Published 2012
| Submitted
Conference Paper
Open
A Meta-Theory of Boundary Detection Benchmarks
- Creators
- Hou, Xiaodi
- Yuille, Alan
-
Koch, Christof
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 instrinsic properties of boundaries.
Additional Information
The first author would like to thank Liwei Wang, Yin Li, Xi (Stephen) Chen, and Katrina Ligett. The research was supported by the ONR via an award made through Johns Hopkins University and by the Mathers Foundation.Attached Files
Submitted - Xiaodi_-_NIPSW_2012.pdf
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Xiaodi_-_NIPSW_2012.pdf
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Additional details
- Eprint ID
- 40704
- Resolver ID
- CaltechAUTHORS:20130816-103412043
- Office of Naval Research (ONR)
- G. Harold and Leila Y. Mathers Charitable Foundation
- Created
-
2013-03-04Created from EPrint's datestamp field
- Updated
-
2023-06-02Created from EPrint's last_modified field
- Caltech groups
- Koch Laboratory (KLAB)