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Block principle component analysis (BPCA) is a recently developed technique in computer vision and pattern classification. In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An...
This paper proposed a novel scheme which gives mathematical description of 2D/3D measurement and includes two visual detection strategies with switching method by Hu moment. On the six degree of freedom serial robot, a rigid planar patch is overlaid as auxiliary measuring. In view of improve computing efficiency and robustness, a preprocess stage of the scheme based on visual attention mechanism is...
This paper presents an iteration self-adapting color image enhancement algorithm to extract visual attention focus in accuracy. Initially, the color space should be translated from RGB to YCbCr where the iteration image enhancement model is deduced in constrain of chroma and hue. The subsequence image evaluation function implements closed-up control to adjust the iteration step and enhancement performance...
Tensor principal component analysis (TPCA), which can make full use of the spatial relationship of images/videos, is a generalization of the classical principal component analysis (PCA). However, the existing TPCA method is based on the Frobenius-norm, which makes it sensitive to outliers. In order to overcome the drawback of TPCA, in this paper, we proposed a novel Lp-norm based TPCA (TPCA-Lp), which...
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