by R. Jain, R. Kasturi, B. G. Schunck
Publisher: McGraw-Hill 1995
Number of pages: 549
This text is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithm in practical application are provided, and engineering aspects of techniques are emphasized. This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time.
Home page url
Download or read it online for free here:
(multiple PDF files)
by Adrian Horridge - ANU E Press
The book is the only account of what the bee actually detects with its eyes. The erratic path to understanding makes interesting reading for anyone with an analytical mind who thinks about the methods of science or the engineering of seeing machines.
by David Marshall - Cardiff School of Computer Science
From the table of contents: Image Acquisition: 2D Image Input, 3D imaging; Image processing: Fourier Methods, Smoothing Noise; Edge Detection; Edge Linking; Segmentation; Line Labelling; Relaxation Labelling; Optical Flow; Object Recognition.
by Dana H. Ballard, Christopher M. Brown - Prentice Hall
The book on computer vision - the construction of explicit, meaningful descriptions of physical objects from images. Parts of the book assume some mathematical and computing background, but mainly mathematical rigor takes a backseat to concepts.
by Bruce G. Batchelor - Springer-Verlag
The author introduces the basic concepts of machine vision, then develops these ideas to describe intelligent imaging techniques for use in a new generation of industrial imaging systems. Several case studies in industrial applications are discussed.