Fundamentals Of Digital Image Processing Annadurai

Fundamentals of Digital Image Processing

Digital Image Processing and Pattern Recognition

This book is designed for undergraduate and postgraduate students of Computer Science and Engineering, Information Technology, Electronics and Communication Engineering, and Electrical Engineering. The book comprehensively covers all the important topics in digital image processing and pattern recognition along with the fundamental concepts, mathematical preliminaries and theoretical derivations of significant theorems. The image processing topics include coverage of image formation, digitization, lower level processing, image analysis, image compression, and so on. The topics on pattern recognition include statistical decision making, decision tree learning, artificial neural networks, clustering and others. An application of simulated annealing for edge detection is described in an appendix. The book is profusely illustrated with more than 200 figures and sketches as an added feature. KEY FEATURES: Provides a large number of worked examples to strengthen the grasp of the concepts. Lays considerable emphasis on the algorithms in order to teach students how to write good practical programs for problem solving. Devotes a separate chapter to currently used image format standards. Offers problems at the end of each chapter to help students test their understanding of the fundamentals of the subject.

Image Processing and Machine Learning, Volume 2

Image processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches. Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important machine learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1. Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.