International Journal of Computer Vision

, Volume 106, Issue 1, pp 31–56 | Cite as

Accurate Junction Detection and Characterization in Natural Images

Article

Abstract

Accurate junction detection and characterization are of primary importance for several aspects of scene analysis, including depth recovery and motion analysis. In this work, we introduce a generic junction analysis scheme. The first asset of the proposed procedure is an automatic criterion for the detection of junctions, permitting to deal with textured parts in which no detection is expected. Second, the method yields a characterization of L-, Y- and X- junctions, including a precise computation of their type, localization and scale. Contrary to classical approaches, scale characterization does not rely on the linear scale-space. First, an a contrario approach is used to compute the meaningfulness of a junction. This approach relies on a statistical modeling of suitably normalized gray level gradients. Then, exclusion principles between junctions permit their precise characterization. We give implementation details for this procedure and evaluate its efficiency through various experiments.

Keywords

Junction detection Scale characterization a-contrario method Scale-invariant keypoints  Contrast invariance 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.State Key Lab. LIESMARSWuhan UniversityWuhanChina
  2. 2.Telecom ParisTech, LTCI CNRSParisFrance

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