全球最全计算机视觉资料（1：入门学习|课程|综述|图书|期刊会议）

目标检测和深度学习

入门学习

1. 计算机视觉：让冰冷的机器看懂这个多彩的世界 by 孙剑
   • [http://www.msra.cn/zh-cn/news/features/computer-vision-20150210]
2. UCLA朱松纯: 正本清源·初探计算机视觉的三个源头、兼谈人工智能
   • [https://mp.weixin.qq.com/s/2ytV5Bt50yhYOFYXYQe6ZQ]
3. 深度学习与视觉计算 by 王亮 中科院自动化所
   • [http://www.caai.cn/index.php?s=/Home/Article/qikandetail/year/2017/month/04.html]
4. 如何做好计算机视觉的研究？ by 微软 华刚博士
   • [http://www.msra.cn/zh-cn/news/features/do-research-in-computer-vision-20161205]
5. 计算机视觉 微软亚洲研究院系列文章
   • 通俗介绍计算机视觉在生活中的各种应用。
   • [http://www.msra.cn/zh-cn/research/computer-vision]
6. 计算机视觉随谈
   • [http://blog.csdn.net/zouxy09/article/details/38639349]
7. 计算机视觉：就在你我身边 微软
   • [https://mp.weixin.qq.com/s/rgvQeW9CwswbmcAI4BISNQ]
8. 什么是计算机视觉？什么是机器视觉？
   • [https://mp.weixin.qq.com/s/PVom2BwEUXw3z68cra9xNQ]
9. 卷积神经网络如何进行图像识别
   • [http://www.infoq.com/cn/articles/convolutional-neural-networks-image-recognition]
10. 相似图片搜索的原理 阮一峰
    • [http://www.ruanyifeng.com/blog/2011/07/principle_of_similar_image_search.html\]
11. 如何识别图像边缘？ 阮一峰
    • [http://www.ruanyifeng.com/blog/2016/07/edge-recognition.html]
12. 图像目标检测（Object Detection）原理与实现 （1-6）
    • [http://www.voidcn.com/article/p-xnjyqlkj-ua.html]
13. 运动目标跟踪系列（1-17）
    • [http://blog.csdn.net/App_12062011/article/category/6269524/1]
14. 看图说话的AI小朋友——图像标注趣谈(上，下)
    • [https://zhuanlan.zhihu.com/p/22408033]
    • [https://zhuanlan.zhihu.com/p/22520434]
15. Video Analysis 相关领域介绍之Video Captioning(视频to文字描述)
    • [https://zhuanlan.zhihu.com/p/26730181]
16. 从特斯拉到计算机视觉之「图像语义分割」
    • [https://zhuanlan.zhihu.com/p/21824299]
17. 计算机视觉识别简史：从 AlexNet、ResNet 到 Mask RCNN
    • [https://github.com/Nikasa1889/HistoryObjectRecognition]
    • [https://mp.weixin.qq.com/s/ZKMi4gRfDRcTxzKlTQb-Mw]
    • [https://github.com/Nikasa1889/HistoryObjectRecognition/blob/master/HistoryOfObjectRecognition%20-%20A0.pdf]
18. 深度学习在计算机视觉领域的前沿进展
    • [https://zhuanlan.zhihu.com/p/24699780]
19. 深度学习时代的计算机视觉
    • [https://mp.weixin.qq.com/s/gExfzCxjHrSb7afn33f-lA]
20. 视觉求索 公众号相关文章系列，
    • 浅谈人工智能：现状、任务、构架与统一 | 正本清源 [http://mp.weixin.qq.com/s/-wSYLu-XvOrsST8_KEUa-Q]
    • 人生若只如初见 | 学术人生 [https://mp.weixin.qq.com/s/kFA7bI_FFjZQkBNDvcn01g]
    • 初探计算机视觉的三个源头、兼谈人工智能｜正本清源 [https://mp.weixin.qq.com/s/2ytV5Bt50yhYOFYXYQe6ZQ]
21. 深度学习大讲堂 公众号相关文章系列
    • 深度学习在目标跟踪中的应用 [https://zhuanlan.zhihu.com/p/22334661]
    • 深度学习在图像取证中的进展与趋势 [https://zhuanlan.zhihu.com/p/23341157]
    • 行人检测、跟踪与检索领域年度进展报告 [https://zhuanlan.zhihu.com/p/26807041]
    • 基于深度学习的目标检测研究进展 [https://zhuanlan.zhihu.com/p/21412911]
    • 基于深度学习的视觉实例搜索研究进展 [https://zhuanlan.zhihu.com/p/22265265]
    • 基于深度学习的VQA（视觉问答）技术 [https://zhuanlan.zhihu.com/p/22530291]
    • 人脸识别简史与近期进展 [https://zhuanlan.zhihu.com/p/21465605]
    • 边缘检测领域年度进展报告 [https://zhuanlan.zhihu.com/p/26848831]
    • 目标跟踪领域进展报告 [https://zhuanlan.zhihu.com/p/27293523]

