Name
Abstract | ||
|---|---|---|
Finding correspondences among objects in different images is a critical problem in computer vision. Even good correspondence procedures can fail, however, when faced with deformations, occlusions, and differences in lighting and zoom levels across images. We present a methodology for augmenting correspondence matching algorithms with a means for triaging the focus of attention and effort in assisting the automated matching. For guiding the mix of human and automated initiatives, we introduce a measure of the expected value of resolving correspondence uncertainties. We explore the value of the approach with experiments on benchmark data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s11263-013-0657-5 | International Journal of Computer Vision |
Keywords | Field | DocType |
Human interaction,Active learning,Value of information,Matching,Correspondence problems | Computer vision,Active learning,Computer science,Zoom,Human interaction,Value of information,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
108 | 1-2 | 0920-5691 |
Citations | PageRank | References |
3 | 0.41 | 32 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefanie Jegelka | 1 | 792 | 46.31 |
| Ashish Kapoor | 2 | 1833 | 119.72 |
| Eric Horvitz | 3 | 9402 | 1058.25 |