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We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach(More)
Visual similarity evaluation plays an important role in intelligent graphics system. In this paper, we focus on the domain of symbolic image recognition and introduce the Directional Division Tree representation to extract and describe the content information of an image. The conducted experiment shows that similarity evaluation algorithm based on this(More)
A severe potential security problem in utilization of Unicode in the Web is identified, which is resulted from the fact that there are many similar characters in the Unicode Character Set (UCS). The foundation of our solution relies on evaluating the similarity of characters in UCS. We develop a solution bsed on the renowned Kernel Density Estimation (KDE)(More)
To provide a means of identification of human emotion in walking, this paper analyzes the capability of walking activity to reveal a person's affective states. We obtain pure wrist and ankle accelerometer data, because of redundant information existing in high dimension data, then we set different w(moving average filter window size) and utilize principal(More)