Name
Abstract | ||
|---|---|---|
In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into part bundles, where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.cviu.2010.03.009 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
Object detection,Part bundle,Shape context | Object detection,Computer vision,Object-oriented programming,Edge detection,Artificial intelligence,Shape context,Bundle,Mathematics,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
114 | 7 | Computer Vision and Image Understanding |
Citations | PageRank | References |
8 | 0.47 | 34 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ChengEn Lu | 1 | 71 | 2.76 |
| Nagesh Adluru | 2 | 208 | 20.57 |
| Haibin Ling | 3 | 4531 | 215.76 |
| Guangxi Zhu | 4 | 437 | 49.06 |
| Longin Jan Latecki | 5 | 3301 | 176.88 |