Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-computer-vision
https://github.com/karim7262/awesome-computer-vision
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Awesome Lists
- Awesome Domain Adaptation
- Awesome Object Detection
- Awesome 3D Machine Learning
- Awesome Action Recognition
- Awesome Scene Understanding
- Awesome Adversarial Machine Learning
- Awesome Adversarial Deep Learning
- Awesome Face
- Awesome Face Recognition
- Awesome Human Pose Estimation
- Awesome medical imaging
- Awesome Images
- Awesome Graphics
- Awesome Neural Radiance Fields
- Awesome Implicit Neural Representations
- Awesome Neural Rendering
- Awesome Dataset Tools
- Awesome Explainable AI
- Awesome Fairness in AI
- Awesome Machine Learning Interpretability
- Awesome Production Machine Learning
- Awesome Video Text Retrieval
- Awesome Image-to-Image Translation
- Awesome Image Inpainting
- Awesome Deep HDR
- Awesome Video Generation
- Awesome GAN applications
- Awesome Generative Modeling
- Awesome Image Classification
- Awesome Machine Learning in Biomedical(Healthcare) Imaging
- Awesome Deep Learning for Tracking and Detection
- Awesome Deep Learning for Video Analysis
- Awesome Vision + Language
- Awesome Robotics
- Awesome Visual Transformer
- Awesome Embodied Vision
- Awesome Anomaly Detection
- Awesome Makeup Transfer
- Awesome Learning with Label Noise
- Awesome Deblurring
- Awsome Deep Geometry Learning
- Awesome Image Distortion Correction
- Awesome Neuron Segmentation in EM Images
- Awsome Delineation
- Awesome ImageHarmonization
- Awsome GAN Training
- Awesome Document Understanding
- Awesome Public Datasets
- Awesome Mobile Machine Learning
- Awesome Machine Learning
- Awesome Robotics Datasets
- Awesome Deep Learning
- Awesome Deep Vision
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Software
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Multiple Camera Calibration Toolbox
- openSLAM
- LVR-KinFu: kinfu_remake based Large Scale KinectFusion with online reconstruction
- Comma Coloring
- Annotorious
- LabelME
- 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
- SimpleCV
- Piotr's Computer Vision Matlab Toolbox
- ImageUtilities
- Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
- MinimalSolvers - Minimal problems solver
- Multi-View Environment
- Visual SFM
- Bundler SFM
- openMVG: open Multiple View Geometry - Multiple View Geometry; Structure from Motion library & softwares
- Floating Scale Surface Reconstruction
- Large-Scale Texturing of 3D Reconstructions
- SIFT++
- BRISK
- FREAK
- AKAZE
- List of Semantic Segmentation algorithms
- 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
- Transformed Self-Exemplars
- 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
- PlanarStructureCompletion
- 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
- Graph-based Segmentation
- Contour Detection and Image Segmentation
- Structured Edge Detection
- Pointwise Mutual Information
- SLIC Super-pixel
- TurboPixels
- Contour Relaxed Superpixels
- Multiscale Combinatorial Grouping
- Random Walker
- Geodesic Segmentation
- Lazy Snapping
- Video Segmentation with Superpixels
- Camera Calibration Toolbox for Matlab
- Camera calibration With OpenCV
- 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
- ANN: A Library for Approximate Nearest Neighbor Searching
- FLANN - Fast Library for Approximate Nearest Neighbors
- 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
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Modular Tracking Framework
- NeuralTalk
- Ceres Solver - Nonlinear least-square problem and unconstrained optimization solver
- GTSAM - Factor graph based lease-square optimization solver
- LIBSVM -- A Library for Support Vector Machines
- 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
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- 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
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- PlanarStructureCompletion
- Transformed Self-Exemplars
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Secrets of Optical Flow Estimation and Their Principles
- Transformed Self-Exemplars
- Unnatural L0 Sparse Representation for Natural Image Deblurring
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Segmentation by Transduction
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- 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
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Transformed Self-Exemplars
- Fast motion deblurring
- Efficient marginal likelihood optimization in blind deconvolution
- Blind Deblurring Using Internal Patch Recurrence
- PlanarStructureCompletion
- Efficient hierarchical graph-based video segmentation
- 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
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Transformed Self-Exemplars
- Hyper-Laplacian Priors
- Blind Deconvolution Using a Normalized Sparsity Measure
- Blur-kernel estimation from spectral irregularities
- PlanarStructureCompletion
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Transformed Self-Exemplars
- PlanarStructureCompletion
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- Parallel Robust Optical Flow by Sánchez Pérez et al.
- Spectral Matting
- Learning-based Matting
- CF2: Hierarchical Convolutional Features for Visual Tracking
- Computer Vision Resources - Jia-Bin Huang (UIUC)
- VLFeat
- Kanade-Lucas-Tomasi Feature Tracker
- CF2: Hierarchical Convolutional Features for Visual Tracking
- PlanarStructureCompletion
- Transformed Self-Exemplars
- Structured Edge Detection
- 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
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Datasets
- Ground Truth Stixel Dataset
- MPI-Sintel Optical Flow Dataset and Evaluation
- Are we there yet? - Which paper provides the best results on standard dataset X?
