L
Lai-Man Po
Researcher at City University of Hong Kong
Publications - 207
Citations - 6191
Lai-Man Po is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Motion estimation & Search algorithm. The author has an hindex of 33, co-authored 199 publications receiving 5608 citations. Previous affiliations of Lai-Man Po include Hong Kong Applied Science and Technology Research Institute & University of Hong Kong.
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A novel four-step search algorithm for fast block motion estimation
Lai-Man Po,Wing-Chung Ma +1 more
TL;DR: Simulation results show that the proposed 4SS performs better than the well-known three- step search and has similar performance to the new three-step search (N3SS) in terms of motion compensation errors.
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A novel cross-diamond search algorithm for fast block motion estimation
Chun-Ho Cheung,Lai-Man Po +1 more
TL;DR: The proposed cross-diamond search (CDS) algorithm employs the halfway-stop technique and finds small motion vectors with fewer search points than the DS algorithm while maintaining similar or even better search quality.
Journal ArticleDOI
Integration of image quality and motion cues for face anti-spoofing
TL;DR: An extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection.
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Normalized partial distortion search algorithm for block motion estimation
Chok-Kwan Cheung,Lai-Man Po +1 more
TL;DR: A novel fast block-matching algorithm named normalized partial distortion search is proposed, which reduces computations by using a halfway-stop technique in the calculation of the block distortion measure and normalized the accumulated partial distortion and the current minimum distortion before comparison.
Journal ArticleDOI
Novel cross-diamond-hexagonal search algorithms for fast block motion estimation
Chun-Ho Cheung,Lai-Man Po +1 more
TL;DR: Two cross-diamond-hexagonal search algorithms, which differ from each other by their sizes of hexagonal search patterns, are proposed, which show that the proposed CDHSs perform faster than the diamond search (DS) by about 144% and the cross- diamond search (CDS)By about 73%, whereas similar prediction quality is still maintained.