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    Probability density estimation using new kernel functions 

    Manachai Rodchuen; Prachoom Suwattee, advisor (National Institute of Development Administration, 2010)

    This dissertation propose two new kernel functions to estimate a density unction with small biases and errors .The errors of the estimats are measured using mean squared error (MSEf (f(x,X ))), mean integrated squared error (MISE(f) and asymptotic mean integrated squared error (AMISE(f) ), and The estimates of these error measures are also given .The AMISE0(f) of the density estimates are compared to the kernel density estimates for the uniform, Epanechnikov and Gaussian kernel functions. One kernel function is derived by minimizing the sum of ...
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    Texture classification using an invariant texture representation and a tree matching kernel 

    Somkid Soottitantawat; Surapong Auwatanamongkol, advisor (National Institute of Development Administration, 2010)

    The real world is rich in many textures, which can be regarded as the visual appearance of surfaces. They may be perceived as being smooth or rough, coarse or fine, homogeneous or non-homogeneous, etc. Moreover, textures within real images vary in scale, rotation and illumination. Several researchers have proposed texture analysis methods to describe textures in many applications, such as computer vision, pattern recognition, image retrieval, scene image analysis, and so on. Although the analysis of texture properties has attracted the ...