Integrating shape and texture in deformabie models: From hybrid methods to metamorphs

D. Metaxas, X. Huang, T. Chen

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

In traditional shape-based deformable models, the external image forces come primarily from edge or gradient information. Such reliance on edge information, however, makes the models prone to get stuck in local minima due to image noise and various other image artifacts. Integrating region statistics constraints has been a centerpiece of the efforts toward more robust, well-behaved deformable models in boundary extraction and segmentation. In this chapter, we review previous work on the loose coupling of boundary and region information in two major classes of deformable models: the parametric models and the geometric models. Then, we propose a new class of deformable shape and texture models, which we term Metamorphs. The novel formulation of the Metamorph models tightly couples shape and interior texture and the dynamics of the models are derived in a unified manner from both boundary and region information in a variational framework.

Original languageEnglish (US)
Title of host publicationHandbook of Mathematical Models in Computer Vision
PublisherSpringer US
Pages113-129
Number of pages17
ISBN (Print)0387263713, 9780387263717
DOIs
StatePublished - Dec 1 2006

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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    Metaxas, D., Huang, X., & Chen, T. (2006). Integrating shape and texture in deformabie models: From hybrid methods to metamorphs. In Handbook of Mathematical Models in Computer Vision (pp. 113-129). Springer US. https://doi.org/10.1007/0-387-28831-7_7