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Yi-Chong Zeng
Researcher at Institute for Information Industry
Publications - 62
Citations - 420
Yi-Chong Zeng is an academic researcher from Institute for Information Industry. The author has contributed to research in topics: Digital watermarking & Color histogram. The author has an hindex of 9, co-authored 60 publications receiving 389 citations. Previous affiliations of Yi-Chong Zeng include National Taiwan University & Academia Sinica.
Papers
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Journal ArticleDOI
Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis
TL;DR: A novel algorithm using color contrast enhancement and lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings and a new patching method is presented using the Markov random field (MRF) model of texture synthesis.
Journal ArticleDOI
A Novel Image Recovery Algorithm for Visible Watermarked Images
Soo-Chang Pei,Yi-Chong Zeng +1 more
TL;DR: The results of this study demonstrate that the proposed algorithm can blindly and successfully remove the visible watermarks without knowing the watermarking methods in advance.
Proceedings ArticleDOI
Monitoring Elder's Living Activity Using Ambient and Body Sensor Network in Smart Home
TL;DR: An activity recognition system for smart home is proposed, so elders can live alone and their children can monitor their parents' living activity to achieve the concept of "Aging in Place".
Proceedings ArticleDOI
Context-aware activity prediction using human behavior pattern in real smart home environments
Ming-Je Tsai,Chao-Lin Wu,Sipun Kumar Pradhan,Yifei Xie,Ting-Ying Li,Li-Chen Fu,Yi-Chong Zeng +6 more
TL;DR: A context-aware framework for human behavior learning and prediction is presented that discovers contexts from resident's real life data and adapts corresponding behavior patterns next accordingly, and the experimental results show promising results.
Proceedings ArticleDOI
Color Images Enhancement using Weighted Histogram Separation
TL;DR: This paper presents a modified approach to the successive mean quantization transform, which is called as the weighted histogram separation (WHS) for enhancement of color images and is further applied to the local enhancement, similar to the adaptive histogram equalization.