IEICE TRANSACTIONS on Information
Hand-Shape Recognition Using the Distributions of Multi-Viewpoint Image Sets
Summary :
This paper proposes a method for recognizing hand-shapes by using multi-viewpoint image sets. The recognition of a hand-shape is a difficult problem, as appearance of the hand changes largely depending on viewpoint, illumination conditions and individual characteristics. To overcome this problem, we apply the Kernel Orthogonal Mutual Subspace Method (KOMSM) to shift-invariance features obtained from multi-viewpoint images of a hand. When applying KOMSM to hand recognition with a lot of learning images from each class, it is necessary to consider how to run the KOMSM with heavy computational cost due to the kernel trick technique. We propose a new method that can drastically reduce the computational cost of KOMSM by adopting centroids and the number of images belonging to the centroids, which are obtained by using k-means clustering. The validity of the proposed method is demonstrated through evaluation experiments using multi-viewpoint image sets of 30 classes of hand-shapes.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.6 pp.1619-1627
- Publication Date
- 2012/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.1619
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Yasuhiro OHKAWA, Kazuhiro FUKUI, "Hand-Shape Recognition Using the Distributions of Multi-Viewpoint Image Sets" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 6, pp. 1619-1627, June 2012, doi: 10.1587/transinf.E95.D.1619.
Abstract: This paper proposes a method for recognizing hand-shapes by using multi-viewpoint image sets. The recognition of a hand-shape is a difficult problem, as appearance of the hand changes largely depending on viewpoint, illumination conditions and individual characteristics. To overcome this problem, we apply the Kernel Orthogonal Mutual Subspace Method (KOMSM) to shift-invariance features obtained from multi-viewpoint images of a hand. When applying KOMSM to hand recognition with a lot of learning images from each class, it is necessary to consider how to run the KOMSM with heavy computational cost due to the kernel trick technique. We propose a new method that can drastically reduce the computational cost of KOMSM by adopting centroids and the number of images belonging to the centroids, which are obtained by using k-means clustering. The validity of the proposed method is demonstrated through evaluation experiments using multi-viewpoint image sets of 30 classes of hand-shapes.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.1619/_p
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@ARTICLE{e95-d_6_1619,
author={Yasuhiro OHKAWA, Kazuhiro FUKUI, },
journal={IEICE TRANSACTIONS on Information},
title={Hand-Shape Recognition Using the Distributions of Multi-Viewpoint Image Sets},
year={2012},
volume={E95-D},
number={6},
pages={1619-1627},
abstract={This paper proposes a method for recognizing hand-shapes by using multi-viewpoint image sets. The recognition of a hand-shape is a difficult problem, as appearance of the hand changes largely depending on viewpoint, illumination conditions and individual characteristics. To overcome this problem, we apply the Kernel Orthogonal Mutual Subspace Method (KOMSM) to shift-invariance features obtained from multi-viewpoint images of a hand. When applying KOMSM to hand recognition with a lot of learning images from each class, it is necessary to consider how to run the KOMSM with heavy computational cost due to the kernel trick technique. We propose a new method that can drastically reduce the computational cost of KOMSM by adopting centroids and the number of images belonging to the centroids, which are obtained by using k-means clustering. The validity of the proposed method is demonstrated through evaluation experiments using multi-viewpoint image sets of 30 classes of hand-shapes.},
keywords={},
doi={10.1587/transinf.E95.D.1619},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Hand-Shape Recognition Using the Distributions of Multi-Viewpoint Image Sets
T2 - IEICE TRANSACTIONS on Information
SP - 1619
EP - 1627
AU - Yasuhiro OHKAWA
AU - Kazuhiro FUKUI
PY - 2012
DO - 10.1587/transinf.E95.D.1619
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2012
AB - This paper proposes a method for recognizing hand-shapes by using multi-viewpoint image sets. The recognition of a hand-shape is a difficult problem, as appearance of the hand changes largely depending on viewpoint, illumination conditions and individual characteristics. To overcome this problem, we apply the Kernel Orthogonal Mutual Subspace Method (KOMSM) to shift-invariance features obtained from multi-viewpoint images of a hand. When applying KOMSM to hand recognition with a lot of learning images from each class, it is necessary to consider how to run the KOMSM with heavy computational cost due to the kernel trick technique. We propose a new method that can drastically reduce the computational cost of KOMSM by adopting centroids and the number of images belonging to the centroids, which are obtained by using k-means clustering. The validity of the proposed method is demonstrated through evaluation experiments using multi-viewpoint image sets of 30 classes of hand-shapes.
ER -
English
