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However, the contribution vector is based on edge features vector.
Tian et al. [15] proposed a neural network (NN) to recognize different AUs based on edge features, face wrinkles, and shape.
Contrast enhancement was applied for each level separately, and edge features were extracted in each level and decomposition coefficients were modified based on edge features.
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The features are based on edge orientation, the configurations of these edges, and on grey level clustering.
Herein, we only consider transitive reduction based on edge removals.
HOG [44] introduced another texture-based method which uses high-dimensional features based on edges and then applies SVM to detect human body regions.
It was also observed that the spiculation feature based on edge-signature information was distinctly better than the rest of the features, although it is not very robust to changes in the quality of the segmentation.
Texture-based methods such as histograms of oriented gradient (HOG) [44] use high dimensional features based on edges and use support vector machine (SVM) to detect human regions.
The metrics are based on edge-width analyses.
That is,, where denote respective features based on color, edge response, phase angle, edge length, and edge response at edge points.
E DGEB ETWEENNESS-BNM (E DGEB ETWEENNESS for biological network motif) algorithm removes edges based on its edge betweenness score.
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