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ECCV
2006
Springer

A Learning Based Approach for 3D Segmentation and Colon Detagging

15 years 19 days ago
A Learning Based Approach for 3D Segmentation and Colon Detagging
Abstract. Foreground and background segmentation is a typical problem in computer vision and medical imaging. In this paper, we propose a new learning based approach for 3D segmentation, and we show its application on colon detagging. In many problems in vision, both the foreground and the background observe large intra-class variation and inter-class similarity. This makes the task of modeling and segregation of the foreground and the background very hard. The framework presented in this paper has the following key components: (1) We adopt probabilistic boosting tree [9] for learning discriminative models for the appearance of complex foreground and background. The discriminative model ratio is proved to be a pseudo-likelihood ratio modeling the appearances. (2) Integral volume and a set of 3D Haar filters are used to achieve efficient computation. (3) We devise a 3D topology representation, grid-line, to perform fast boundary evolution. The proposed algorithm has been tested on over ...
Zhuowen Tu, Xiang Zhou, Dorin Comaniciu, Luca Bogo
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2006
Where ECCV
Authors Zhuowen Tu, Xiang Zhou, Dorin Comaniciu, Luca Bogoni
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