Computer Vision

My expertise is in signal-image-video processing, video compression, digital communications, and information theory. I have read many books in all the mentioned areas; Simon Prince's book is one of the best books I have ever read. The book is written beautifully and it is evident that Prince has spent a lot of time in writing the book; I really appreciate his efforts and wish we had more talented book authors like him.I had intermediate knowledge in machine learning and zero knowledge in computer vision when I started reading the Prince's book. All the material are presented in very well organized order. Pictures are excellent and some of them are really masterpiece! In introducing any new topic, the book first gives you a concise intuition about the topic and then presents the mathematical stuff beautifully. Another important aspect of the book is that it never leaves you in the middle of the mathematical arguments and takes you to the end; this is missing in almost all of the advanced-level book I have read. Notation is great and always consistent.I should mention that this book is not written for readers who are not familiar with estimation theory and machine learning. I believe the reader should have an intermediate knowledge in machine learning and estimation theory.

Computer vision is very active field with increasing number of papers being published every year. While the new papers slowly push the knowledge boundary forward, it is often difficult to separate useful information from noise. At the same time, only a few core principles keep repeating over and over again. This book is absolutely brilliant at presenting these principles and mapping them to the already discovered applications in computer vision. This is a connection that I have not found in any other computer vision book available. A connection that allowed me to better understand my own work and to discover new ways forward. I humbly recommend to buy this book to any person seriously interested in computer vision.Dr. Zdenek KalalTLD Vision

This book is my favorite when it comes to probability and computer vision. Much better and more concise than Hartley and Zisserman and much more logically structures than R. Szelinski ones. Chapters 14-16 may be all you need to get a quick intro into Computer Vision. For me it helped to score a job at Google. Last but not least - very intuitive graphs and examples plus a great motivational task in the beginning of every chapter.

I recommend it for start.

The most updated, lucid and complete machine vision book. I liked Trucco and Verri very much due to its completeness and simplicity. But since its kinda old now, this is the best in the market for both beginner and grads.

This book is filled with wonderfully intuitive explanations of key topics backed up by careful formal arguments. The author tells a very convincing Bayesian story about computer vision and makes a clear separation between the models driving the thinking and the concrete algorithmic techniques for realizing and evaluating those models.

Not only this is a great vision book, but also it can be a great introduction to machine learning with applications far beyond vision. The figures make it super-easy to understand rather complex concepts. I highly recommend this book.

It's an excellent book, and view CV from a statistical view.But why I can't download the errata from the author's webpage.Is there any one can help me? e-mail: [email protected].