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In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Abstract In this paper, we propose a novel robust line orientation code for palmprint(More)
In this paper, we propose a novel palmprint verification approach based on principal lines. In feature extraction stage, the modified finite Radon transform is proposed, which can extract principal lines effectively and efficiently even in the case that the palmprint images contain many long and strong wrinkles. In matching stage, a matching algorithm based(More)
In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC(More)
The purpose of this study is to discuss existing fractal-based algorithms and propose novel improvements of these algorithms to identify tumors in brain magnetic-response (MR) images. Considerable research has been pursued on frac-tal geometry in various aspects of image analysis and pattern recognition. Magnetic-resonance images typically have a degree of(More)
Keywords: Sparse neighborhood preserving embedding Sparse subspace learning Discriminant learning Maximum margin criterion Discriminant sparse neighborhood preserving embedding Face recognition a b s t r a c t Sparse subspace learning has drawn more and more attentions recently. However, most of the sparse subspace learning methods are unsupervised and(More)
Tumor classification based on Gene Expression Profiles (GEPs), which is of great benefit to the accurate diagnosis and personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden(More)