Fingerprint image enhancement is a key aspect of an automated fingerprint identification system. This paper describes an effective algorithm based on a novel lighting compensation scheme. The scheme involves the use of adaptive higher-order singular value decomposition on a tensor of wavelet subbands of a fingerprint (AHTWF) image to enhance the quality of the image. The algorithm consists of three stages. The first stage is the decomposition of an input fingerprint image of size 2M × 2N into four subbands at the first level by applying a two-dimensional discrete wavelet transform. In the second

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Nowadays, eyeglassesare used for vision correction as well as in the fashion industry. Eyeglasses have become more expensive and the eyewear industry has grown rapidly, thereby requiring the development of advanced coating technologies. However, defect detection by visual inspection in the manufacturing process of eyeglass coatings is difficult. To solve this problem, we propose the coated eyeglass defect detection system framework based on machine vision for real-time inspection. First, we locate and extract regions of interest (ROI) ofthe coated eyeglass by adopting cross-projection based on symmetrizedenergy analysis. Next, we propose an efficient method based on the symmetrized energy analysis of color channelsto enhance defectsin each color channel of the ROI of the coated eyeglass. Then, we adoptsymmetrized cross-projection energy analysis for locating defective areas inside the ROI of the coated eyeglass. Finally, we compare the defect detection resultsfor the coated eyeglass with the standard manufacturingquality. An experiment is conducted using real data collected froma Taiwanese eyeglass factory to validate the performance of the proposed framework. This framework achieves a 100% defect detection rate, demonstrating that it is valid and useful for inspecting coated eyeglasses in industries.

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