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|Title:||A model-based range image segmentation algorithm using a novel robust estimator|
|Citation:||3rd International Workshop on Statistical and Computational Theories of Vision - SCTV (in conjunction with ICCV03), Nice, France, Oct. 2003|
|Conference Name:||International Workshop on Statistical and Computational Theories of Vision (3rd : 2003 : Nice, France)|
|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) . 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.|
|Appears in Collections:||Computer Science publications|
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