The Hodgkin–Huxley neuron model for motion detection in image sequences
| Autor: | Olivier Lezoray, Dounia Yedjour, Hayat Yedjour, Boudjelal Meftah |
|---|---|
| Přispěvatelé: | Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran] (USTO MB), Department of Computer Science (University of Mascara), University Mustapha Stambouli [Mascara], Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU) |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Population Boundary (topology) Biological neuron model Hodgkin-Huxley model 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Receptive field education 030304 developmental biology Network model Spiking neural network 0303 health sciences education.field_of_study Spiking neural networks business.industry Pattern recognition Motion detection Real image [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Artificial intelligence Visual system business 030217 neurology & neurosurgery Software |
| Zdroj: | Neural Computing and Applications Neural Computing and Applications, Springer Verlag, In press, ⟨10.1007/s00521-021-06446-0⟩ |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-021-06446-0 |
| Popis: | International audience; In this paper, we consider a biologically inspired spiking neural network model for motion detection. The proposed model simulates the neurons' behavior in the cortical area MT to detect different kinds of motion in image sequences. We choose the conductance-based neuron model of the Hodgkin-Huxley to define MT cell responses. Based on the center-surround antagonism of MT receptive fields, we model the area MT by its great proportion of cells with directional selective responses. The network's spiking output corresponds to an MT neuron population's firing rates and enables to extract motion boundaries. We conduct several experiments on real image sequences. The experimental results show the proposed network's ability to segregate multiple moving objects from an image sequence and reproduce the MT cells' responses. We perform a quantitative evaluation on the YouTube Motion Boundaries (YMB) dataset, and we compare the result to stateof-the-art methods for boundary detection in videos: boundary flow estimation (BF) and temporal boundary difference (BD). The proposed network model provides the best results on YMB compared to BF and BD methods. |
| Databáze: | OpenAIRE |
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