IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition
An LIUMaoyin CHENDonghua ZHOU
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2015 Volume E98.D Issue 5 Pages 1116-1119

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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.

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© 2015 The Institute of Electronics, Information and Communication Engineers
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