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 3 ( From Pattern to Object ) examines object recognition, neural networks, motion analysis, and 3D reconstruction of a scene. 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.
