Name
Abstract | ||
|---|---|---|
Developing an efficient object localization system for complicated industrial objects is an important, yet difficult robotic task. To tackle this problem, we have developed a system consisting first of a vision model acquisition editor, where the object salient features are acquired through a human-in-the-loop approach. Subsequently, two feature extraction algorithms, region-growing and edge-grouping, are applied to the object scene. Finally, by Kalman filter estimation of a proper ellipse representation, our object localization system successfully generates ellipse hypotheses by grouping edge fragments in the scene. The proposed system is validated by experiments using actual industrial objects. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1145/967900.967908 | SAC |
Keywords | Field | DocType |
object localization system,kalman filter estimation,ellipse hypothesis,complicated industrial object,efficient object localization system,proper ellipse representation,proposed system,object scene,object salient feature,actual industrial object,concatenate feature extraction,elliptic object localization,kalman filter,region growing,feature extraction | Computer vision,Feature extraction algorithm,Pattern recognition,Computer science,Kalman filter,Feature extraction,Localization system,Concatenation,Artificial intelligence,Ellipse,Salient | Conference |
ISBN | Citations | PageRank |
1-58113-812-1 | 4 | 0.63 |
References | Authors | |
17 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuichi Motai | 1 | 230 | 24.68 |
| Akio Kosaka | 2 | 439 | 46.88 |