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Magnetic resonance (MR) image classification generally performs slice by slice in which case training samples are slice-dependent. Each slice requires its own specific training samples and training samples obtained from one slice are not necessarily applicable to another slice. This paper develops a new approach to unsupervised classification for magnetic resonance images which consists of two stage...
This paper presents a new approach to unsupervised classification for multispectral imagery. It first implements the pixel purity index (PPI) which is commonly used in hyperspectral imaging for endmember extraction to find seed samples without prior knowledge, then uses the PPI-found samples as support vectors for a kernel-based support vector machine (SVM) to generate a set of initial training samples...
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