Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6876540 | Computer-Aided Design | 2015 | 11 Pages |
Abstract
Our framework uses Online Multi-Class LPBoost (OMCLP) to train/update a segmentation model progressively, which includes several Online Random forests (ORFs) as the weak learners. Then, it performs graph cuts optimization to segment the 3D shape by using the trained/updated segmentation model as the optimal data term. There exist three features of our framework. Firstly, the segmentation model can be trained gradually during the collection of the shapes. Secondly, the segmentation results can be refined progressively until users' requirements are met. Thirdly, the segmentation model can be updated incrementally without retraining all shapes when users add new shapes. Experimental results demonstrate the effectiveness of our approach.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Graphics and Computer-Aided Design
Authors
Feiqian Zhang, Zhengxing Sun, Mofei Song, Xufeng Lang,