File Size: 38611 KB
Print Length: 581 pages
Publisher: Cambridge University Press; 1 edition (June 18, 2012)
Publication Date: November 19, 2012
Dr. Zdenek Kalal
TLD Vision, This specific book is my favorite when it comes to probability and computer vision. Much better and more concise than Hartley and Zisserman and many more logically structures than R. Szelinski ones. Chapters 14-16 might be all you want to get a quick intro into Computer Vision. For me it helped to attain a job at Google. Lastly - extremely intuitive graphs and illustrations plus a great motivational task in the beginning of each chapter., Best guide to learn machine learning and computer vision (non strong learning), This book will be filled with wonderfully user-friendly explanations of key topics backed up by cautious formal arguments. The writer tells a really convincing Bayesian story about computer vision and makes an obvious separation between the models driving the thinking and the concrete algorithmic techniques regarding realizing and evaluating all those models., The things i can point out, this is the best book on all the necessary topic of computer vision. Must have read this specific earlier., It's a great guide, and view CV coming from a statistical view.
But why I can't download the errata through the author's webpage.
Is there any 1 can help me? email: ryan153770@163. com
Thanks., Not only this specific is an excellent vision book, nevertheless also it can be a great intro to machine learning along with applications far beyond vision. The figures ensure it is super-easy to understand rather complex concepts. I highly advise this guide., The most up-to-date, lucid and complete machine vision book. I liked Imbroglio and Verri very very much due to its completeness and simplicity. But given that its kinda old today, this is the best in the market for each beginner and grads.
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