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Proceedings Paper

Integration of edge- and region-based techniques for range image segmentation
Author(s): Ezzet H. Al-Hujazi; Arun K. Sood
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Paper Abstract

Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision applications. For tasks such as 3-D object recognition a representation of the object(s) present in the image is derived and then matched with stored models to determine the object(s) identity. Surface based representation is the most widely used representation in range image analysis. To generate surface based representation of an object a segmentation of the object into a number of surfaces is needed. In this paper we present an approach for the segmentation of range images into a number of surfaces that in turn can be used to generate surface based representation. The approach integrates both edge detection and region growing techniques to achieve the segmentation. We start by detecting jump edges. Jump edge map is processed and regions surrounded by jump edges are isolated. Next fold edges are detected iteratively using normals and residual. Fold edge map is processed to obtain the final segmented image. Jump and fold edge maps are processed using a Bayesian approach. The apriori knowledge is modeled using Markov Random Field. For jump edges we have used a coupled line and depth process. For fold edges we have combined line residuals and normals to process the fold edge map. The performance of the algorithm on a number of range images is presented.

Paper Details

Date Published: 1 February 1991
PDF: 11 pages
Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); doi: 10.1117/12.25190
Show Author Affiliations
Ezzet H. Al-Hujazi, Wayne State Univ. (United States)
Arun K. Sood, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 1381:
Intelligent Robots and Computer Vision IX: Algorithms and Techniques
David P. Casasent, Editor(s)

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