K
Kuo-Chin Fan
Researcher at National Central University
Publications - 135
Citations - 3022
Kuo-Chin Fan is an academic researcher from National Central University. The author has contributed to research in topics: Feature extraction & Pattern recognition (psychology). The author has an hindex of 28, co-authored 128 publications receiving 2870 citations.
Papers
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Journal ArticleDOI
Genetic-based search for error-correcting graph isomorphism
TL;DR: A genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism is presented and results reveal the superiority of this new technique than several other well-known algorithms.
Journal ArticleDOI
Triangle-based approach to the detection of human face ☆
Chiunhsiun Lin,Kuo-Chin Fan +1 more
TL;DR: A robust and e$cient human face detection system that can detect multiple faces in complex backgrounds is presented and experimental results reveal that the proposed method is better than traditional methods in terms of e$ciency and accuracy.
Proceedings ArticleDOI
The Application of a Convolution Neural Network on Face and License Plate Detection
TL;DR: Two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network (CNN) verifier, and Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates.
Journal ArticleDOI
Motion Flow-Based Video Retrieval
Chih-Wen Su,Hong-Yuan Mark Liao,Hong-Yuan Mark Liao,Hsiao-Rong Tyan,Chia-Wen Lin,Duan-Yu Chen,Kuo-Chin Fan +6 more
TL;DR: The use of motion vectors embedded in MPEG bitstreams are used to generate so-called ldquomotion flowsrdquo, which are applied to perform video retrieval, which is indeed superb in the video retrieval process.
Journal ArticleDOI
An adaptive clustering algorithm for color quantization
Ing-Sheen Hsieh,Kuo-Chin Fan +1 more
TL;DR: Experimental results reveal the feasibility and superiority of the proposed approach in solving color quantization problem and the executing speed of the algorithm is quite fast due to the reduced RGB color space, sorted histogram list, suitable color design and destined pixel mapping.