Shape recovery from an unorganized image sequence

Kazuhiko Kawamoto; Atsushi Imiya; Kaoru Hirota

doi:10.1007/3-540-45065-3_34

Shape recovery from an unorganized image sequence

Kazuhiko Kawamoto^*
, Atsushi Imiya
, Kaoru Hirota

^*Corresponding author for this work

Institute of Science Tokyo
Chiba University
Research Organization of Information and Systems, National Institute of Informatics

Research output: Contribution to journal › Conference article › peer-review

Abstract

We propose a method for recovering a 3D object from an unorganized image sequence, in which the order of the images and the corresponding points among the images are unknown, using a random sampling and voting process. Least squares methods such that the factorization method and the 8-point algorithm are not directly applicable to an unorganized image sequence, because the corresponding points are a priori unknown. The proposed method repeatedly generates relevant shape parameters from randomly sampled data as a series of hypotheses, and finally produces the solutions supported by a large number of the hypotheses. The method is demonstrated on synthetic and real data.

Original language	English
Pages (from-to)	389-399
Number of pages	11
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2734
DOIs	https://doi.org/10.1007/3-540-45065-3_34
Publication status	Published - 2003
Externally published	Yes
Event	Third International Conference, MLDM 2003 - Leipzig, Germany Duration: 5 Jul 2003 → 7 Jul 2003

Keywords

Hough transform
Random sampling
Shape recovery
Unorganized image sequence
Voting

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10.1007/3-540-45065-3_34

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title = "Shape recovery from an unorganized image sequence",

abstract = "We propose a method for recovering a 3D object from an unorganized image sequence, in which the order of the images and the corresponding points among the images are unknown, using a random sampling and voting process. Least squares methods such that the factorization method and the 8-point algorithm are not directly applicable to an unorganized image sequence, because the corresponding points are a priori unknown. The proposed method repeatedly generates relevant shape parameters from randomly sampled data as a series of hypotheses, and finally produces the solutions supported by a large number of the hypotheses. The method is demonstrated on synthetic and real data.",

keywords = "Hough transform, Random sampling, Shape recovery, Unorganized image sequence, Voting",

author = "Kazuhiko Kawamoto and Atsushi Imiya and Kaoru Hirota",

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TY - JOUR

T1 - Shape recovery from an unorganized image sequence

AU - Kawamoto, Kazuhiko

AU - Imiya, Atsushi

AU - Hirota, Kaoru

PY - 2003

Y1 - 2003

N2 - We propose a method for recovering a 3D object from an unorganized image sequence, in which the order of the images and the corresponding points among the images are unknown, using a random sampling and voting process. Least squares methods such that the factorization method and the 8-point algorithm are not directly applicable to an unorganized image sequence, because the corresponding points are a priori unknown. The proposed method repeatedly generates relevant shape parameters from randomly sampled data as a series of hypotheses, and finally produces the solutions supported by a large number of the hypotheses. The method is demonstrated on synthetic and real data.

AB - We propose a method for recovering a 3D object from an unorganized image sequence, in which the order of the images and the corresponding points among the images are unknown, using a random sampling and voting process. Least squares methods such that the factorization method and the 8-point algorithm are not directly applicable to an unorganized image sequence, because the corresponding points are a priori unknown. The proposed method repeatedly generates relevant shape parameters from randomly sampled data as a series of hypotheses, and finally produces the solutions supported by a large number of the hypotheses. The method is demonstrated on synthetic and real data.

KW - Hough transform

KW - Random sampling

KW - Shape recovery

KW - Unorganized image sequence

KW - Voting

UR - https://www.scopus.com/pages/publications/8344278039

U2 - 10.1007/3-540-45065-3_34

DO - 10.1007/3-540-45065-3_34

M3 - Conference article

AN - SCOPUS:8344278039

SN - 0302-9743

VL - 2734

SP - 389

EP - 399

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - Third International Conference, MLDM 2003

Y2 - 5 July 2003 through 7 July 2003

ER -

Shape recovery from an unorganized image sequence

Abstract

Keywords

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