Shape and surface measurement technology by an improved shape-from-shading neural algorithm

Author

Cho, Siu-Yeung ; Chow, Tommy W S

Author_Institution

Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong

Volume

47

Issue

1

fYear

2000

fDate

2/1/2000 12:00:00 AM

Firstpage

225

Lastpage

230

Abstract

A new approach for measuring the shape and surface of an object observed from a single camera is proposed. The proposed approach is based on using the neural networks as a parametric representation of the three-dimensional object and the shape-from-shading problem is formulated as the minimization of an intensity error function with respect to the network weights. Experimental results demonstrate that the authors´ proposed methodology exhibits high efficiency and accuracy for measuring and inspecting a product´s surface in the manufacturing industry

Keywords

image processing; learning (artificial intelligence); neural nets; shape measurement; surface topography measurement; accuracy; efficiency; intensity error function minimisation; manufacturing industry; network weights; parametric representation; product surface inspection; shape measurement technology; shape-from-shading neural algorithm; surface measurement technology; three-dimensional object; Capacitors; Circuit simulation; Conferences; DC-DC power converters; Impedance; Neural networks; Resonance; Shape measurement; Stability; Surface finishing;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/41.824148

Filename

824148