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2 Machine Graphics and Vision

2 Tominaga S.

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Material Classification Method for Printed Circuit Boards Using a Spectral Imaging System

Tominaga S.

Machine Graphics and Vision

2009

Vol. 18, No. 2

233-250

This paper proposes a method for classifying object materials on a raw circuit board into element materials by means of surface-spectral reflectance. First, we develop a spectral imaging system for observing the minute details of the board and capturing their spectral data. Second, the surface-spectral reflectance functions of the board are estimated by a direct method using narrow band sensor outputs. We investigate the reflection properties of various objects on the board under different illumination directions. Third, we find key features of the body spectral reflectances for different materials, and present a rule for classifying the objects into six element materials. Finally, experiments are executed using a real circuit board. The observed spectral reflectance image is segmented into the element material areas. The performance and robustness of the proposed method are examined in detail in comparison with other methods.

Object recognition using a multi-spectral imaging system

Tominaga S.

Machine Graphics and Vision

2002

Vol. 11, No. 2/3

221-240

A spectral-imaging system and algorithms for identifying obects in a natural scene based on surface-spectral reflectances are described. The imaging system in composed of a liquid-crystals tunable filter, a monochrome CCD camera, and a personal computer. The tunable filter is convenient for spectral imaging because the wavelenght band can ce changed easily and electronically. It is described how we can recover the surface-spectral refle tances of natural objects by using the multi-spectral imaging sysems. Algorithms are presented for estimating both spectral functions of the illuminant spectral--power distribution and suface-spectral reflectance from the spectral image data. Moreover, effective image processing procedures are proposed for highlight extraction and region segmentation. The segmentation is based on a pixel classification method using only the maximum sensor outputs. The overall performance of the proposed imaging system and algorithms is examined in an experiment using natural products, in which 21 spectral images are acquired in the wavelenght range of 450-650 nm.