Dr. Haiying Guan
Postdoctoral Fellow
SPIE Involvement:
Author
Area of Expertise:
Computer Vision , Machine Learning , Pattern Recognition , Medical Image Processing , Human Computer Interaction , Computer Graphics
Websites:
Profile Summary

Haiying Guan received her Ph.D. degree in Computer Science from University of California, Santa Barbara, in 2007; her M.S. degree in Nanyang Technological University, Singapore, in 2000; and her B.S. degree in Beijing Jiaotong University, Beijing, China, in 1994, respectively. She is currently postdoctoral fellow in the Lister Hill National Center for Biomedical Communications, National Library of Medicine (NLM), National Institutes of Health (NIH). Prior to her current position, she worked in Samsung Information System America as a senior research engineer, worked in Centre for Signal Processing, School of Electrical & Electronic Engineering, Nanyang Technological University as a research engineer, and worked in CASCO Signal Ltd, ALSTOM China as a software engineer. Her research interests include computer vision and image understanding, medical image processing, machine learning, pattern recognition, intelligent user interface, and human computer interaction. She has authored or co-authored over 15 publications including journal articles, refereed conference proceedings papers, etc. She has served as a reviewer for several journals and conferences including IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Computer Vision and Image Understanding (CVIU), Image and Vision Computing (IVC), Pattern Recognition (PR), International Conference on Computer Vision (ICCV), International Conference on Computer Vision and Pattern Recognition (CVPR), and so on. She has 5 U.S. patents pending.

Publications (1)

Proceedings Article | 11 March 2010 Paper
Proceedings Volume 7628, 762807 (2010) https://doi.org/10.1117/12.844470
KEYWORDS: Feature extraction, Image fusion, Biomedical optics, Feature selection, Image retrieval, Spine, X-ray imaging, X-rays, Content based image retrieval, Genetic algorithms

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