Name
Abstract | ||
|---|---|---|
Abstract. This paper describes a method,for recognizing partially occluded objects under different levels of illumi- nation brightness by using the eigenspace analysis. In our previous work, we developed the “eigenwindow” method to recognize the partially occluded objects in an assembly task, and demonstrated,with sufficient high performance,for the industrial use that the method,works successfully for multi- ple objects with specularity under constant illumination. In this paper, we modify the eigenwindow method for recog- nizing objects under different illumination conditions, as is sometimes the case in manufacturing environments, by us- ing additional color information. In the proposed method, a measured,color in the RGB color space is transformed into one in the HSV color space. Then, the hue of the measured color, which is invariant to change in illumination brightness and direction, is used for recognizing multiple objects under different illumination conditions. The proposed method,was applied to real images of multiple objects under various il- lumination conditions, and the objects were recognized and localized successfully. Key words: Assembly tasks – Object recognition – Visual |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/s001380050138 | machine vision applications |
Keywords | DocType | Volume |
visual learning,object recognition,illumination invariance,eigenspace,hsv color space,color space | Conference | 12 |
Issue | ISSN | Citations |
4 | 0932-8092 | 5 |
PageRank | References | Authors |
0.47 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| kohtaro ohba | 1 | 317 | 66.11 |
| Yoichi Sato | 2 | 44 | 7.29 |
| Katsushi Ikeuchi | 3 | 4651 | 881.49 |