Topological segmentation of discrete surfaces
International Journal of Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
International Journal of Computer Vision
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Deformable M-Reps for 3D Medical Image Segmentation
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
An Integrated Framework for Image Segmentation and Perceptual Grouping
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Discriminative Learning for Deformable Shape Segmentation: A Comparative Study
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Image processing framework for virtual colonoscopy
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
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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 100 volumes of size 500 × 512 × 512 at the speed of 2 ~ 3 minutes per volume. The results obtained are encouraging.