课程

1. 斯坦福视觉实验室主页：http://vision.stanford.edu/ 李飞飞组CS131, CS231A, CS231n 三个课程，可是说是最好的计算机视觉课程。
2. CS 131 Computer Vision: Foundations and Applications： 基础知识：主要讲传统的边缘检测，特征点描述，相机标定，全景图拼接等知识 [http://vision.stanford.edu/teaching/cs131_fall1415/schedule.html]
3. CS231A Computer Vision: from 3D reconstruction to recognition: [http://cvgl.stanford.edu/teaching/cs231a_winter1415/schedule.html]
4. CS231n 2017: Convolutional Neural Networks for Visual Recognition 主要讲卷积神经网络的具体结构，各组成部分的原理优化以及各种应用。 [http://vision.stanford.edu/teaching/cs231n/] 国内地址：[http://www.bilibili.com/video/av13260183/]
5. Stanford CS231n 2016 : Convolutional Neural Networks for Visual Recognition
   • homepage: [http://cs231n.stanford.edu/]
   • homepage: [http://vision.stanford.edu/teaching/cs231n/index.html]
   • syllabus: [http://vision.stanford.edu/teaching/cs231n/syllabus.html]
   • course notes: [http://cs231n.github.io/]
   • youtube: [https://www.youtube.com/watch?v=NfnWJUyUJYU&feature=youtu.be]
   • mirror: [http://pan.baidu.com/s/1pKsTivp]
   • mirror: [http://pan.baidu.com/s/1c2wR8dy]
   • 网易中文字幕：[http://study.163.com/course/introduction/1003223001.htm]
   • assignment 1: [http://cs231n.github.io/assignments2016/assignment1/]
   • assignment 2: [http://cs231n.github.io/assignments2016/assignment2/]
   • assignment 3: [http://cs231n.github.io/assignments2016/assignment3/]
6. 1st Summer School on Deep Learning for Computer Vision Barcelona: (July 4-8, 2016)
   • youtube: [https://www.youtube.com/user/imatgeupc/videos?shelf_id=0&sort=dd&view=0]
   • 深度学习计算机视觉夏季学校课程， 包含基础知识以及许多深度学习在计算机视觉中的应用，比如分类，检测，captioning等等
   • homepage(slides+videos): [http://imatge-upc.github.io/telecombcn-2016-dlcv/]
   • homepage: [https://imatge.upc.edu/web/teaching/deep-learning-computer-vision]
7. 2nd Summer School on Deep Learning for Computer VisionBarcelona (June 21-27, 2017) [https://telecombcn-dl.github.io/2017-dlcv/]

综述

1. Annotated Computer Vision Bibliography: Table of Contents. Since 1994 Keith Price从1994年开始做了这个索引，涵盖了所有计算机视觉里面所有topic，所有subtopic的著作，包括论文，教材，还对各类主题的关键词。这个网站频繁更新（最近一次是2017年8月28号），收录每个方向重要期刊，会议文献和书籍，并且保证了所有链接不失效。
2. What Sparked Video Research in 1877? The Overlooked Role of the Siemens Artificial Eye by Mark Schubin 2017 [http://ieeexplore.ieee.org/document/7857854/]
3. Giving machines humanlike eyes. by Posch, C., Benosman, R., Etienne-Cummings, R. 2015 [http://ieeexplore.ieee.org/document/7335800/]
4. Seeing is not enough by Tom GellerOberlin, OH [https://dl.acm.org/citation.cfm?id=2001276]
5. Visual Tracking: An Experimental Survey [https://dl.acm.org/citation.cfm?id=2693387]
6. A survey on object recognition and segmentation techniques [http://ieeexplore.ieee.org/document/7724975/]
7. A Review of Image Recognition with Deep Convolutional Neural Network [https://link.springer.com/chapter/10.1007/978-3-319-63309-1_7\]
8. Recent Advance in Content-based Image Retrieval: A Literature Survey. Wengang Zhou, Houqiang Li, and Qi Tian 2017 [https://arxiv.org/pdf/1706.06064.pdf]
9. Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures 2016 [https://www.jair.org/media/4900/live-4900-9139-jair.pdf]

