Name
Abstract | ||
|---|---|---|
In this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-14612-6_32 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
curvilinear structure extraction,marked point process,Monte Carlo sampling with delayed rejection,aggregation algorithm | Line segment,Mathematical optimization,Monte Carlo method,Computer science,Curvilinear coordinates,Gradient magnitude,Estimation theory,Marked point process,Prior probability | Conference |
Volume | ISSN | Citations |
8932 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seong-Gyun Jeong | 1 | 1 | 1.03 |
| Yuliya Tarabalka | 2 | 907 | 47.12 |
| Josiane Zerubia | 3 | 2032 | 232.91 |