Name
Abstract | ||
|---|---|---|
Human labeled datasets, along with their corresponding evaluation algorithms, play an important role in boundary detection. We here present a psychophysical experiment that addresses the reliability of such benchmarks. To find better remedies to evaluate the performance of any boundary detection algorithm, we propose a computational framework to remove inappropriate human labels and estimate the intrinsic properties of boundaries. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Data mining,Metatheory,Computer science,Boundary detection,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1302.5985 | 6 |
PageRank | References | Authors |
0.44 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodi Hou | 1 | 2069 | 72.53 |
| Alan L. Yuille | 2 | 10339 | 1902.01 |
| Christof Koch | 3 | 7248 | 973.47 |