An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.

In 2 lists

Open source Python module for computer vision

Open source Python module for computer vision. [Deprecated]

In 2 lists

ccv: A Modern Computer Vision Library

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library. [BSD]

In 5 listsDetails

VLFeat

VLFeat is an open and portable library of computer vision algorithms, which has a Matlab toolbox.

In 2 lists

Matlab Computer Vision System Toolbox

Piotr's Computer Vision Matlab Toolbox

PCL: Point Cloud Library

Standalone, large scale, open project for 2D/3D image and point cloud processing. Licence: BSD.

In 5 listsDetails

MATLAB Functions for Multiple View Geometry

Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis

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

. M. Waechter, N. Moehrle, M. Goesele. ECCV 2014.

In 2 lists

Awesome 3D reconstruction list

In 2 lists

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

HDR_Toolbox

HDR Toolbox for processing High Dynamic Range (HDR) images into MATLAB and Octave

In 2 lists

List of Semantic Segmentation algorithms

The KITTI Vision Benchmark Suite

LIBELAS: Library for Efficient Large-scale Stereo Matching

Middlebury Optical Flow Evaluation

MPI-Sintel Optical Flow Dataset and Evaluation

The KITTI Vision Benchmark Suite

Secrets of Optical Flow Estimation and Their Principles

Parallel Robust Optical Flow by Sánchez Pérez et al.

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

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

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 for Shaken Images

Single Image Deblurring Using Motion Density Functions

Image Deblurring using Inertial Measurement Sensors

Fast Removal of Non-uniform Camera Shake

GIMP Resynthesizer

Priority BP

ImageMelding

PlanarStructureCompletion

RetargetMe

Alpha Matting Evaluation

Closed-form image matting

Spectral Matting

Learning-based 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

Domain Transform

Adaptive Manifold

Guided image filtering

Recovering Intrinsic Images with a global Sparsity Prior on Reflectance

Intrinsic Images by Clustering

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

Structured Edge Detection Toolbox

In 2 lists

Random Walker

Geodesic Segmentation

Lazy Snapping

Power Watershed

Geodesic Graph Cut

Segmentation by Transduction

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

openSLAM

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

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

C++/ROS

In 2 lists

DVO: dense visual odometry

FOVIS: RGB-D visual odometry

GTSAM: General smoothing and mapping library for Robotics and SFM

Georgia Institute of Technology

G2O: General framework for graph optomization

g2o: A General Framework for Graph Optimization

In 3 lists

FabMap: appearance-based loop closure system

also available in OpenCV2.4.11

DBoW2: binary bag-of-words loop detection system

RatSLAM

LSD-SLAM

Large-Scale Direct Monocular SLAM is a real-time monocular SLAM.

In 3 lists

ORB-SLAM

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

Object detection system using deformable part models (DPMs) and latent SVM (voc-release5). You may want to use the latest tarball on my website. The github code may include code changes that have not been tested as thoroughly and will not necessarily reproduce the results on the website.

In 2 lists

Histograms of Sparse Codes for Object Detection

R-CNN: Regions with Convolutional Neural Network Features

In 2 lists

SPP-Net

SPP_net : Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

In 2 lists

BING: Objectness Estimation

ReInspect

ANN: A Library for Approximate Nearest Neighbor Searching

GNU General Public License

In 2 lists

FLANN - Fast Library for Approximate Nearest Neighbors

Fast Library for Approximate Nearest Neighbors.

In 4 lists

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

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

Modular Tracking Framework

NeuralTalk

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

In 3 lists

Ceres Solver

Nonlinear least-square problem and unconstrained optimization solver

In 2 lists

NLopt

Nonlinear least-square problem and unconstrained optimization solver

In 2 lists

OpenGM

Factor graph based discrete optimization and inference solver

GTSAM

Factor graph based lease-square optimization solver

In 2 lists

Awesome Deep Vision

Deep learning for computer vision

In 5 listsDetails

Awesome Machine Learning

The definitive curated list of machine learning frameworks, libraries and software organized by language. Covers Python, C++, Java, JavaScript, and more with comprehensive coverage of the ML ecosystem. CC0-1.0 licensed.

In 15 listsDetails

Bob: a free signal processing and machine learning toolbox for researchers

LIBSVM -- A Library for Support Vector Machines

is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

In 5 listsDetails

Datasets

CV Datasets on the web

CVPapers

Are we there yet?

Which paper provides the best results on standard dataset X?

Yet Another Computer Vision Index To Datasets

ComputerVisionOnline Datasets

CVOnline Dataset

CV datasets

visionbib

VisualData

Middlebury Stereo Vision

The KITTI Vision Benchmark Suite

LIBELAS: Library for Efficient Large-scale Stereo Matching

Ground Truth Stixel Dataset

Middlebury Optical Flow Evaluation

MPI-Sintel Optical Flow Dataset and Evaluation

The KITTI Vision Benchmark Suite

HCI Challenge

DAVIS: Densely Annotated VIdeo Segmentation

SegTrack v2

Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms

ChangeDetection.net

Single-Image Super-Resolution: A Benchmark

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

Materials Dataset with real world images in 23 categories.

In 2 lists

Multi-View Stereo Reconstruction

See "A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms". CVPR 2006.

In 2 lists

Visual Tracker Benchmark

Visual Tracker Benchmark v1.1

Visual Tracking Challenge

Princeton Tracking Benchmark

Tracking Manipulation Tasks (TMT)

VIRAT

CAM2

The PASCAL Visual Object Classes

ImageNet Large Scale Visual Recognition Challenge

PASS: An An ImageNet replacement for self-supervised pretraining without humans

SUN Database

Place Dataset

Microsoft COCO

is the modern replacement to the ImageNet challenge

In 2 lists

Stanford background dataset

CamVid

Barcelona Dataset

SIFT Flow Dataset

3D Object Dataset

EPFL Car Dataset

KTTI Dection Dataset

SUN 3D Dataset

PASCAL 3D+

NYU Car Dataset

Fine-grained Classification Challenge

Caltech-UCSD Birds 200

Caltech Pedestrian Detection Benchmark

ETHZ Pedestrian Detection

HOLLYWOOD2 Dataset

Spatio-Temporal annotations

In 2 lists

UCF Sports Action Data Set

, note: the train/test split link in the official website is broken. Instead, you can download it from here.

In 2 lists

Edge-based Blur Kernel Estimation Using Patch Priors

Levin dataset

Flickr 8K

In 2 lists

Flickr 30K

In 2 lists

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)

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)

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)

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

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

In 3 lists

AI Shack

Utkarsh Sinha

Computer Vision Talks

Eugene Khvedchenya

Computer Vision Basics with Python Keras and OpenCV

Jason Chin (University of Western Ontario)

Songs

The Fundamental Matrix Song

The RANSAC Song

Machine Learning A Cappella - Overfitting Thriller

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