机器视觉

Awesome Computer Vision: Awesome

精选的精彩计算机视觉资源列表,受到启发 awesome-php.

有关计算机视觉的人员列出了他们的学术谱系,请访问 here

Contributing

请随时发送给我 pull requests 或发送电子邮件(jbhuang1@illinois.edu)以添加链接.

Books

Computer Vision

OpenCV Programming

Machine Learning

Fundamentals

Courses

Computer Vision

Computational Photography

Machine Learning and Statistical Learning

Optimization

Papers

Conference papers on the web

Survey Papers

Tutorials and talks

Computer Vision

Recent Conference Talks

3D Computer Vision

Internet Vision

Computational Photography

Learning and Vision

Object Recognition

Graphical Models

Machine Learning

Optimization

Deep Learning

Software

Annotation tools

General Purpose Computer Vision Library

Multiple-view Computer Vision

Feature Detection and Extraction

  • VLFeat
  • SIFT
  • David G. Lowe,“尺度不变关键点的独特图像特征”,国际计算机视觉杂志,60,2(2004),第91-110页.
  • SIFT++
  • BRISK
  • Stefan Leutenegger,Margarita Chli和Roland Siegwart,“BRISK:Binary Robust Invariant Scalable Keypoints”,ICCV 2011
  • SURF
  • Herbert Bay,Andreas Ess,Tinne Tuytelaars,Luc Van Gool,“SURF:加速强大功能”,计算机视觉和图像理解(CVIU),Vol. 110,第3期,第346-359页,2008年
  • FREAK
  • A. Alahi,R.Ortiz和P. Vandergheynst,“FREAK:Fast Retina Keypoint”,CVPR 2012
  • AKAZE
  • Pablo F. Alcantarilla,Adrien Bartoli和Andrew J. Davison,“KAZE Features”,ECCV 2012
  • Local Binary Patterns

High Dynamic Range Imaging

Semantic Segmentation

Low-level Vision

Stereo Vision
Optical Flow
Image Denoising

BM3D,KSVD,

Super-resolution
  • Multi-frame image super-resolution *拾取,LC机器学习多帧图像超分辨率,博士论文2008
  • Markov Random Fields for Super-Resolution
    • W. T Freeman和C. Liu. 用于超分辨率和纹理合成的马尔可夫随机场. 在A. Blake,P.Kohli和C. Rother编辑,马克索夫随机视野和视觉处理的进展,第10章.麻省理工学院出版社,2011年
  • Sparse regression and natural image prior
    • KI Kim和Y. Kwon,“使用稀疏回归和自然图像先验的单图像超分辨率”,IEEE Trans. 模式分析与机器智能,第一卷 32,不. 6,pp.1127-1133,2010.
  • Single-Image Super Resolution via a Statistical Model
    • T. Peleg和M. Elad,基于稀疏表示的单图像超分辨率的统计预测模型,IEEE Transactions on Image Processing,Vol. 2014年6月23日第6期,第2569-2582页
  • Sparse Coding for Super-Resolution
    • R. Zeyde,M.Elad和M. Protter使用稀疏表示,曲线和曲面进行单幅图像放大,阿维尼翁 - 法国,2010年6月24日至30日(也出现在讲座 - 计算机笔记中 - 科学 - LNCS).
  • Patch-wise Sparse Recovery
    • Jian Jiano Yang,John Wright,Thomas Huang和Yi Ma. 通过稀疏表示的图像超分辨率. IEEE图像处理交易(TIP),第一卷. 19,2010年第11期.
  • Neighbor embedding
    • H. Chang,DY Yeung,Y.Xiong. 通过邻居嵌入的超分辨率. 2004年6月27日至7月2日在美国华盛顿特区举行的IEEE计算机协会计算机视觉和模式识别会议(CVPR),第1卷,第275-282页.
  • Deformable Patches
    • Yu Zhu,Yanning Zhang和Alan Yuille,使用可变形补丁的单图像超分辨率,CVPR 2014
  • SRCNN
    • Chao Dong,Chen Change Loy,Kaiming He,Xiaoou Tang,在ECCV 2014中学习深度卷积网络以实现图像超分辨率
  • A+: Adjusted Anchored Neighborhood Regression
    • R. Timofte,V.De Smet和L. Van Gool. A +:调整的锚定邻域回归,用于快速超分辨率,ACCV 2014
  • Transformed Self-Exemplars
    • Jia-Bin Huang,Abhishek Singh和Narendra Ahuja,使用转换自我范例的单图像超分辨率,IEEE计算机视觉和模式识别会议,2015
Image Deblurring

非盲去卷积 * 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

盲目反卷积 * 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

非均匀的去模糊 * 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

Image Completion
Image Retargeting
Alpha Matting
Image Pyramid
Edge-preserving image processing

Intrinsic Images

Contour Detection and Image Segmentation

Interactive Image Segmentation

Video Segmentation

Camera calibration

Simultaneous localization and mapping

SLAM community:
Tracking/Odometry:
Graph Optimization:
Loop Closure:
Localization & Mapping:

Single-view Spatial Understanding

Object Detection

Nearest Neighbor Field Estimation

Visual Tracking

Saliency Detection

Attributes

Action Reconition

Egocentric cameras

Human-in-the-loop systems

Image Captioning

Optimization

  • Ceres Solver - 非线性最小二乘问题和无约束优化求解器
  • NLopt- 非线性最小二乘问题和无约束优化求解器
  • OpenGM - 基于因子图的离散优化和推理求解器
  • GTSAM - 基于因子图的租赁平方优化求解器

Deep Learning

Machine Learning

Datasets

Low-level Vision

Stereo Vision
Optical Flow
Video Object Segmentation
Change Detection
Image Super-resolutions

Intrinsic Images

Material Recognition

Multi-view Reconsturction

Saliency Detection

Visual Tracking

Visual Surveillance

Saliency Detection

Change detection

Visual Recognition

Image Classification
Scene Recognition
Object Detection
Semantic labeling
Multi-view Object Detection
Fine-grained Visual Recognition
Pedestrian Detection

Action Recognition

Image-based
Video-based
Image Deblurring

Image Captioning

Scene Understanding

# SUN RGB-D - RGB-D场景理解基准套件 # NYU depth v2 - RGBD图像的室内分割和支持推断

Aerial images

# Aerial Image Segmentation - 从在线地图学习航空图像分割

Resources for students

Writing

Presentation

Research

Time Management

Blogs

Songs

Licenses

License

CC0

在法律允许的范围内, Jia-Bin Huang 已放弃对此作品的所有版权及相关或相邻权利.