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Edge detection model based on involuntary eye movements of the eye-retina system

2007; Óbuda University; Volume: 4; Issue: 1 Linguagem: Inglês

2064-2687

András Róka, Ádám Csapó, Barna Reskó, Péter Bárányi,

Neural dynamics and brain function

Traditional edge-detection algorithms in image processing typically convolute a filter operator and the input image, and then map overlapping input image regions to output signals. Convolution also serves as a basis in biologically inspired (Sobel, Laplace, Canny) algorithms. Recent results in cognitive retinal research have shown that ganglion cell receptive fields cover the mammalian retina in a mosaic arrangement, with insignificant amounts of overlap in the central fovea. This means that the biological relevance of traditional and widely adapted edge-detection algorithms with convolutionbased overlapping operator architectures has been disproved. However, using traditional filters with non-overlapping operator architectures leads to considerable losses in contour information. This paper introduces a novel, tremor-based retina model and edge-detection algorithm that reconciles these differences between the physiology of the retina and the overlapping architectures used by today's widely adapted algorithms. The algorithm takes into consideration data convergence, as well as the dynamic properties of the retina, by incorporating a model of involuntary eye tremors and the impulse responses of ganglion cells. Based on the evaluation of the model, two hypotheses are formulated on the highly debated role of involuntary eye tremors: 1) The role of involuntary eye tremors has information theoretical implications 2) From an information processing point of view, the functional role of involuntary eye-movements extends to more than just the maintenance of action potentials. Involuntary eye-movements may be responsible for the compensation of information losses caused by a non-overlapping receptive field architecture. In support of these hypotheses, the article provides a detailed analysis of the model's biological relevance, along with numerical simulations and a hardware implementation.