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Proceedings Paper

Robust 3D part extraction from range images with deformable superquadric models
Author(s): Yong-Lin Hu; William G. Wee
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Paper Abstract

The extraction of 3-D geometric primitives is an important issue in model-based computer vision. The reliability of the primitives extraction is vital for further object recognition processing. In this paper, we develop a robust 3-D part extraction system. The deformable superquadrics are selected as 3-D part primitives, and a robust superquadric extraction method is developed. First, we introduce a novel adaptive weighted partial data minimization algorithm which can robustly extract superquadric from data containing both Gaussian and random noise. The convergence and the efficiency of the algorithm are discussed. The fuzzy logic techniques are introduced to further improve the algorithm to handle input containing multiple objects. Finally, a range image processing system is developed based on robust superquadric extraction method. This system can efficiently extract 3-D parts from range images. The testing results using both synthetic and real data are presented.

Paper Details

Date Published: 5 July 1995
PDF: 12 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213046
Show Author Affiliations
Yong-Lin Hu, Univ. of Cincinnati (United States)
William G. Wee, Univ. of Cincinnati (United States)


Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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