本文转载自 Awesome Computer Vision，受 awesome-php 启发的精选计算机视觉资源列表。

有关计算机视觉领域的人员名单及其学术谱系，请访问这里。

1. 资料列表

• Awesome Machine Learning
• Awesome Deep Vision
• 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 Public Datasets
• Awesome Dataset Tools
• Awesome Robotics Datasets
• Awesome Mobile Machine Learning
• 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 Deep Learning
• Awesome Machine Learning in Biomedical(Healthcare) Imaging
• Awesome Deep Learning for Tracking and Detection
• Awesome Human Pose Estimation
• 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

2. 图书

2.1 计算机视觉

• 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
• 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

2.2 OpenCV 编程

• 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

2.3 机器学习

• 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

2.4 基础知识

• Linear Algebra and Its Applications - Gilbert Strang 1995

3. 课程

3.1 计算机视觉

• 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)
• Visual Recognition Spring 2016, Fall 2016 - 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)
• 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):

3.2 计算摄影

• 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)

3.3 机器学习和统计学习

• 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)

3.4 优化方法

• 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)

4. 论文

4.1 网络会议

• 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)

4.2 综述

• Visionbib Survey Paper List
• Foundations and Trends® in Computer Graphics and Vision
• Computer Vision: A Reference Guide

4.3 预训练模型

• List of Computer Vision models These models are trained on custom objects

5. 教程和讲座

5.1 计算机视觉

• 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

5.2 会议讲座

• CVPR 2015 - Jun 2015
• ECCV 2014 - Sep 2014
• CVPR 2014 - Jun 2014
• ICCV 2013 - Dec 2013
• ICML 2013 - Jul 2013
• CVPR 2013 - Jun 2013
• ECCV 2012 - Oct 2012
• ICML 2012 - Jun 2012
• CVPR 2012 - Jun 2012

5.3 3D 计算机视觉

• 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

5.4 网络视觉

• 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

5.5 计算摄影

• 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

5.6 视觉学习

• 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

5.7 目标识别

• Object Recognition - Larry Zitnick (Microsoft Research)
• Generative Models for Visual Objects and Object Recognition via Bayesian Inference - Fei-Fei Li (Stanford University)

5.8 图形模型

• 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

5.9 机器学习

• 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)

5.10 优化

• 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)

5.11 深度学习

• 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)
• Graduate Summer School 2012: Deep Learning, Feature Learning - IPAM, 2012
• Workshop on Big Data and Statistical Machine Learning
• Machine Learning Summer School - Reykjavik, Iceland 2014
  • 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)

6. 软件

6.1 标注工具

• Comma Coloring
• Annotorious
• LabelME
• gtmaker

6.2 外部资源链接

• 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

6.3 通用计算机视觉库

• Open CV
• mexopencv
• SimpleCV
• Open source Python module for computer vision
• ccv: A Modern Computer Vision Library
• VLFeat
• Matlab Computer Vision System Toolbox
• Piotr's Computer Vision Matlab Toolbox
• PCL: Point Cloud Library
• ImageUtilities

6.4 Multiple-view

• MATLAB Functions for Multiple View Geometry
• Peter Kovesi's Matlab Functions for Computer Vision and Image Analysis
• OpenGV - geometric computer vision algorithms
• MinimalSolvers - Minimal problems solver
• Multi-View Environment
• Visual SFM
• Bundler SFM
• 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
• Awesome 3D reconstruction list

6.5 特征检测和提取

• VLFeat
• SIFT
  • David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.
• SIFT++
• 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
• Local Binary Patterns

6.6 高动态范围成像

• HDR_Toolbox

6.7 语义分割

• List of Semantic Segmentation algorithms

6.8 Low-level 视觉

6.8.1 立体视觉

• Middlebury Stereo Vision
• The KITTI Vision Benchmark Suite
• LIBELAS: Library for Efficient Large-scale Stereo Matching
• Ground Truth Stixel Dataset

6.8.2 Optical Flow

• 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
• C++/MatLab Optical Flow by C. Liu (based on Brox et al. and Bruhn et al.)
• Parallel Robust Optical Flow by Sánchez Pérez et al.

6.8.3 图像去噪

BM3D, KSVD,

6.8.4 超分辨率

• 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

6.8.6 图像去模糊化

Non-blind 解卷积法

• 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

6.8.6 Blind 解卷积法

• 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

6.8.7 非均匀的去模糊化

• 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

6.8.8 图像完成度

• GIMP Resynthesizer
• Priority BP
• ImageMelding
• PlanarStructureCompletion

6.8.9 图像重定位

• RetargetMe

6.8.10 Alpha Matting

• Alpha Matting Evaluation
• Closed-form image matting
• Spectral Matting
• Learning-based Matting
• Improving Image Matting using Comprehensive Sampling Sets

6.8.11 图像金字塔

• The Steerable Pyramid
• CurveLab

6.8.12 保留边缘的图像处理

• 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

6.9 Intrinsic Images

• Recovering Intrinsic Images with a global Sparsity Prior on Reflectance
• Intrinsic Images by Clustering

6.10 轮廓检测和图像分割

• 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

6.11 交互式图像分割

• Random Walker
• Geodesic Segmentation
• Lazy Snapping
• Power Watershed
• Geodesic Graph Cut
• Segmentation by Transduction

6.12 视频分割

• Video Segmentation with Superpixels
• Efficient hierarchical graph-based video segmentation
• Object segmentation in video
• Streaming hierarchical video segmentation

