• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1149-1155
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• 2012

In this paper, we propose cause diagnosis method of semiconductor defects from semiconductor industrial images. Our method constructs feature database (DB) of defect images. Then, defect and input images are subdivided by uniform block. And the block similarity is measured using histogram kai-square distance after color histogram calculation. Then, searched blocks in each image are merged into connected objects using clustering. Finally, the most similar defect image from feature DB is searched with the defect cause by measuring cluster similarity based on features of each cluster. Our method was validated by calculating the search accuracy of n output images having high similarity. With n = 1, 2, 3, the search accuracy was measured to be 100% regardless of defect categories. Our method could be used for the industrial applications.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1337-1347
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• 2010

Fingerprint recognition is a biometric technology to identify individual by using fingerprint features such ridges and valleys. Most fingerprint systems perform the recognition based on minutiae points after acquiring a fingerprint image from contact type sensor. They have an advantage of acquiring a clear image of uniform size by touching finger on the sensor. However, they have the problems of the image quality can be reduced in case of severely dry or wet finger due to the variations of touching pressure and latent fingerprint on the sensor. To solve these problems, the contactless capturing devices for a fingerprint image was introduced in previous works. However, the accuracy of detecting minutiae points and recognition performance are reduced due to the degradation of image quality by the illumination variation. So, this paper proposes a new LDP-based fingerprint recognition method. It can effectively extract fingerprint patterns of iterative ridges and valleys. After producing histograms of the binary codes which are extracted by the LDP method, chi square distance between the enrolled and input feature histograms is calculated. The calculated chi square distance is used as the score of fingerprint recognition. As the experimental results, the EER of the proposed approach is reduced by 0.521% in comparison with that of the previous LBP-based fingerprint recognition approach.