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Wen-Hsing Hsu
Researcher at National Tsing Hua University
Publications - 89
Citations - 1546
Wen-Hsing Hsu is an academic researcher from National Tsing Hua University. The author has contributed to research in topics: Parallel algorithm & Digital watermarking. The author has an hindex of 21, co-authored 89 publications receiving 1499 citations. Previous affiliations of Wen-Hsing Hsu include Industrial Technology Research Institute & Academia Sinica.
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Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes
TL;DR: Based on 2-D cluster approach, a fast algorithm for point pattern matching is proposed to effectively solve the problems of optimal matches between two point pattern under geometrical transformation and correctly identify the missing or spurious points of patterns.
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A modified fast parallel algorithm for thinning digital patterns
Yung-Sheng Chen,Wen-Hsing Hsu +1 more
TL;DR: A modified version of the fast parallel thinning algorithm proposed by Zhang and Suen is presented, which preserves the original merits such as the contour noise immunity and good effect in thinning crossing lines; and overcomes the original demerits.
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A Video Watermarking Technique Based on Pseudo-3-D DCT and Quantization Index Modulation
TL;DR: An effective video watermarking method based on a pseudo-3-D discrete cosine transform (DCT) and quantization index modulation (QIM) against several attacks is proposed that can survive filtering, compressions, luminance change, and noise attacks with a good invisibility and robustness.
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Recognition of handwritten Chinese characters by modified Hough transform techniques
TL;DR: Results are presented for two experiments conducted for a database called ETL8, which contains 881 Chinese characters and 160 variations for each one, to prove the usefulness of the MHT and DP matching methods.
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Accurate optical flow computation under non-uniform brightness variations
TL;DR: A generalized dynamic image model (GDIM) in conjunction with a regularization framework is based on, based on a reweighted least-squares method, to suppress unreliable flow constraints, thus leading to robust estimation of optical flow.