Image Processing, Analysis, and Machine Vision: Second Edition

Author: Sang Ke
Publisher:
Publish Date: 2003-09-01
Features: This book can be used as a textbook for upper-level undergraduate and graduate students in computer science at universities, allowing them to study relevant chapters based on actual situations. It is also particularly suitable for readers with some foundational knowledge to self-study. The book offers high reference value for researchers and engineering technicians in related scientific and technological fields. Additionally, professionals in this field can use it alongside the English edition as a technical manual. This book is written as a textbook for image processing, image analysis, and machine vision courses in computer science programs and has been adopted by universities such as Carnegie Mellon University in the U.S. The book covers topics including image preprocessing, image segmentation, shape representation and description, object recognition and image understanding, three-dimensional vision, mathematical morphology image processing techniques, discrete image transforms, image compression, texture description, motion analysis, and case studies of practical applications, among others. The book strives to present complex concepts in easily understandable algorithmic descriptions, with numerous illustrations and diagrams that are particularly helpful for understanding the concepts. Each chapter also includes a wealth of review questions, exercises, and a comprehensive list of references to facilitate learning and comprehension. The concepts and principles explained in this book cover a wide range of fields, involving artificial intelligence (e.g., heuristic search), signal processing (e.g., convolution, Fourier transform), artificial neural networks, pattern recognition, fuzzy mathematics, and a series of related disciplines. Through studying this book, readers can acquire many universally valuable knowledge and practical application methods in these fields.