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

^*此作品的通信作者

研究成果: 雜誌貢獻 › 期刊論文 › 同行評審

11 引文斯高帕斯（Scopus）

摘要

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.

原文	英語
頁（從 - 到）	374-387
頁數	14
期刊	IEEE Transactions on Neural Networks
卷	7
發行號	2
DOIs	https://doi.org/10.1109/72.485673
出版狀態	已發佈 - 1996

ASJC Scopus subject areas

軟體
電腦科學應用
電腦網路與通信
人工智慧

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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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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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