CV2017Spring - 2017年春季学期《计算机视觉导论》（研究生）

Class Time and Location 上课时间和地点

• 周四 234节（上午09:00-12:00） 中国海洋大学崂山校区3503教室

TextBook 参考书

• Digital Image Processing (3rd edition), by Rafael Gonzalez and Richard Woods, Prentice Hall, 2008.
• Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010.
• 《机器学习》，周志华，清华大学出版社，2016年。

Grading Policy 成绩构成

总成绩 ${G}_{total}={G}_{class}\times 20\% + {G}_{assignments}\times 80\% + {G}_{award} - {G}_{penalty} - {G}_{absence}$

• 平时表现成绩 $G_{class}$：课堂积极参与（Active class and participation）
• 任务表现成绩 $G_{assignments}$：科研学术写作（Problem Sets: Programming and Writeup）
• 优异表现加分 $G_{award}$：主动前沿推介、论文宣读、算法演示等
• 不良表现扣分 $G_{penalty}$：课上和平台不良表现等 惩罚学生名单
• 缺勤扣分 $G_{absence}$：
  • 无故 缺勤一次：${G}_{absence}=10$ 一次缺勤学生名单
  • 无故 缺勤二次：${G}_{absence}=30$ 二次缺勤学生名单
  • 无故 缺勤三次：${G}_{absence}=60$

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Syllabus and Lecture Notes 教学大纲和课堂讲义

All the resources are available from GitHub: https://github.com/zhenglab/2017SpringCV

任务要求：8次任务任选4次完成科研学术写作，其中至少2次需要全部采用英文撰写。

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Sources 相关资源

• Teaching at Center for Research in Computer Vision of University of Central Florida, by Mubarak Shah.
• Teaching at Computer Graphics, Vision, Animation, and Game Science of University of Washington, by Steven Seitz.
• Teaching at Vision Lab of Stanford University, directed by Fei-Fei Li.
• Teaching at UT-Austin Computer Vision Group of University of Texas at Austin, directed by Kristen Grauman.
• Teaching at Computer Science and Artificial Intelligence Laboratory of Massachusetts Institute of Technology, by Antonio Torralba.
• Teaching at Graphics, Visualization, and Interaction Group of Brown University, by James Hays.
• Teaching at Computational Vision & Geometry Lab of Stanford University, directed by Silvio Savarese.
• Teaching at Serre Lab of Brown University, directed by Thomas Serre.
• Teaching at Vision & Learning Lab of University of Wisconsin Milwaukee, directed by Ali Borji.
• Leading Journals and Conferences in Computer Vision
  • International Journal of Computer Vision (IJCV)
  • Pattern Analysis and Machine Intelligence (PAMI)
  • IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • International Conference on Computer Vision (ICCV)

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Tools 工具

• OpenCV: Open Source Computer Vision
• VLFeat: Vision Lab Features Library
• scikit-image: Image Processing in Python
• scikit-learn: Machine Learning in Python
• Caffe: Deep Learning Framework
• MXNet: Flexible and Efficient Library for Deep Learning
• TensorFlow: An open-source software library for Machine Intelligence

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Contact 联系方式

• 郑海永 zhenghaiyong@gmail.com

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Updated: Jun. 5, 2017	Home / Index	Edited by Haiyong Zheng