Aurora image segmentation by combining patch and texture thresholding

Authors:
Xinbo Gao;Rong Fu;Xuelong Li;Dacheng Tao;Beichen Zhang;Huigen Yang
Affiliations:
School of Electronic Engineering, Xidian University, Xi'an 710071, Shaanxi, PR China;School of Electronic Engineering, Xidian University, Xi'an 710071, Shaanxi, PR China;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...;School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N4, 639798 Singapore, Singapore;SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, PR China;SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, PR China
Venue:
Computer Vision and Image Understanding
Year:
2011

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Abstract

The proportion of aurora to the field-of-view in temporal series of all-sky images is an important index to investigate the evolvement of aurora. To obtain such an index, a crucial phase is to segment the aurora from the background of sky. A new aurora segmentation approach, including a feature extraction method and the segmentation algorithm, is presented in this paper. The proposed feature extraction method, called adaptive local binary patterns (ALBP), selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in traditional local binary patterns. According to the different morphologies and different semantics of aurora, the segmentation algorithm is designed into two parts, texture part segmentation based on ALBP features and patch part segmentation based on modified Otsu method. As it is simple and efficient, our implementation is suitable for large-scale datasets. The experiments exhibited the segmentation effect of the proposed method is satisfactory from human visual aspect and segmentation accuracy.