In this paper we present a new complete detectordescriptor framework for local features extraction from grayscale texture-plus-depth images. It is designed by putting together a locally normalized binary descriptor and the popular AGAST corner detector modified to incorporate the...
The proposed method combines Local Ternary Pattern with Booth's Algorithm for enhanced feature extraction.The textures of the facial regions are locally encoded by the LBP patterns while the whole shape of the face is recovered by the construction of the face feature histogram.
Many methods based on texture features have been proposed to solve this problem. Most of the existing algorithms only estimate crowd density on the whole image while ignore crowd density in local region.
MT-ULTP extracts local significant texture information in different locality levels. In this respect, local ternary patterns are extracted in different thresholds and finally the occurrence probability of the uniform patterns is extracted as features.
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35 propose an approach that combines image processing, feature extraction, and feature selection techniques to improve palm vein recognition performance. The approach uses local texture features, and Gabor filters to capture unique characteristics of palm vein patterns...
In this paper, we propose a novel method on such an issue by combining both boosted local texture and shape features extracted from 3D face models, in contrast to the existing ones that only depend on 2D facial images.
In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture...
