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Self-supervised texture segmentation using complementary types of features
2001
Pattern Recognition
A two-stage texture segmentation approach is proposed where an initial segmentation map is obtained through unsupervised clustering of multiresolution simultaneous autoregressive (MRSAR) features and is followed by self-supervised classification of wavelet features. The regions of "high confidence" and "low confidence" are identified based on the MRSAR segmentation result using multilevel morphological erosion. The second-stage classifier is trained by the "high-confidence" samples and is used
doi:10.1016/s0031-3203(00)00146-1
fatcat:n7vnfrtahvalnonkg6ep2wa7vi