IEICE TRANSACTIONS on Information
Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition
Summary :
Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.5 pp.1116-1119
- Publication Date
- 2015/05/01
- Publicized
- 2015/02/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8254
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
An LIU
Tsinghua University
Maoyin CHEN
Tsinghua University
Donghua ZHOU
Tsinghua University
Keyword
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An LIU, Maoyin CHEN, Donghua ZHOU, "Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 5, pp. 1116-1119, May 2015, doi: 10.1587/transinf.2014EDL8254.
Abstract: Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8254/_p
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@ARTICLE{e98-d_5_1116,
author={An LIU, Maoyin CHEN, Donghua ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition},
year={2015},
volume={E98-D},
number={5},
pages={1116-1119},
abstract={Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.},
keywords={},
doi={10.1587/transinf.2014EDL8254},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1116
EP - 1119
AU - An LIU
AU - Maoyin CHEN
AU - Donghua ZHOU
PY - 2015
DO - 10.1587/transinf.2014EDL8254
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2015
AB - Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.
ER -
English
