Branch-and-bound hypothesis selection for two-view multiple structure and motion segmentation

2008 ◽  
Author(s):  
Ninad Thakoor ◽  
Jean Gao
Keyword(s):  
Branch And Bound ◽  
Motion Segmentation ◽  
Multiple Structure ◽  
Structure And Motion ◽  
Hypothesis Selection ◽  
Selection For

A Divided-and-Conquer Algorithm Based on Guided Selection for Two-View Motion Segmentation

2010 ◽  
Vol 20-23 ◽  
pp. 452-458
Author(s):  
Hui Jing Wang ◽  
Kai Chen ◽  
Yi Zhou ◽  
Yan Zhang ◽  
Hai Bing Guan
Keyword(s):  
Computer Vision ◽  
Expectation Maximization ◽  
Random Sampling ◽  
Fundamental Problem ◽  
Motion Segmentation ◽  
Dynamic Scene ◽  
Clustering Method ◽  
Cluster Process ◽  
Selection For ◽  
Segmentation Problem

Motion segmentation for dynamic scene is a fundamental problem in computer vision due to its well-known chicken-and-egg character. The key issue is to estimate both numbers and parameters of motions simultaneously. Different from global clustering method and random sampling scheme, in this paper, we propose a divided-and-conquer algorithm to solve the motion segmentation problem. A guided selection is used to choose the most creditable hypothetical motion as a candidate seed and then make it grow larger. Compared to previous works such as expectation maximization and factorization approaches, there is no need for any pre-knowledge of the number of motions. To global non-parametric clustering method, it is fast because each time we only do cluster process in a partitioned sub-set. Experiments have shown that the proposal method can give a satisfying result for motion segmentation.

scholarly journals Optimizing the Paths of Trains Formed at the Loading Area in a Multi-loop Rail Network

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 844
Author(s):  
Xingkui Li ◽  
Boliang Lin ◽  
Yinan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Branch And Bound ◽  
Large Scale ◽  
Path Selection ◽  
Branch And Bound Algorithm ◽  
Computational Experiments ◽  
Commercial Software ◽  
Rail Network ◽  
Selection For

Each loop in a multi-loop rail network consists of two segments, both of which have roughly the same conditions and mileage and are approximately symmetrical. This paper is devoted to optimizing the paths of trains formed at the loading area in a multi-loop rail network. To attain this goal, three different situations are analyzed, and two models are proposed for networks with adequate and inadequate capabilities. Computational experiments are also carried out using the commercial software Lingo, with the branch and bound algorithm. The results show that the models can achieve the same solution with different solution times. To solve the problem of path selection for large-scale train flows, a genetic algorithm is also designed and proves to perform well in a set of computational experiments.

Sign in / Sign up

Export Citation Format

Share Document