Machine Vision : Theory, Algorithms, Practicalities
Machine vision may be defined as the automatic analysis of images by computer with the aim of controlling machines and monitoring real-world processes. This edition contains material on artificial neural networks, mathematical morphology, motion, invariance, texture analysis, x-ray inspection and foreign object detection. Intermediate level vision is examined in depth (especially Hough transforms), and automated visual inspection is discussed. The author considers theoretical aspects as well as practical applications, including perspective invariants and robust statistics.
- Paperback | 768 pages
- 150.88 x 229.62 x 34.54mm | 1,183.87g
- 01 Nov 1996
- Elsevier Science Publishing Co Inc
- Academic Press Inc
- San Diego, United States
- 2nd Revised edition
- 185 b&w illustrations, index
Table of contents
Introduction - vision, the challenge. Part 1 Low-level processing: images and imaging operations; basic image filtering operations; thresholding techniques; locating objects via their edges; binary shape analysis; boundary pattern analysis. Part 2 Intermediate-level processing: line detection; circle detection; the Hough transform and its nature; ellipse detection; hole detection; polygon and corner detection. Part 3 Application level processing: abstract pattern matching techniques; the three-dimensional world; tackling the pespective n-point problem; motion; invariants and their applications; automated visual inspection; statistical pattern recognition; biologically inspired recognition schemes; texture; image acquisition; the need for speed - real-time electronic hardware systems. Part 4 Perspectives on vision: machine vision, art or science?.
About R. Davies
By E. R. Davies