Dr. Yufeng Zheng
Associate Professor at Alcorn State Univ
SPIE Involvement:
Senior status | Conference Program Committee | Conference Chair | Author | Editor | Instructor | Student Chapter Advisor
Area of Expertise:
Bio-inspired Image analysis , Pattern recognition , Biometrics (face recognition) , Computer-aided (cancer) detection , Multispectral image fusion and colorization
Websites:
Profile Summary

Yufeng Zheng received his Ph.D. degree in Digital Image Processing from the Tianjin University (Tianjin, China) in 1997. He is presently with the Alcorn State University (Mississippi, USA) as an associate professor. He serves as a program director of the Computer Networking and Information Technology Program at the Advanced Technologies Department and a director of the Center for Imaging and Pattern Recognition in the System Research Institute. Dr. Zheng holds two patents in glaucoma classification and face recognition, and has edited two books, published six book chapters and more than 70 scientific papers. His research interests focus on bio-inspired image analysis, pattern recognition, biometrics, and computer-aided diagnosis. Dr. Zheng is a Cisco Certified Network Professional (CCNP).
Publications (38)

PROCEEDINGS ARTICLE | May 10, 2017
Proc. SPIE. 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017
KEYWORDS: Image fusion, Infrared imaging, Visible radiation, Facial recognition systems, Image compression, Detection and tracking algorithms, Cameras, Wavelets, Image quality, Data fusion

PROCEEDINGS ARTICLE | May 19, 2016
Proc. SPIE. 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
KEYWORDS: Information fusion, Defense and security, Thermography, Image fusion, Cameras, Sensors, Image processing, Multispectral imaging, Analytical research, Data fusion

PROCEEDINGS ARTICLE | May 19, 2016
Proc. SPIE. 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
KEYWORDS: Breast, Computer aided diagnosis and therapy, Computer aided diagnosis and therapy, Cancer, Breast cancer, Detection and tracking algorithms, Detection and tracking algorithms, Databases, Feature extraction, Mammography, Computer aided design, Solid modeling

PROCEEDINGS ARTICLE | May 19, 2016
Proc. SPIE. 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
KEYWORDS: Thermography, Signal to noise ratio, Image compression, Data modeling, Visualization, Discrete wavelet transforms, JPEG2000, Multispectral imaging, Image quality, Image quality standards

Showing 5 of 38 publications
Conference Committee Involvement (6)
Mobile Multimedia/Image Processing, Security, and Applications 2018
16 April 2018 | Orlando, Florida, United States
Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
17 April 2016 | Baltimore, Maryland, United States
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
23 April 2015 | Baltimore, Maryland, United States
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
7 May 2014 | Baltimore, Maryland, United States
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
1 May 2013 | Baltimore, Maryland, United States
Showing 5 of 6 published special sections
Course Instructor
SC1135: Multispectral Image Fusion and Night Vision Colorization
This course presents methods and applications of multispectral image fusion and night vision colorization organized into three areas: (1) image fusion methods, (2) evaluation, and (3) applications. Two primary multiscale fusion approaches, image pyramid and wavelet transform, will be emphasized. Image fusion comparisons include data, metrics, and analytics. <br/> Fusion applications presented include off-focal images, medical images, night vision, and face recognition. Examples will be discussed of night-vision images rendered using channel-based color fusion, lookup-table color mapping, and segment-based method colorization. These colorized images resemble natural color scenes and thus can improve the observer’s performance. After taking this course you will know how to combine multiband images and how to render the result with colors in order to enhance computer vision and human vision especially in low-light conditions. <br/> In addition to the course notes, attendees will receive a set of published papers, the data sets used in the analysis, and MATLAB code of methods and metrics for evaluation. A FTP website is established for course resource access.
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