DOUTSI , Effrosyni

The goal of this thesis is to propose a novel video coding architecture which is inspired by the mammalian visual system and the retina. If one sees the retina as a machine which processes the visual stimulus, it seems an intelligent and very efficient model to mimic. There are several reasons to claim that, first of all because it consumes low...

International audience

This paper aims to build an image coding system based on a 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 visual information and based on previous studies we aim to build and analytically...

During the last decade, there is an ever increasing interest about the decryption and analysis of the human visual system, which offers an intelligent mechanism for capturing and transforming the visual stimulus into a very dense and informative code of spikes. The compression capacity of the visual system is beyond the latest image and video...

In this paper, we discover the special properties of neurons in terms of compression. Neurons are able to transform a visual stimulus into a sequence of discrete biphasic events, called spikes trains, forming the neural code. The neural spike generation properties are beneficial to image processing community as the neural code is very compact,...

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...

The goal of this paper is to investigate whether purely neuro-mimetic architectures are more efficient for signal compression than architectures that combine neuroscience and state-of-the-art models. We are motivated to produce spikes, using the LIF model, in order to compress images. Seeking for solutions to improve the efficiency of the LIF...

International audience

International audience

In this paper, we aim to propose a video codec based on the novel retina-inspired filter and retina-inspired quantizer which both perform according to the early visual system. The recently released non-separable spatiotemporal OPL retina-inspired filter enables to progressively extract different kind of information from the input signal which...

International audience

This paper introduces a novel filter which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific...

This paper presents a novel neuro-inspired quantization model which is the extension of the recently released perfect-Leaky Integrate and Fire (LIF) model. We propose that the LIF, which is a very efficient neuromathematical model that describes the spike generation neural mechanism, can lead to a groundbreaking and above all dynamic...

This paper introduces a novel retina-inspired sparse representation which is applied to temporally constant 2D inputs. This architecture consists of the recently released retina-inspired filtering which mimics the transformation of the visual stimulus into current as it takes place in the retina. This transform is very redundant. As a result,...

International audience

This paper introduces a novel coding/decoding mechanism that mimics one of the most important properties of the human visual system: its ability to enhance the visual perception quality in time. In other words, the brain takes advantage of time to process and clarify the details of the visual scene. This characteristic is yet to be considered...

-Cet article présente une nouvelle architecture bio-inspirée pour encoder, puis décoder, un signal 1D. Un filtrage spatio-temporel est appliqué au signal d'entrée afin de le décomposer en plusieurs composantes. Ensuite, chaque composante estéchantillonnée par un neurone du type "intègre-et-tire". Cetéchantillonneur produit ainsi un train...