Image coding using Leaky Integrate-and-Fire neurons

Abstract : This paper aims to build an image coding system based on the model of the mammalian retina. The retina is the light-sensitive layer of tissue located on the inner coat of the eye and it is responsible for vision. Inspired by the way the retina handles and compresses the visual information and based on previous studies we aim to build and analytically study a retinal-inspired image quantizer, based on the Leaky Integrate-and-Fire (LIF) model, a neural model according to which function the ganglion cells of the Ganglionic retinal layer that is responsible for the visual data compression. In order to have a more concrete view of the encoder's behavior, in our experiments, we make use of the spatiotemporal decomposition layers provided by extensive previous studies on a previous retinal layer, the Outer Plexiform Layer (OPL). The decomposition layers produced by the OPL, are being encoded using our LIF image encoder and then, they are reconstructed to observe the en-coder's efficiency.
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Communication dans un congrès
CORESA 2017, Nov 2017, Caen, France
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Dernière modification le : lundi 4 décembre 2017 - 15:14:19

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  • HAL Id : hal-01650721, version 1

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Melpomeni Dimopoulou, Effrosyni Doutsi, Marc Antonini. Image coding using Leaky Integrate-and-Fire neurons. CORESA 2017, Nov 2017, Caen, France. 〈hal-01650721〉

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