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- W2044496652 abstract "AbstractIn this paper, a bio-inspired neural network is developed to represent images and analysis features of images effectively. This model adopts schemes of retinal ganglion cells (GC) working and GCs’ non-classical receptive fields (nCRF) that can dynamically adjust their sizes/scales according to the visual information. Extensive experiments are provided to value the effect of image representing, and experimental results show that this neural network model can represent images at a low cost and with a favor in improving both segmentation and integration processing. Most importantly, the GC-array model provides a basic infrastructure for image semantic extraction.KeywordsnCRFImage representationMulti-scaleContour detection" @default.
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- W2044496652 date "2013-01-01" @default.
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- W2044496652 title "A General Image Representation Scheme and Its Improvement for Image Analysis" @default.
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- W2044496652 doi "https://doi.org/10.1007/978-3-642-40728-4_45" @default.
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