A Neural-Learning-Algorithm-Based Shape from Shading System

Author

Gao, Yue-Fang ; Luo, Fei ; Cao, Jian-zhong

Author_Institution

Coll. of Autom., South China Univ. of Technol., Guangzhou

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3991

Lastpage

3995

Abstract

This paper introduces a shape from shading (SFS) system, which is based on a neural learning algorithm. The system takes a single image of an object with a CCD camera and then reconstructs the object surface with a neural-learning-based SFS algorithm. This SFS algorithm solves and optimizes the neural elements, named network weights, by minimizing the cost function that is composed of the intensity constraint and the integrability constraint. This neural-learning-based SFS algorithm can provide a promising effectiveness and accuracy. Moreover, the reconstructed surface of this system can be applied in the area of surface measurement and defect detection

Keywords

CCD image sensors; image reconstruction; learning (artificial intelligence); neural nets; CCD camera; cost function minimization; integrability constraint; intensity constraint; neural-learning-algorithm-based SFS system; object surface reconstruction; shape from shading system; Charge coupled devices; Charge-coupled image sensors; Cost function; Cybernetics; Image reconstruction; Inspection; Machine learning; Machine vision; Magnetic field measurement; Reflectivity; Shape; Shape measurement; Surface reconstruction; Ultrasonic variables measurement; 3D reconstruction; CCD camera; Lambertian reflectance model; Neural learning algorithm; Shape from shading;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258797

Filename

4028770