Image Processing, Analysis and Machine Vision

Купить бумажную книгу и читать

Автор: Sonka M., Hlavac V., Boyle R.

Название: Image Processing, Analysis and Machine Vision

Издательство: Thomson

Год: 2008

Формат: PDF

Размер: 151.36mb

язык: английский

Для сайта:

Image processing, analysis, and machine vision represent an exciting and dynamic part of cognitive and computer science. Following an explosion of interest during the 1970s and the 1980s, the last three decades were characterized by a maturing of the field and significant growth of active applications; remote sensing, technical diagnostics, autonomous vehicle guidance, biomedical imaging BD, 3D, and 4D) and automatic surveillance are the most rapidly developing areas. This progress can be seen in an increasing number of software and hardware products on the market—as a single example of many, the omnipresence of consumer-level digital cameras is striking. Reflecting this continuing development, the number of digital image processing and machine vision courses offered at universities worldwide is increasing rapidly.

There are many texts available in the areas we cover—many of them are referenced somewhere in this book. The subject suffers, however, from a shortage of texts which are 'complete' in the sense that they are accessible to the novice, of use to the educated, and up to date. Here we present the third edition of a text first published in 1993. We include many of the very rapid developments that have taken and are still taking place, which quickly age some of the very good textbooks produced in the recent past.

The target audience spans the range from the undergraduate with negligible experience in the area through to the Master's and research student seeking an advanced springboard in a particular topic. Every section of this text has been updated since the second version (particularly with respect to references). Chapters 2 and 3 were reorganized and enhanced to better present a broad yet not overwhelming foundation, which is used throughout the book. While the second edition published in 1998 provided a comprehensive treatment of 2D image processing and analysis, analysis of volumetric and thus inherently 3D image data has become a necessity. To keep up with the rapidly advancing field, a brand new Chapter 7 covers image segmentation methods and approaches with 3D (or higher dimension) capabilities such as mean shift segmentation, gradient vector flow snakes, level sets, direct graph cut segmentation, and optimal single and multiple surface detection. As a result, the book now has two chapters devoted to segmentation, clearly reflecting the importance of this area.