Region Segmentation via Deformable Model-Guided Split and Merge

Abstract

An improved method for deformable shape based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. Perceptually motovated crtieria are used to determine where/how to split regions, based on the local shape properties of the regions group's bounding contour. A globally consistent interpretation is determined in part by the minimum description lingth principle. Experiments show that model guided split and merge yields a significant improvement in segmentation over a method that uses merging alone.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2001
Accession Number
ADA451541

Entities

People

  • Lifeng Liu
  • Stan Sclaroff

Organizations

  • Boston University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Computer Science
  • Blood Cells
  • Boundaries
  • Cells
  • Computations
  • Computer Science
  • Computer Vision
  • Curvature
  • Detection
  • Image Processing
  • Image Segmentation
  • Military Research
  • Models
  • Shape
  • Statistical Shape Models
  • Two Dimensional

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Computational Modeling and Simulation
  • Structural Dynamics.