Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Conference papers

Highly Accurate Boundary Detection and Grouping

Abstract : In this work we address boundary detection and boundary grouping. We first pursue a learning- based approach to boundary detection. For this (i) we leverage appearance and context information by extracting descriptors around edgels and use them as features for classification, (ii) we use discrimina- tive dimensionality reduction for efficiency and (iii) we use outlier-resilient boosting to deal with noise in the training set. We then introduce fractional-linear programming to optimize a grouping criterion that is expressed as a cost ratio. Our contributions are systematically evaluated on the Berkeley benchmark.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-00857481
Contributor : Iasonas Kokkinos Connect in order to contact the contributor
Submitted on : Tuesday, September 3, 2013 - 3:46:06 PM
Last modification on : Friday, January 21, 2022 - 3:01:25 AM

Identifiers

  • HAL Id : hal-00857481, version 1

Collections

Citation

Iasonas Kokkinos. Highly Accurate Boundary Detection and Grouping. CVPR - IEEE Conf. on Computer Vision and Pattern Recognition, 2010, San Francisco, United States. pp.2520-2527. ⟨hal-00857481⟩

Share

Metrics

Record views

79