IEICE TRANSACTIONS on Information
GOCD: Gradient Order Curve Descriptor
Summary :
In this paper, the concept of gradient order is introduced and a novel gradient order curve descriptor (GOCD) for curve matching is proposed. The GOCD is constructed in the following main steps: firstly, curve support region independent of the dominant orientation is determined and then divided into several sub-regions based on gradient magnitude order; then gradient order feature (GOF) of each feature point is generated by encoding the local gradient information of the sample points; the descriptor is finally achieved by turning to the description matrix of GOF. Since both the local and the global gradient information are captured by GOCD, it is more distinctive and robust compared with the existing curve matching methods. Experiments under various changes, such as illumination, viewpoint, image rotation, JPEG compression and noise, show the great performance of GOCD. Furthermore, the application of image mosaic proves GOCD can be used successfully in actual field.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.12 pp.2973-2983
- Publication Date
- 2017/12/01
- Publicized
- 2017/09/15
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7097
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Hongmin LIU
Henan Polytechnic University
Lulu CHEN
Henan Polytechnic University
Zhiheng WANG
Henan Polytechnic University
Zhanqiang HUO
Henan Polytechnic University
Keyword
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Hongmin LIU, Lulu CHEN, Zhiheng WANG, Zhanqiang HUO, "GOCD: Gradient Order Curve Descriptor" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 12, pp. 2973-2983, December 2017, doi: 10.1587/transinf.2017EDP7097.
Abstract: In this paper, the concept of gradient order is introduced and a novel gradient order curve descriptor (GOCD) for curve matching is proposed. The GOCD is constructed in the following main steps: firstly, curve support region independent of the dominant orientation is determined and then divided into several sub-regions based on gradient magnitude order; then gradient order feature (GOF) of each feature point is generated by encoding the local gradient information of the sample points; the descriptor is finally achieved by turning to the description matrix of GOF. Since both the local and the global gradient information are captured by GOCD, it is more distinctive and robust compared with the existing curve matching methods. Experiments under various changes, such as illumination, viewpoint, image rotation, JPEG compression and noise, show the great performance of GOCD. Furthermore, the application of image mosaic proves GOCD can be used successfully in actual field.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7097/_p
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@ARTICLE{e100-d_12_2973,
author={Hongmin LIU, Lulu CHEN, Zhiheng WANG, Zhanqiang HUO, },
journal={IEICE TRANSACTIONS on Information},
title={GOCD: Gradient Order Curve Descriptor},
year={2017},
volume={E100-D},
number={12},
pages={2973-2983},
abstract={In this paper, the concept of gradient order is introduced and a novel gradient order curve descriptor (GOCD) for curve matching is proposed. The GOCD is constructed in the following main steps: firstly, curve support region independent of the dominant orientation is determined and then divided into several sub-regions based on gradient magnitude order; then gradient order feature (GOF) of each feature point is generated by encoding the local gradient information of the sample points; the descriptor is finally achieved by turning to the description matrix of GOF. Since both the local and the global gradient information are captured by GOCD, it is more distinctive and robust compared with the existing curve matching methods. Experiments under various changes, such as illumination, viewpoint, image rotation, JPEG compression and noise, show the great performance of GOCD. Furthermore, the application of image mosaic proves GOCD can be used successfully in actual field.},
keywords={},
doi={10.1587/transinf.2017EDP7097},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - GOCD: Gradient Order Curve Descriptor
T2 - IEICE TRANSACTIONS on Information
SP - 2973
EP - 2983
AU - Hongmin LIU
AU - Lulu CHEN
AU - Zhiheng WANG
AU - Zhanqiang HUO
PY - 2017
DO - 10.1587/transinf.2017EDP7097
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2017
AB - In this paper, the concept of gradient order is introduced and a novel gradient order curve descriptor (GOCD) for curve matching is proposed. The GOCD is constructed in the following main steps: firstly, curve support region independent of the dominant orientation is determined and then divided into several sub-regions based on gradient magnitude order; then gradient order feature (GOF) of each feature point is generated by encoding the local gradient information of the sample points; the descriptor is finally achieved by turning to the description matrix of GOF. Since both the local and the global gradient information are captured by GOCD, it is more distinctive and robust compared with the existing curve matching methods. Experiments under various changes, such as illumination, viewpoint, image rotation, JPEG compression and noise, show the great performance of GOCD. Furthermore, the application of image mosaic proves GOCD can be used successfully in actual field.
ER -
English
