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Generalized Boundaries from Multiple Image Interpretations

Leordeanu, Marius; Sukthankar, Rahul and Sminchisescu, Cristian LU (2014) In IEEE Transactions on Pattern Analysis and Machine Intelligence 36(7). p.1312-1324
Abstract
Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and occlusion boundaries from video. Our generalized boundary detection method (Gb) simultaneously combines low-level and mid-level image representations in a single eigenvalue problem and solves for the optimal continuous boundary orientation and strength. The closed-form solution to boundary detection enables our algorithm to achieve... (More)
Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and occlusion boundaries from video. Our generalized boundary detection method (Gb) simultaneously combines low-level and mid-level image representations in a single eigenvalue problem and solves for the optimal continuous boundary orientation and strength. The closed-form solution to boundary detection enables our algorithm to achieve state-of-the-art results at a significantly lower computational cost than current methods. We also propose two complementary novel components that can seamlessly be combined with Gb: first, we introduce a soft-segmentation procedure that provides region input layers to our boundary detection algorithm for a significant improvement in accuracy, at negligible computational cost; second, we present an efficient method for contour grouping and reasoning, which when applied as a final post-processing stage, further increases the boundary detection performance. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Edge, boundary and contour detection, occlusion boundaries, soft image, segmentation, computer vision
in
IEEE Transactions on Pattern Analysis and Machine Intelligence
volume
36
issue
7
pages
1312 - 1324
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000338209900003
  • scopus:84903157998
ISSN
1939-3539
DOI
10.1109/TPAMI.2014.17
language
English
LU publication?
yes
id
d8441854-8519-4970-aed3-e95f45f76f81 (old id 4602780)
date added to LUP
2014-09-04 14:12:01
date last changed
2017-10-29 03:53:59
@article{d8441854-8519-4970-aed3-e95f45f76f81,
  abstract     = {Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and occlusion boundaries from video. Our generalized boundary detection method (Gb) simultaneously combines low-level and mid-level image representations in a single eigenvalue problem and solves for the optimal continuous boundary orientation and strength. The closed-form solution to boundary detection enables our algorithm to achieve state-of-the-art results at a significantly lower computational cost than current methods. We also propose two complementary novel components that can seamlessly be combined with Gb: first, we introduce a soft-segmentation procedure that provides region input layers to our boundary detection algorithm for a significant improvement in accuracy, at negligible computational cost; second, we present an efficient method for contour grouping and reasoning, which when applied as a final post-processing stage, further increases the boundary detection performance.},
  author       = {Leordeanu, Marius and Sukthankar, Rahul and Sminchisescu, Cristian},
  issn         = {1939-3539},
  keyword      = {Edge,boundary and contour detection,occlusion boundaries,soft image,segmentation,computer vision},
  language     = {eng},
  number       = {7},
  pages        = {1312--1324},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title        = {Generalized Boundaries from Multiple Image Interpretations},
  url          = {http://dx.doi.org/10.1109/TPAMI.2014.17},
  volume       = {36},
  year         = {2014},
}