- ComputerVisionOnline Datasets
- CV datasets
- visionbib
- Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms
- ChangeDetection.net
- Ground-truth dataset and baseline evaluations for intrinsic image algorithms
- Intrinsic Images in the Wild
- Intrinsic Image Evaluation on Synthetic Complex Scenes
- OpenSurface
- Flickr Material Database
- Materials in Context Dataset
- Visual Tracker Benchmark v1.1
- Tracking Manipulation Tasks (TMT)
- CAM2
- ChangeDetection.net
- The PASCAL Visual Object Classes
- SUN Database
- Place Dataset
- Microsoft COCO
- Stanford background dataset
- CamVid
- Barcelona Dataset
- SIFT Flow Dataset
- EPFL Car Dataset
- NYU Car Dataset
- Fine-grained Classification Challenge
- Caltech-UCSD Birds 200
- Caltech Pedestrian Detection Benchmark
- ETHZ Pedestrian Detection
- UCF Sports Action Data Set
- Flickr 8K
- Flickr 30K
- Ground Truth Stixel Dataset
- HCI Challenge
- Ground Truth Stixel Dataset
- Sun dataset
- Ground Truth Stixel Dataset
- Computer Vision Dataset on the web
- Ground Truth Stixel Dataset
- Ground Truth Stixel Dataset
- Visual Tracker Benchmark
- Ground Truth Stixel Dataset
- Levin dataset
- NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images
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Contributing
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Books
- Computer Vision: Models, Learning, and Inference - Simon J. D. Prince 2012
- Computer Vision: Theory and Application - Rick Szeliski 2010
- Visual Object Recognition synthesis lecture - Kristen Grauman and Bastian Leibe 2011
- Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics - Justin Solomon 2015
- Image Processing and Analysis - Stan Birchfield 2018
- Computer Vision, From 3D Reconstruction to Recognition - Silvio Savarese 2018
- 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
- Neural Networks and Deep Learning - Michael Nielsen 2014
- Computer Vision - Linda G. Shapiro 2001
- Learning OpenCV: Computer Vision with the OpenCV Library - Gary Bradski and Adrian Kaehler
- Pattern Recognition and Machine Learning - Christopher M. Bishop 2007
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Courses
- 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)
- Language and Vision - Tamara Berg (UNC Chapel Hill)
- 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)
- 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
- 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)
- Machine Learning - Andrew Ng (Stanford University)
- 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)
- Course on Information Theory, Pattern Recognition, and Neural Networks - David MacKay (University of Cambridge)
- Methods for Applied Statistics: Unsupervised Learning - Lester Mackey (Stanford)
- Intro to Machine Learning - Sebastian Thrun (Stanford University)
- (Convolutional) Neural Networks for Visual Recognition - Fei-Fei Li, Andrej Karphaty, Justin Johnson (Stanford University)
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Convex Optimization II - Stephen Boyd (Stanford University)
- Convex Optimization - Stephen Boyd (Stanford University)
- Optimization at MIT - (MIT)
- Convex Optimization - Ryan Tibshirani (CMU)
- Image Manipulation and Computational Photography - Alexei A. Efros (UC Berkeley)
- Computational Photography - James Hays (Brown University)
- Computational Photography - Derek Hoiem (UIUC)
- Multiple View Geometry
- Machine Learning for Computer Vision - Rudolph Triebel (TU Munich)
- Learning from Data - Yaser S. Abu-Mostafa (Caltech)
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Blogs
- Andrej Karpathy blog - Andrej Karpathy
- AI Shack - Utkarsh Sinha
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Links
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Papers
- CVPapers - Computer vision papers on the web
- Visionbib Survey Paper List
- Computer Vision: A Reference Guide
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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
- 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
- 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
- Object Recognition - Larry Zitnick (Microsoft Research)
- Graphical Models - Zoubin Ghahramani (University of Cambridge) 2009
- Machine Learning, Probability and Graphical Models - Sam Roweis (NYU) 2006
- 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
- 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)
- 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
- Object Recognition - Larry Zitnick (Microsoft Research)
- Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)
- 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
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Songs
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Resources for students
- 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)
- 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)
- Resources for students - Frédo Durand (MIT)
- Write Good Papers - Frédo Durand (MIT)
- Notes on writing - Frédo Durand (MIT)
- How to Write a Bad Article - Frédo Durand (MIT)
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Keywords
deep-learning
13
machine-learning
11
awesome-list
10
awesome
8
computer-vision
8
papers
5
interpretability
4
paper
3
xai
3
interpretable-ai
2
datasets
2
fairness
2
face-recognition
2
curated-list
2
computer-graphics
2
robotics
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primitives
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dataset
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point-cloud
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face-detection
2
image-translation
2
detection
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object-detection
2
privacy-preserving-machine-learning
2
3d-reconstruction
2
neural-networks
2
paper-reading
1
pose-and-language
1
pose-dataset
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pose-focasting
1
pose-generation
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awsome-list
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real-time-pose-estimation
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awsome
1
annotations
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annotation-tool
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medical-imaging
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image-processing
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nerf
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neural-representations
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neural-rendering
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video-pose-estimation
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data-mining
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transparency
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secure-ml
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reliable-ai
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r
1
python
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privacy-enhancing-technologies
1
machine-learning-interpretability
1