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Proceedings Paper

Parametric surface denoising
Author(s): Ioannis A. Kakadiaris; Ioannis Konstantinidis; Manos Papadakis; Wei Ding; Lixin Shen
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Paper Abstract

Three dimensional (3D) surfaces can be sampled parametrically in the form of range image data. Smoothing/denoising of such raw data is usually accomplished by adapting techniques developed for intensity image processing, since both range and intensity images comprise parametrically sampled geometry and appearance measurements, respectively. We present a transform-based algorithm for surface denoising, motivated by our previous work on intensity image denoising, which utilizes a non-separable Parseval frame and an ensemble thresholding scheme. The frame is constructed from separable (tensor) products of a piecewise linear spline tight frame and incorporates the weighted average operator and the Sobel operators in directions that are integer multiples of 45°. We compare the performance of this algorithm with other transform-based methods from the recent literature. Our results indicate that such transform methods are suited to the task of smoothing range images.

Paper Details

Date Published: 17 September 2005
PDF: 11 pages
Proc. SPIE 5914, Wavelets XI, 59141K (17 September 2005); doi: 10.1117/12.619172
Show Author Affiliations
Ioannis A. Kakadiaris, Univ. of Houston (United States)
Ioannis Konstantinidis, Univ. of Houston (United States)
Manos Papadakis, Univ. of Houston (United States)
Wei Ding, Univ. of Houston (United States)
Lixin Shen, Western Michigan Univ. (United States)

Published in SPIE Proceedings Vol. 5914:
Wavelets XI
Manos Papadakis; Andrew F. Laine; Michael A. Unser, Editor(s)

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