Handbook of Image Processing and Computer Vision : Volume 2: From Image to Pattern

Across three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 2 (From Image to Pattern) examines image transforms, image restoration, and image segmentation.
Topics and features:

Describes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy
Covers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal
Discusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image
Reviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration
Presents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest
Examines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration
Provides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks

This all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT/software and electronics industries involved in machine vision, imaging, and artificial intelligence.