Название: Image Processing and Machine Learning, Volume 1: Foundations of Image Processing
Автор: Erik Cuevas, Alma Nayeli Rodríguez
Издательство: CRC Press
Год: 2024
Страниц: 225
Язык: английский
Формат: pdf (true)
Размер: 40.9 MB

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 first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. It provides a solid foundation for readers interested in understanding the core principles and practical applications of image processing, establishing the essential groundwork necessary for further explorations covered in Volume 2.

Our primary objective was to create a comprehensive textbook that serves as an invaluable resource for an image processing class. With this goal in mind, we carefully crafted a book that encompasses both the theoretical foundations and practical applications of the most prevalent image processing methods. From pixel operations to geometric transformations, spatial filtering to image segmentation, and edge detection to color image processing, we have meticulously covered a wide range of topics essential to understanding and working with images. Moreover, recognizing the increasing relevance of ML in image processing, we have incorporated fundamental ML concepts and their applications in this field. By introducing readers to these concepts, we aim to equip them with the necessary knowledge to leverage ML techniques for various image processing tasks. Our ultimate aspiration is for this book to be a valuable companion for students and practitioners alike, providing them with a solid understanding of image processing fundamentals and empowering them to apply these techniques in real-world scenarios.

Many books on image processing techniques are geared toward readers with a strong mathematical background. Upon reviewing various related books, the authors noticed the need for a more general and less technical approach to these topics to attract a wider audience of readers and students. This book includes all the topics found in other similar books, but with a greater emphasis on explaining, putting into practice, and utilizing the methods, and less emphasis on the mathematical details.

Volume 1 is organized in a way that allows readers to easily understand the goal of each chapter and reinforce their understanding through practical exercises using MATLAB programs.

Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.




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