Object modeling from multiple images using genetic algorithms

Hideo Saito; Masayuki Mori

doi:10.1109/ICPR.1996.547649

Object modeling from multiple images using genetic algorithms

Hideo Saito, Masayuki Mori

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This paper describes an application of genetic algorithms (GAs) to modeling of multiple objects from CCD images. Shape modeling is a very important issue for shape recognition for robot vision, representing 3-D shapes in the virtual world, and so on. In this paper, we propose a method for object modeling from multiple view images using genetic algorithms (GAs). In this method, similarity between the model and the image at each view angle is evaluated. The model having the maximum evaluation is found by GAs. In the proposed method, a sharing scheme is used for finding multiple solutions efficiently. Some results of object modeling experiments from synthetic and real multiple view images demonstrate that the proposed method can robustly generate models by using GAs.

Original language	English
Title of host publication	Track D
Subtitle of host publication	Parallel and Connectionist Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	669-673
Number of pages	5
ISBN (Print)	081867282X, 9780818672828
DOIs	https://doi.org/10.1109/ICPR.1996.547649
Publication status	Published - 1996
Event	13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria Duration: 1996 Aug 25 → 1996 Aug 29

Publication series

Name	Proceedings - International Conference on Pattern Recognition
Volume	4
ISSN (Print)	1051-4651

Other

Other	13th International Conference on Pattern Recognition, ICPR 1996
Country	Austria
City	Vienna
Period	96/8/25 → 96/8/29

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICPR.1996.547649

Fingerprint Dive into the research topics of 'Object modeling from multiple images using genetic algorithms'. Together they form a unique fingerprint.

Cite this

Object modeling from multiple images using genetic algorithms. / Saito, Hideo; Mori, Masayuki.

Track D: Parallel and Connectionist Systems. Institute of Electrical and Electronics Engineers Inc., 1996. p. 669-673 547649 (Proceedings - International Conference on Pattern Recognition; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Saito, H & Mori, M 1996, Object modeling from multiple images using genetic algorithms. in Track D: Parallel and Connectionist Systems., 547649, Proceedings - International Conference on Pattern Recognition, vol. 4, Institute of Electrical and Electronics Engineers Inc., pp. 669-673, 13th International Conference on Pattern Recognition, ICPR 1996, Vienna, Austria, 96/8/25. https://doi.org/10.1109/ICPR.1996.547649

@inproceedings{de435e3464994c44b0af5022fab866af,

title = "Object modeling from multiple images using genetic algorithms",

abstract = "This paper describes an application of genetic algorithms (GAs) to modeling of multiple objects from CCD images. Shape modeling is a very important issue for shape recognition for robot vision, representing 3-D shapes in the virtual world, and so on. In this paper, we propose a method for object modeling from multiple view images using genetic algorithms (GAs). In this method, similarity between the model and the image at each view angle is evaluated. The model having the maximum evaluation is found by GAs. In the proposed method, a sharing scheme is used for finding multiple solutions efficiently. Some results of object modeling experiments from synthetic and real multiple view images demonstrate that the proposed method can robustly generate models by using GAs.",

author = "Hideo Saito and Masayuki Mori",

year = "1996",

doi = "10.1109/ICPR.1996.547649",

language = "English",

isbn = "081867282X",

series = "Proceedings - International Conference on Pattern Recognition",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "669--673",

booktitle = "Track D",

}

TY - GEN

T1 - Object modeling from multiple images using genetic algorithms

AU - Saito, Hideo

AU - Mori, Masayuki

PY - 1996

Y1 - 1996

N2 - This paper describes an application of genetic algorithms (GAs) to modeling of multiple objects from CCD images. Shape modeling is a very important issue for shape recognition for robot vision, representing 3-D shapes in the virtual world, and so on. In this paper, we propose a method for object modeling from multiple view images using genetic algorithms (GAs). In this method, similarity between the model and the image at each view angle is evaluated. The model having the maximum evaluation is found by GAs. In the proposed method, a sharing scheme is used for finding multiple solutions efficiently. Some results of object modeling experiments from synthetic and real multiple view images demonstrate that the proposed method can robustly generate models by using GAs.

AB - This paper describes an application of genetic algorithms (GAs) to modeling of multiple objects from CCD images. Shape modeling is a very important issue for shape recognition for robot vision, representing 3-D shapes in the virtual world, and so on. In this paper, we propose a method for object modeling from multiple view images using genetic algorithms (GAs). In this method, similarity between the model and the image at each view angle is evaluated. The model having the maximum evaluation is found by GAs. In the proposed method, a sharing scheme is used for finding multiple solutions efficiently. Some results of object modeling experiments from synthetic and real multiple view images demonstrate that the proposed method can robustly generate models by using GAs.

UR - http://www.scopus.com/inward/record.url?scp=84898775649&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898775649&partnerID=8YFLogxK

U2 - 10.1109/ICPR.1996.547649

DO - 10.1109/ICPR.1996.547649

M3 - Conference contribution

AN - SCOPUS:84898775649

SN - 081867282X

SN - 9780818672828

T3 - Proceedings - International Conference on Pattern Recognition

SP - 669

EP - 673

BT - Track D

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th International Conference on Pattern Recognition, ICPR 1996

Y2 - 25 August 1996 through 29 August 1996

ER -