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Proceedings Paper

Multiple scale segmentation of range images using wavelets
Author(s): Osama Neiroukh; Mongi A. Abidi
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Paper Abstract

Analysis of range data is important for performing scene interpretation in robotic environments. The first step applied to range images for feature extraction is segmentation, which reveals the inner borders between three-dimensional scene elements. Segmentation of range images can be performed by detecting edges and interpreting them as boundaries of the different surfaces. A segmentation method for range images is proposed using the a trous algorithm implementation of the discrete wavelet transform. This algorithm applies oriented band pass filters at multiple scales for derivative estimations. The wavelet transform (WT) is used as a smoothing multiscale differentiation operator. A model for range image features is developed based on the differential properties of step and roof edges in range images. We show how specific families of wavelets are used for investigating the local and scale properties of the differentials of two-dimensional signals, with emphasis on the higher derivative singularities and zero-crossings usually found in range data. These features are detected, then combined into a binary edge map. Following the modulus maxima of the wavelet transform in the direction of the gradient vector obviates the need for thresholding. The resulting binary edge map provides a basis for complete segmentation. The final result is a segmented image with labeled regions indicating different surface patches. This segmented image can be used as input to a higher-level recognition or for a three-dimensional reconstruction algorithm. The technique is applied to synthetic and real range images with different features and is shown to yield consistent and reliable results.

Paper Details

Date Published: 10 October 1994
PDF: 13 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188895
Show Author Affiliations
Osama Neiroukh, Univ. of Tennessee/Knoxville (United States)
Mongi A. Abidi, Univ. of Tennessee/Knoxville (United States)

Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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