Peter Tay

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VIVA Affiliation

Research Scientist
2004-2006

Degrees

Ph.D. Electrical and Computer Engineering, University of Oklahoma
M.A. Mathematics, University of Oklahoma
B.S. Mathematics, University of Oklahoma

Biographical Sketch

Peter Tay received a B.S. (1990), a M.A. (1995) degree in pure and applied mathematics, and a Ph.D. (2003) degree in Electrical and Computer Engineering from the University of Oklahoma in Norman, OK. He was employed as a software engineer at the National Weather Service for six years before joining VIVA. Dr. Peter Tay is member of IEEE and IEEE Signal Processing society.

Research Interests

Dr. Tay's areas of research included time-frequency analysis, perfect reconstruction filter banks, image enhancement, restoration, and compression, machine vision, bio-medical imaging, image interpretation and analysis, and information theory.

Book Chapter:

• AM-FM Image Models: Fundamental Techniques and Emerging Trends, Handbook of Image Processing Second Edition, A. C. Bovik (ed), Elsevier Academic Press, 2005.

Journal Articles

• Techniques to enhance ultrasound images, submitted to IEEE Trans. Image Proc.
• Properties of the magnitude terms of orthogonal scaling functions, submitted to IEEE Signal Proc. Letters.
• Determination of the number of texture segments using wavelets, Electronic Journal of Differential Equations.

Conference Papers

• Multi-assignment interacting multiple model for tracking micro-bubbles, Conference Record of the Thirty-Nineth Asilomar Conference on Signals, Systems and Computers, October 30-November 2, 2005, submitted for publication.
• Image denoising and scalar quantization using an optimally frequency localized modulated lapped transform, IEEE ICIP at Genova, Italy, September 11-14, 2005, accepted for publication.
• Frequency implementation of discrete wavelet transforms, IEEE SSIAI at Lake Tahoe, NV, March 28-30, 2004, pp. 167-171.
• Joint uncertainty measure for maximally decimated M-channel prime factor cascaded wavelet filter banks, IEEE ICIP at Barcelona, Spain, September 14-17, 2003, vol. I, pp. 1033-1036.
• Discrete wavelet transform with optimal joint localization for determining the number of image texture segments, IEEE ICIP at Rochester, NY, September 22-25, 2002, vol. III, pp. 281-284.
• Image watermarking using wavelets, IEEE MWSCAS at Tulsa, OK, August 4-7, 2002, vol. III, pp. 258-261.
• A novel translation and modulation invariant discrete-discrete uncertainty measure, IEEE ICASSP at Orlando, FL, May 13-17, 2002, vol. II, pp. 1461-1464.
• A wavelet filter bank which minimizes a novel translation invariant discrete uncertainty measure, IEEE SSIAI in Santa Fe, NM, April 7-9, 2002, pp. 173-177.
• Determination of the number of texture segments using wavelets, Conference on Applied Mathematics at Edmond, OK, Feb. 23-24, 2001, pp. 153-162.
• Unsupervised texture segmentation using dominant image modulations, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, 2000, vol. 2, pp. 911-915.
• Signals and systems: a consistent, unified approach, Frontiers in Education Conference 2000, vol. 2, pp. F4E/1-F4E/6.