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Chi-Man Pun
Researcher at University of Macau
Publications - 270
Citations - 5090
Chi-Man Pun is an academic researcher from University of Macau. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 29, co-authored 224 publications receiving 3524 citations. Previous affiliations of Chi-Man Pun include University UCINF & The Chinese University of Hong Kong.
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2D Sine Logistic modulation map for image encryption
TL;DR: A new two-dimensional Sine Logistic modulation map (2D-SLMM) which is derived from the Logistic and Sine maps is introduced which has the wider chaotic range, better ergodicity, hyperchaotic property and relatively low implementation cost.
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Log-polar wavelet energy signatures for rotation and scale invariant texture classification
Chi-Man Pun,Moon-Chuen Lee +1 more
TL;DR: The proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, and its robustness to noise also performs better than the other methods.
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Cascade Chaotic System With Applications
TL;DR: This paper introduces a general chaotic framework called the cascade chaotic system (CCS), and introduces a pseudo-random number generator (PRNG) and a data encryption system using a chaotic map generated by CCS.
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Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching
TL;DR: The proposed forgery region extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions to detect the detected forgery regions.
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Complex Zernike Moments Features for Shape-Based Image Retrieval
TL;DR: The proposed IZMD feature is, in general, robust to changes caused by image shape rotation, translation, and/or scaling and outperforms the commonly used magnitude-only ZMD in terms of noise robustness and object discriminability.