Internal symmetry networks for image processing

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Copyright: Li, Guanzhong
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Abstract
Internal Symmetry Networks are a recently developed class of Cellular Neural Network inspired by the phenomenon of internal symmetry in quantum physics. Their hidden unit activations are acted on non-trivially by the dihedral group of symmetries of the square. Here, we extend Internal Symmetry Networks to include recurrent connections, and train them by backpropagation to perform a variety of image processing tasks, smoothing, sharpening, edge detection, synthetic image segmentation, texture segmentation and object recognition. By a large number of experiments, we find some guidelines to construct appropriate configurations of the net for different tasks.
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Author(s)
Li, Guanzhong
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Publication Year
2009
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Thesis
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Masters Thesis
UNSW Faculty
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