6.13 相机校准

• Camera Calibration Toolbox for Matlab
• Camera calibration With OpenCV
• Multiple Camera Calibration Toolbox

6.14 同时进行定位和测绘

6.14.1 SLAM community:

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

6.14.2 定位/测绘:

• 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
• DVO: dense visual odometry
• FOVIS: RGB-D visual odometry

6.14.3 图优化：

• GTSAM: General smoothing and mapping library for Robotics and SFM -- Georgia Institute of Technology
• G2O: General framework for graph optomization

6.14.4 Loop Closure:

• FabMap: appearance-based loop closure system - also available in OpenCV2.4.11
• DBoW2: binary bag-of-words loop detection system

6.14.5 Localization & Mapping:

• RatSLAM
• LSD-SLAM
• ORB-SLAM

6.15 单视图空间理解

• Geometric Context - Derek Hoiem (CMU)
• Recovering Spatial Layout - Varsha Hedau (UIUC)
• Geometric Reasoning - David C. Lee (CMU)
• RGBD2Full3D - Ruiqi Guo (UIUC)

6.16 对象检测

• 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
• BING: Objectness Estimation
• Edge Boxes
• ReInspect

6.17 最近邻搜索

6.17.1 通用最近邻搜索

• ANN: A Library for Approximate Nearest Neighbor Searching
• FLANN - Fast Library for Approximate Nearest Neighbors
• Fast k nearest neighbor search using GPU

6.17.2 最近邻区域估计

• PatchMatch
• Generalized PatchMatch
• Coherency Sensitive Hashing
• PMBP: PatchMatch Belief Propagation
• TreeCANN

6.18 视觉跟踪

• 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

6.19 优化

• 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

6.20 深度学习

• Awesome Deep Vision

6.21 机器学习

• Awesome Machine Learning
• Bob: a free signal processing and machine learning toolbox for researchers
• LIBSVM -- A Library for Support Vector Machines

7. 数据集

7.1 外部数据集链接集合

• 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
• VisualData

7.2 Low-level 视觉

7.2.1 立体视觉

• Middlebury Stereo Vision
• The KITTI Vision Benchmark Suite
• LIBELAS: Library for Efficient Large-scale Stereo Matching
• Ground Truth Stixel Dataset

7.7.2 Optical Flow

• Middlebury Optical Flow Evaluation
• MPI-Sintel Optical Flow Dataset and Evaluation
• The KITTI Vision Benchmark Suite
• HCI Challenge

7.2.3 视频对象分割

• DAVIS: Densely Annotated VIdeo Segmentation
• SegTrack v2

7.2.4 变化检测

• Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms
• ChangeDetection.net

7.2.5 图像超分辨率

• Single-Image Super-Resolution: A Benchmark

7.3 Intrinsic Images

• Ground-truth dataset and baseline evaluations for intrinsic image algorithms
• Intrinsic Images in the Wild
• Intrinsic Image Evaluation on Synthetic Complex Scenes

7.4 材料识别

• OpenSurface
• Flickr Material Database
• Materials in Context Dataset

7.5 Multi-view 重建

• Multi-View Stereo Reconstruction

7.6 视觉跟踪

• Visual Tracker Benchmark
• Visual Tracker Benchmark v1.1
• VOT Challenge
• Princeton Tracking Benchmark
• Tracking Manipulation Tasks (TMT)

7.7 视觉监测

• VIRAT
• CAM2

7.8 变化检测

• ChangeDetection.net

7.9 视觉识别

7.9.1 图像分类

• The PASCAL Visual Object Classes
• ImageNet Large Scale Visual Recognition Challenge

7.9.2 自监督学习

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

7.9.3 场景识别

• SUN Database
• Place Dataset

7.9.4 目标检测

• The PASCAL Visual Object Classes
• ImageNet Object Detection Challenge
• Microsoft COCO

7.9.5 语义标签

• Stanford background dataset
• CamVid
• Barcelona Dataset
• SIFT Flow Dataset

7.9.6 Multi-view 目标检测

• 3D Object Dataset
• EPFL Car Dataset
• KTTI Dection Dataset
• SUN 3D Dataset
• PASCAL 3D+
• NYU Car Dataset

7.9.7 细粒度视觉识别

• Fine-grained Classification Challenge
• Caltech-UCSD Birds 200

7.9.8 行人检测

• Caltech Pedestrian Detection Benchmark
• ETHZ Pedestrian Detection

7.10 行动识别

7.10.1 基于视频

• HOLLYWOOD2 Dataset
• UCF Sports Action Data Set

7.10.2 图像去模糊化

• Sun dataset
• Levin dataset

7.11 Image Captioning

• Flickr 8K
• Flickr 30K
• Microsoft COCO

7.12 场景理解

SUN RGB-D - A RGB-D Scene Understanding Benchmark Suite

NYU depth v2 - Indoor Segmentation and Support Inference from RGBD Images

Aerial images

Aerial Image Segmentation - Learning Aerial Image Segmentation From Online Maps

8. 面向学生

8.1 资源链接集合

• 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)

8.2 写作

• 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)
• Common mistakes in technical writing - Wojciech Jarosz (Dartmouth College)

8.3 演讲

• 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

8.4 研究

• 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)

8.5 时间管理

• Time Management - Randy Pausch (CMU)

9. 博客

• 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)

10. 友情链接

• The Computer Vision Industry - David Lowe
• German Computer Vision Research Groups & Companies
• awesome-deep-learning
• awesome-machine-learning
• Cat Paper Collection
• Computer Vision News