Name
Abstract | ||
|---|---|---|
One of the challenges of computer vision is to improve the automatic systems for the recognition and tracking of objects in a set of images. One approach that has recently gained importance is based on extracting descriptors, such as the covariance descriptor, because they manage to remain invariant in the regions of these images despite changes of translation, rotation and scale. In this work we propose, using the Covariance Descriptor, a novel saliency system able to find the most relevant regions in an image, which can be used for recognition and tracking objects. Our method is based on the amount of information from each point in the image, and allows us to adapt the regions to maximize the difference of information between the region and its environment. The results show that this tool's improvements can boost trackers precision up to 90% (with initial precision of 50%) without compromising the recall. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25085-9_16 | CIARP |
Keywords | Field | DocType |
initial precision,computer vision,novel saliency system,automatic system,covariance descriptor,relevant region,trackers precision,tracking,object recognition,saliency | BitTorrent tracker,Computer vision,Pattern recognition,Salience (neuroscience),Computer science,Edge detector,Invariant (mathematics),Artificial intelligence,Recall,Covariance,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | Citations |
7042 | 0302-9743 | 2 |
PageRank | References | Authors |
0.36 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristobal Undurraga | 1 | 2 | 0.36 |
| Domingo Mery | 2 | 466 | 42.09 |