Advanced Calculus with Applications in Statistics

the algorithms under various conditions and settings. The total amount of code consists of about 25,000 lines and even includes a graphical routine based on the X Windows system. The code itself is reasonably well documented and thus is amenable to extension and further development by the interested reader. The datasets presented in the book are also included in the tar archive. So what is the use for 2D Object Detection and Recognition? The book seems to be intended primarily for those researchers in computer vision and related fields who would like to see the world though Amit’s eyes. The book requires some background knowledge of computer vision (not much, though) and is reasonably self-contained. It would also work very well in a more-advanced study group setting, where several students can work their way through the chapters, making extensive use of the available software. I congratulate Yali Amit on writing a very fine book based on his many years of research experience that led to excellent and novel contributions to the field. This book is an example of how to do things right: simply, efficiently, and correctly. I have no reservations at all in recommending the book highly to all readers interested in the field of computer vision and/or (really) applied statistics.