SO dynamic deformation for building of 3-D models

Sei Wang Chen; George C. Stockman; Kuo En Chang

doi:10.1109/72.485673

SO dynamic deformation for building of 3-D models

Sei Wang Chen^*, George C. Stockman, Kuo En Chang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

Three-dimensional (3-D) modeling based on an ensemble of multilayer self-organizing (SO) neural networks is described. Our objective for 3-D modeling is to construct a representation of a 3-D object shape from sensed surface points acquired from the object. Current modeling techniques can be classified into two categories: the static and the dynamic approaches, the former grounded in computational geometry, and the latter rooted in the mechanics of elastic materials. In this paper, a neural-based dynamic modeling approach is presented. The method used is proved to converge and experimental results are shown which support its applicability to real problems.

Original language	English
Pages (from-to)	374-387
Number of pages	14
Journal	IEEE Transactions on Neural Networks
Volume	7
Issue number	2
DOIs	https://doi.org/10.1109/72.485673
Publication status	Published - 1996

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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title = "SO dynamic deformation for building of 3-D models",

abstract = "Three-dimensional (3-D) modeling based on an ensemble of multilayer self-organizing (SO) neural networks is described. Our objective for 3-D modeling is to construct a representation of a 3-D object shape from sensed surface points acquired from the object. Current modeling techniques can be classified into two categories: the static and the dynamic approaches, the former grounded in computational geometry, and the latter rooted in the mechanics of elastic materials. In this paper, a neural-based dynamic modeling approach is presented. The method used is proved to converge and experimental results are shown which support its applicability to real problems.",

author = "Chen, {Sei Wang} and Stockman, {George C.} and Chang, {Kuo En}",

note = "Funding Information: Manuscript received January 27, 1994; revised January 4, 1995. This work was supported by the National Science Council, R.O.C., under Contract NSC-83-0408E-003-00 1. S:W. Chen and K.-E. Chang are with the Department of Information and Computer Education, National Taiwan Normal University, Taiper, Taiwan, Republic of China. G. C. Stockman is with the Computer Science Department, Michigan State University, East Lansing, MI 48824 USA. Publisher Item Identifier S 1045-9227(96)00166-X.",

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journal = "IEEE Transactions on Neural Networks",

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AU - Chen, Sei Wang

AU - Stockman, George C.

AU - Chang, Kuo En

N1 - Funding Information: Manuscript received January 27, 1994; revised January 4, 1995. This work was supported by the National Science Council, R.O.C., under Contract NSC-83-0408E-003-00 1. S:W. Chen and K.-E. Chang are with the Department of Information and Computer Education, National Taiwan Normal University, Taiper, Taiwan, Republic of China. G. C. Stockman is with the Computer Science Department, Michigan State University, East Lansing, MI 48824 USA. Publisher Item Identifier S 1045-9227(96)00166-X.

PY - 1996

Y1 - 1996

N2 - Three-dimensional (3-D) modeling based on an ensemble of multilayer self-organizing (SO) neural networks is described. Our objective for 3-D modeling is to construct a representation of a 3-D object shape from sensed surface points acquired from the object. Current modeling techniques can be classified into two categories: the static and the dynamic approaches, the former grounded in computational geometry, and the latter rooted in the mechanics of elastic materials. In this paper, a neural-based dynamic modeling approach is presented. The method used is proved to converge and experimental results are shown which support its applicability to real problems.

AB - Three-dimensional (3-D) modeling based on an ensemble of multilayer self-organizing (SO) neural networks is described. Our objective for 3-D modeling is to construct a representation of a 3-D object shape from sensed surface points acquired from the object. Current modeling techniques can be classified into two categories: the static and the dynamic approaches, the former grounded in computational geometry, and the latter rooted in the mechanics of elastic materials. In this paper, a neural-based dynamic modeling approach is presented. The method used is proved to converge and experimental results are shown which support its applicability to real problems.

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SO dynamic deformation for building of 3-D models

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