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This paper introduces a multi-class classification algorithm based on sparse representation which considers on rejection option to minimize risks caused by outliers. Here the outliers include signals that do not belong to any classes learned in a training step. To successfully reject the outliers, new rejection measure and corresponding dictionary learning algorithm are presented. Experimental results...
This paper proposes a novel discriminative dictionary learning algorithm for face recognition. A benefit of proposed algorithm is that it can learn discriminative dictionary by represented sparse coding. Our proposed algorithm makes more discriminative features than previous by using the discriminative dictionary. Face recognition experiment is performed for Extended Yale B face dataset and shows...
This paper proposes dark image enhancement algorithm based on dictionary learning(DL). When image is dark, many vision based robotic applications cannot work well. Existing dark image enhancement algorithms have defects. For example, histogram modification results in color information losses and gamma correction makes degradation in dark area. Proposed algorithm preserves color information and provides...
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