Highly Accurate Boundary Detection and Grouping - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2010

Highly Accurate Boundary Detection and Grouping


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.
No file

Dates and versions

hal-00857481 , version 1 (03-09-2013)


  • HAL Id : hal-00857481 , version 1


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⟩
80 View
0 Download


Gmail Facebook Twitter LinkedIn More