Stanford: Cengage Learning, 2015. — 930 p.Image processing, analysis, and machine vision are an exciting and dynamic part of cognitive and computer science. Following an explosion of interest during the 1970s and 1980s, subsequent decades were characterized by a maturing of the field and significant growth of active applications; remote sensing, technical diagnostics, autonomous vehicle guidance, biomedical imaging (2D, 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, each of which depends on a sophisticated chain of embedded consumer-invisible image processing steps performed in real time, is strik ing. Reflecting this continuing development, the number of digital image processing and machine vision courses offered at universities worldwide continues to increase rapidly. There are many texts available in the areas we cover—a lot of them are referenced 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 fourth 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. Our target audience spans the range from the undergraduate with negligible experience in the area through to the Master’s, Ph.D., and research student seeking an advanced springboard in a particular topic. The entire text has been updated since the third version (particularly with respect to most recent development and associated references). We retain the same Chapter structure, but many sections have been rewritten or introduced as new. Among the new topics are the Radon transform, a unified approach to image/template matching, efficient object skeletonization (MB and MB2 lgorithms), nearest neighbor classification including BBF/FLANN, histogram-of-oriented-Gaussian (HOG) approach to object detection, random forests, Markov random fields, Bayesian belief networks, scale invariant feature transform (SIFT), recent 3D image analysis/vision development, texture description using local binary patterns, and several point tracking approaches for motion analysis. Approaches to 3D vision evolve especially quickly and we have revised this material and added new comprehensive examples. In addition, several sections have been rewritten or expanded in response to reader and reviewer comments. All in all, about 15% of this edition consists of newly written material presenting state of-the-art methods and techniques that already have proven their importance in the field: additionally, the whole text has been edited for currency and to correct a small number of oversights detected in the previous edition. 4th edition.
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