Self-organizing deformable model

A new method for fitting mesh model to given object surface

Kenichi Morooka, Hiroshi Nagahashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper presents a new method for projecting a mesh model of a source object onto a surface of an arbitrary target object. A deformable model, called Self-organizing Deformable Model(SDM), is deformed so that the shape of the model is fitted to the target object. We introduce an idea of combining a competitive learning and an energy minimization into the SDM deformation. Our method is a powerful tool in the areas of computer vision and computer graphics. For example, it enables to map mesh models onto various kinds of target surfaces like other methods for a surface parameterization, which have focused on specified target surface. Also the SDM can reconstruct shapes of target objects like general deformable models.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - First International Symposium, ISVC 2005, Proceedings
Pages151-158
Number of pages8
DOIs
Publication statusPublished - Dec 1 2005
EventFirst International Symposium on Advances in Visual Computing, ISVC 2005 - Lake Tahoe, NV, United States
Duration: Dec 5 2005Dec 7 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3804 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherFirst International Symposium on Advances in Visual Computing, ISVC 2005
CountryUnited States
CityLake Tahoe, NV
Period12/5/0512/7/05

Fingerprint

Deformable Models
Self-organizing
Mesh
Target
Computer Graphics
Competitive Learning
Learning
Model
Energy Minimization
Computer graphics
Computer Vision
Parameterization
Object
Computer vision
Arbitrary

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Morooka, K., & Nagahashi, H. (2005). Self-organizing deformable model: A new method for fitting mesh model to given object surface. In Advances in Visual Computing - First International Symposium, ISVC 2005, Proceedings (pp. 151-158). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3804 LNCS). https://doi.org/10.1007/11595755_19

Self-organizing deformable model : A new method for fitting mesh model to given object surface. / Morooka, Kenichi; Nagahashi, Hiroshi.

Advances in Visual Computing - First International Symposium, ISVC 2005, Proceedings. 2005. p. 151-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3804 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Morooka, K & Nagahashi, H 2005, Self-organizing deformable model: A new method for fitting mesh model to given object surface. in Advances in Visual Computing - First International Symposium, ISVC 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3804 LNCS, pp. 151-158, First International Symposium on Advances in Visual Computing, ISVC 2005, Lake Tahoe, NV, United States, 12/5/05. https://doi.org/10.1007/11595755_19
Morooka K, Nagahashi H. Self-organizing deformable model: A new method for fitting mesh model to given object surface. In Advances in Visual Computing - First International Symposium, ISVC 2005, Proceedings. 2005. p. 151-158. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11595755_19
Morooka, Kenichi ; Nagahashi, Hiroshi. / Self-organizing deformable model : A new method for fitting mesh model to given object surface. Advances in Visual Computing - First International Symposium, ISVC 2005, Proceedings. 2005. pp. 151-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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