Learn More
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)
The Chinese University of Hong Kong holds the copyright of this thesis. Any person(s) intending to use a part or the whole of the materials in this thesis in a proposed publication must seek copyright release from the Dean of the Graduate School. Abstract Support Vector Regression (SVR) has been applied successfully to financial time series prediction(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 on the Web is identified, which is resulted from the fact that there are many similar characters in the Universal Character Set (UCS). The foundation of our solution relies on evaluating the similarity of characters in UCS. We develop a solution based on the renowned Kernel Density Estimation(More)
In this paper, a novel, adaptive noise reduction method for engineering drawings is proposed based on assessment of both primitives and noise. Unlike the current approaches, our method takes into account the special features of engineering drawings and assesses the characteristics of primitives and noise such that adaptive procedures and parameters are(More)