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Motion Boundary Detection Improved by Bio-Inspired Approach

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Motion detection in video sequences is a problem that different techniques and algorithms have faced. Several computer vision applications require an efficient detection stage. A reliable motion detection system should work under a wide range of a variety of scenes and various acquisition conditions. In this paper, a new approach to detect moving objects boundaries in an image sequence is presented. The proposed approach is based on the frame differing of modified Haynes technique combined with a biologically inspired spiking neural network for boundaries detection. First, the detection of temporal changes consists in extracting the moving objects between two consecutive frames in a dynamic scene without any estimate of the optical flow, nor any priory on the stage as the background image. The main goal is to get a fast and effective detection. In order to obtain a motion boundaries information, the extracted moving objects are then presented to a spiking neural network based on the Hodgkin-Huxley neuron model whose responses can form a complete closed curve coinciding with the motion object boundaries. Simulation results in some experiments are satisfactory. The performance of the motion boundary detection system is compared against competing boundary detection method using the YouTube Motion Boundaries dataset (YMB) dataset. The comparison results obtained confirm the robustness of the proposed approach. The extracted boundaries objects results can be used in further applications.
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Spiking Neural Network; Hodgkin-Huxley Model; Motion Segmentation; Boundary Detection; (YMB) Dataset

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