Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/56568
Type: Conference paper
Title: A model-based range image segmentation algorithm using a novel robust estimator
Author: Wang, H.
Suter, D.
Citation: 3rd International Workshop on Statistical and Computational Theories of Vision - SCTV (in conjunction with ICCV03), Nice, France, Oct. 2003
Publisher: CS Press
Publisher Place: Washington
Issue Date: 2003
Conference Name: International Workshop on Statistical and Computational Theories of Vision (3rd : 2003 : Nice, France)
Statement of
Responsibility: 
Hanzi Wang and David Suter
Abstract: This paper presents a novel range image segmentation algorithm based on a newly proposed robust estimator: Adaptive Scale Sample Consensus (ASSC) [28]. The proposed algorithm is a model-based top-down technique and directly extracts the required primitives (models) from the raw images. Compared with current popular methods (region-based and edge-based methods), the algorithm is very robust to noisy or occluded data due to the adoption of the novel robust estimator ASSC. Using a hierarchical implementation, the proposed method is computationally efficient. Experimental results on real range images show that the proposed algorithm is attractive when compared with other state-of-the-art segmentation methods.
RMID: 0020094051
Description (link): http://www.cs.adelaide.edu.au/~hanzi/
Appears in Collections:Computer Science publications

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