Chin (University of Western Ontario)
Links
6_ENTRIES- The Computer Vision Industry
David Lowe