We use the wavelet-based decomposition to generate the multiresolution representation of dermatoscopic images of potentially malignant pigmented lesions. Three different machine learning methods are experimentally applied, namely neural networks, support vector machines, and Attributional Calculus. The obtained results confirm that neighborhood properties of pixels in dermatoscopic images are a sensitive probe of the melanoma progression and together with the selected machine learning methods may be an important diagnostic tool.
Abstract. The paper deals with visual computational design in which emergence is a key to creativity. The presented framework for conceptual design uses shape grammars and curious agent assistants. The intelligent agents perceive the changing environment and emergent phenomena that occur in it. Interacting with each other and the designer they look for the most original and plausible solutions to a given design task. The approach is illustrated by the example of a designing floor-layouts.
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