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Graph-Based Segmentation with Local Band Constraints

  • Caio de Moraes Braz
  • Paulo A. V. MirandaEmail author
  • Krzysztof Chris Ciesielski
  • Fábio A. M. Cappabianco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11414)

Abstract

Shape constraints are potentially useful high-level priors for object segmentation, allowing the customization of the segmentation to a given target object. In this work, we present a novel shape constraint, named Local Band constraint (\({{\,\mathrm{LB}\,}}\)), for the generalized graph-cut framework, which in its limit case is strongly related to the Boundary Band constraint, preventing the generated segmentation to be irregular in relation to the level sets of a given reference cost map or template of shapes. The \({{\,\mathrm{LB}\,}}\) constraint is embedded in the graph construction with additional arcs defined by a translation-variant adjacency relation, making it easy to combine with other high-level constraints. The \({{\,\mathrm{LB}\,}}\) constraint demonstrates competitive results as compared to Geodesic Star Convexity, Boundary Band, and Hedgehog Shape Prior in Oriented Image Foresting Transform (OIFT) for various scenarios involving natural and medical images, with reduced sensibility to seed positioning.

Keywords

Boundary Band constraint Hedgehog Shape Prior Image Foresting Transform Graph-cut segmentation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Mathematics and StatisticsUniversity of São PauloSão PauloBrazil
  2. 2.Department of MathematicsWest Virginia UniversityMorgantownUSA
  3. 3.Instituto de Ciência e TecnologiaUniversidade Federal de São PauloSão José dos CamposBrazil

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