IEICE TRANSACTIONS on Information
Active Contour Model Based on Salient Boundary Point Image for Object Contour Detection in Natural Image
Summary :
This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.11 pp.3136-3139
- Publication Date
- 2010/11/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.3136
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Yan LI, Siwei LUO, Qi ZOU, "Active Contour Model Based on Salient Boundary Point Image for Object Contour Detection in Natural Image" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 11, pp. 3136-3139, November 2010, doi: 10.1587/transinf.E93.D.3136.
Abstract: This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.3136/_p
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@ARTICLE{e93-d_11_3136,
author={Yan LI, Siwei LUO, Qi ZOU, },
journal={IEICE TRANSACTIONS on Information},
title={Active Contour Model Based on Salient Boundary Point Image for Object Contour Detection in Natural Image},
year={2010},
volume={E93-D},
number={11},
pages={3136-3139},
abstract={This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.},
keywords={},
doi={10.1587/transinf.E93.D.3136},
ISSN={1745-1361},
month={November},}
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TY - JOUR
TI - Active Contour Model Based on Salient Boundary Point Image for Object Contour Detection in Natural Image
T2 - IEICE TRANSACTIONS on Information
SP - 3136
EP - 3139
AU - Yan LI
AU - Siwei LUO
AU - Qi ZOU
PY - 2010
DO - 10.1587/transinf.E93.D.3136
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2010
AB - This paper combines the LBP operator and the active contour model. It introduces a salient gradient vector flow snake (SGVF snake), based on a novel edge map generated from the salient boundary point image (SBP image). The MDGVM criterion process helps to reduce feature detail and background noise as well as retaining the salient boundary points. The resultant SBP image as an edge map gives powerful support to the SGVF snake because of the inherent combination of the intensity, gradient and texture cues. Experiments prove that the MDGVM process has high efficiency in reducing outliers and the SGVF snake is a large improvement over the GVF snake for contour detection, especially in natural images with low contrast and small texture background.
ER -
English
