Please use this identifier to cite or link to this item:
https://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. |
Description (link): | http://www.cs.adelaide.edu.au/~hanzi/ |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.