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

文章目录

1. 资料列表

2. 图书

2.1 计算机视觉

2.2 OpenCV 编程

2.3 机器学习

2.4 基础知识

3. 课程

3.1 计算机视觉

3.2 计算摄影

3.3 机器学习和统计学习

3.4 优化方法

4. 论文

4.1 网络会议

4.2 综述

4.3 预训练模型

5. 教程和讲座

5.1 计算机视觉

5.2 会议讲座

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

5.7 目标识别

5.8 图形模型

5.9 机器学习

5.10 优化

5.11 深度学习

6. 软件

6.1 标注工具

  • Comma Coloring
  • Annotorious
  • LabelME
  • gtmaker

6.2 外部资源链接

6.3 通用计算机视觉库

6.4 Multiple-view

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

6.7 语义分割

6.8 Low-level 视觉

6.8.1 立体视觉

6.8.2 Optical Flow

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

6.8.6 Blind 解卷积法

6.8.7 非均匀的去模糊化

6.8.8 图像完成度

6.8.9 图像重定位

6.8.10 Alpha Matting

6.8.11 图像金字塔

6.8.12 保留边缘的图像处理

6.9 Intrinsic Images

6.10 轮廓检测和图像分割

6.11 交互式图像分割

6.12 视频分割

6.13 相机校准

6.14 同时进行定位和测绘

6.14.1 SLAM community:

6.14.2 定位/测绘:

6.14.3 图优化:

6.14.4 Loop Closure:

6.14.5 Localization & Mapping:

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

6.17 最近邻搜索

6.17.1 通用最近邻搜索

6.17.2 最近邻区域估计

6.18 视觉跟踪

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

6.21 机器学习

7. 数据集

7.1 外部数据集链接集合

7.2 Low-level 视觉

7.2.1 立体视觉

7.7.2 Optical Flow

7.2.3 视频对象分割

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

7.3 Intrinsic Images

7.4 材料识别

7.5 Multi-view 重建

7.6 视觉跟踪

7.7 视觉监测

7.8 变化检测

7.9 视觉识别

7.9.1 图像分类

7.9.2 自监督学习

7.9.3 场景识别

7.9.4 目标检测

7.9.5 语义标签

7.9.6 Multi-view 目标检测

7.9.7 细粒度视觉识别

7.9.8 行人检测

7.10 行动识别

7.10.1 基于视频

7.10.2 图像去模糊化

7.11 Image Captioning

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

8.2 写作

8.3 演讲

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

10. 友情链接