I have previously written a reading list for beginners in computer vision. I think that I stepped out step by step without knowing the depth, and made a lot of detours in the middle, so I decided to re-write this reading list and add some recently read articles and books, we also hope to help new users with computer vision.
1. on computer vision:
(1) David Marr, "vision" http://ishare.iask.sina.com.cn/f/6000880.htmlDavid Marr is really a legend in both computer vision and computational neuroscience. although he died in 1982 in only 35 years old, his theory still have great influence in today's vision research. you do not have to read every detail of this book,
Just to find out how great research is and how new ideas are sparkled by interdisciplinary research. (2) Richard szeliski, "Computer Vision: algorithms and Applications" http://szeliski.org/Book/This is the best review I have ever read on current computer vision. you can find its latest draft and supplementary materials on its website. it's reader-friendly, self-contained, comprehensive and most importantly, it's written in 2010. you can easily
Find recent achievements in different sub-areas in computer vision from this book. remember that only reading is not enough, try to Google the related codes and implement the algorithms in opencv or Matlab, otherwise you cannot fully understand them.2. on research methods:
(1) how to do research at the mit ai lab: http://www.cs.umass.edu /~ Emery/MISC/how-to.pdfThis article is a good guide before you start to pursue your PhD, and it's worth being read more than once. A rectified version of Chinese translation can be found at my technical blog: http://blog.csdn.net/scyscyao/archive/2010/12/11/6069401.aspx. (2) You and your research (Richard Hamming) http://www.cs.virginia.edu /~ Robins/youandyourresearch.html "all kinds of things you may be thinking of in research: motivation, luck, intellectual ability, age (FAME and early success), working conditions, commitment and passion, courage, open-mindedness, selling your work; and also possible impediments to success
-I suggest you try to summarize each point while reading, as it contains a lot of pieces. "quoted from http://sfxnus.wordpress.com/2011/05/10/two-general-readings-on-research-methodology-2/3. on Biological vision and mathematics (1) David Hubel, "Eye, brain and vision", http://hubel.med.harvard.edu/Almost all the breakthrough methods in computer vision area are on the mathematical models of Biolog Ical vision system. so it's good to learn some of them. this book is a good beginner's book on human vision system. (2) H. g. adrian. "What does the honeybee see and how do we know?: A Critique of scientific reason ", http://epress.anu.edu.au/honeybee/pdf/whole_book.pdfAn interesting reading on bee vision. you can find that despite its low resolution and poor ability in shape and pattern recognition, bee's vision system is proved to be an effective system for places recognition and navigation. (3) basic mathematics in pattern recognition and machine learning: Very interesting Reading -- Chinese Blog: http://leftnoteasy.cnblogs.com/, has a series of article called "Mathematics in Machine Learning" -- Chinese Blog: http://sites.google.com/site/junwu02/beautyofmathematics, on Google China blog, there's a series of artical called "Beauty of mathematics" the above reading list is my personal recommendation. Please add it. PS: In view of the blog article was arbitrarily cited and did not indicate the source, so the post will add the following copyright statement: This article was published by chengyao in http://blog.csdn.net/scyscyao, this article can be all reproduced or part of the use, but please indicate the source, if there is a problem, please contact scyscyao@gmail.com
