Name
Abstract | ||
|---|---|---|
In this work we present an extension of the SIFT algorithm to color images. In the extrema detection stage, an energy level descriptor based on the color tensor of the image is computed and used to locate keypoints candidates. Then, in the description stage, the color gradient magnitude and orientation of the samples around the keypoint are used to compute an orientation histogram to create the keypoint descriptor. A comparative study is carried out between the proposed algorithm and the classic SIFT and the C-SIFT algorithms in several illumination settings. The proposed algorithm presents a better performance in terms of accuracy when objects are poorly illuminated. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LATINCOM.2014.7041889 | Communications |
Keywords | DocType | Citations |
feature extraction,image colour analysis,object recognition,tensors,c-sift algorithms,color gradient magnitude,color images,color tensor,energy level descriptor,extrema detection stage,illumination settings,keypoint descriptor,orientation histogram,poorly illuminated object,tensor based feature detection,sift,feature detection,tensile stress,color,databases,vectors,lighting | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Femando Merchan | 1 | 0 | 0.34 |
| Filadelfio Caballero | 2 | 0 | 0.34 |
| Damien Rousseau | 3 | 0 | 0.34 |
| Hector Poveda | 4 | 1 | 1.03 |