A Tiered-Color Illumination Approach for Machine Inspection of Solder Joints
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Surface Identification Using the Dichromatic Reflection Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition by Multi-Spectral Imaging with a Liquid Crystal Filter
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Reflectance-Based Material Classification for Printed Circuit Boards
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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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.