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 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, the first instalment explores the fundamental concepts and techniques in image processing, starting from pixel operations and their properties, exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. The second instalment 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 (SVD) for image compression.
Written with instructors and students of image processing in mind, this book’s intuitive organisation also contains appeal for app developers and engineers.
Contents
Book I
Preface Volume 1
Chapter 1 Pixel Operations
Chapter 2 Spatial Filtering
Chapter 3 Edge Detection
Chapter 4 Segmentation and Processing of Binary Images
Chapter 5 Corner Detection
Chapter 6 Line Detection
Book II
Preface Volume II
Chapter 1 Morphological Operations
Chapter 2 Color Images
Chapter 3 Geometric Operations in Images
Chapter 4 Comparison and Recognition of Images
Chapter 5 Mean Shift Algorithm for Segmentation
Chapter 6 Singular Value Decomposition (SVD) in Image Processing
