Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
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The crevasse, which has always been one of the most dangerous factors on the Antarctic continent, threatens the life of the team members during the polar expedition. Crevasse detection is thus an increasingly important issue as it facilitates the analysis of glaciers and ice cap movements, research on the effects of climate change, and improves security for expedition staff. In this paper, we first analyze the characteristics of crevasse in ASTER image. We then test five features: Gray-Level Co-occurrence Matrices (GLCM), Gabor filters, Local Phase Quantization (LPQ), the completed local binary pattern (CLBP), and local self-similarity (LSS) for crevasse detection with the SVM classifier. Finally, we evaluate and validate the detection performance on two datasets. Experimental results show that the LSS descriptor performs better than other descriptors, and is thus a promising feature descriptor for crevasse detection.