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Overview: 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. 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. 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.
Genre: Non-Fiction > Tech & Devices
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