Turorial

1. Intro to Deep Learning for Computer Vision 2016 [http://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/]
2. CVPR 2014 Tutorial on Deep Learning in Computer Vision [https://sites.google.com/site/deeplearningcvpr2014/]
3. CVPR 2015 Applied Deep Learning for Computer Vision with Torch [https://github.com/soumith/cvpr2015]
4. Deep Learning for Computer Vision – Introduction to Convolution Neural Networks [http://www.analyticsvidhya.com/blog/2016/04/deep-learning-computer-vision-introduction-convolution-neural-networks/]
5. A Beginner's Guide To Understanding Convolutional Neural Networks [https://adeshpande3.github.io/adeshpande3.github.io/A-Beginners-Guide-To-Understanding-Convolutional-Neural-Networks/']
6. CVPR'17 Tutorial Deep Learning for Objects and Scenes by Kaiming He Ross Girshick [http://deeplearning.csail.mit.edu/]
7. CVPR tutorial : Large-Scale Visual Recognition [http://www.europe.naverlabs.com/Research/Computer-Vision/Highlights/CVPR-tutorial-Large-Scale-Visual-Recognition]
8. CVPR’16 Tutorial on Image Tag Assignment, Refinement and Retrieval [http://www.lambertoballan.net/2016/06/cvpr16-tutorial-image-tag-assignment-refinement-and-retrieval/]
9. Tutorial on Answering Questions about Images with Deep Learning The tutorial was presented at '2nd Summer School on Integrating Vision and Language: Deep Learning' in Malta, 2016 [https://arxiv.org/abs/1610.01076]
10. “Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial [ https://www.youtube.com/watch?v=pQ318oCGJGY]
11. A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the "echo state network" approach [http://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf] [http://deeplearning.cs.cmu.edu/notes/shaoweiwang.pdf]
12. Towards Good Practices for Recognition & Detection by Hikvision Research Institute. Supervised Data Augmentation (SDA) [http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf]
13. Generative Adversarial Networks by Ian Goodfellow, NIPS 2016 tutorial [ https://arxiv.org/abs/1701.00160] [http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf]
14. Deep Learning for Computer Vision – Introduction to Convolution Neural Networks [http://www.analyticsvidhya.com/blog/2016/04/deep-learning-computer-vision-introduction-convolution-neural-networks/]

图书

1. 两本经典教材《Computer Vision: A Modern Approach》和《Computer Vision: Algorithms and Applications》，可以先读完第一本再读第二本。
2. Computer Vision: A Modern Approach by David A. Forsyth, Jean Ponce 英文：[http://cmuems.com/excap/readings/forsyth-ponce-computer-vision-a-modern-approach.pdf] 中文：[https://pan.baidu.com/s/1min99eK]
3. Computer Vision: Algorithms and Applications by Richard Szeliski 英文：[http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf\] 中文：[https://pan.baidu.com/s/1mhYGtio]
4. Computer Vision: Models, Learning, and Inference by Simon J.D. Prince 书的主页上还有配套的Slider, 代码，tutorial，演示等各种资源。 [http://www.computervisionmodels.com/]

相关期刊与会议

国际会议

1. CVPR, Computer Vision and Pattern Recognition CVPR 2017：[http://cvpr2017.thecvf.com/]
2. ICCV, International Conference on Computer Vision ICCV2017：[http://iccv2017.thecvf.com/]
3. ECCV, European Conference on Computer Vision
4. SIGGRAPH, Special Interest Group on Computer Graphics and Interactive techniques SIGGRAPH2017：[http://s2017.siggraph.org/]
5. ACM International Conference on Multimedia ACMMM2017：[http://www.acmmm.org/2017/]
6. ICIP, International Conference on Image Processing [http://2017.ieeeicip.org/]

期刊

1. ACM Transactions on Graphics, TOG
2. International Journal of Computer Vision, IJCV
3. IEEE Trans on Pattern Analysis and Machine Intelligence, TPAMI
4. IEEE Transactions on Image Processing, TIP
5. IEEE Transactions on Visualization and Computer Graphics, TVCG
6. IEEE Communications Surveys and Tutorials
7. IEEE Signal Processing Magazine
8. IEEE Transactions on EVOLUTIONARY COMPUTATION
9. IEEE Transactions on GEOSCIENCE and REMOTE SENSING 2区
10. IEEE Transactions on Pattern Analysis and Machine Intelligence
11. NEUROCOMPUTING 2区
12. Pattern Recognition Letters 2区
13. Proceedings of the IEEE
14. Signal image and Video Processing 4区
15. IEEE journal on Selected areas in Communications 2区
16. IEEE Transactions on image Processing 2区
17. journal of Visual Communication and image Representation 3区
18. Machine Vision and Application 3区
19. Pattern Recognition 2区
20. Signal Processing-image Communication 3区
21. COMPUTER Vision and image UNDERSTANDING 3区
22. IEEE Communications Surveys and Tutorials
23. IET image Processing 4区
24. Artificial Intelligence 2区
25. Machine Learning 3区
26. Medical image Analysis 2区

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