Nonparametric Pixel Appearance Probability Model Using Grid Quantization for Local Image Information Representation

We describe a nonparametric pixel appearance probability model to represent local image information. It allows an optimal image analysis framework that integrates lowand high-level stages to substantially improve overall accuracy of object reconstruction. In this framework, feature detection would be an overall consequence rather than an intermediate result. The pixel appearance probability model is a probability density function obtained by grid quantization. The grid is found by a genetic algorithm and a local refinement algorithm. The density values are computed by smoothing neighboring cells. We apply the pixel appearance probability model to represent features of echocardiographic images. We illustrate the substantially improved performance on left ventricle surface reconstruction due to the proposed pixel appearance probability model.