Bio-inspired computational object classification model for object recognition |
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| Affiliation: | 1. Ruhr-University Bochum, Institute for Philosophy II, Bochum, Germany;2. The University of Edinburgh, School of Philosophy, Psychology and Language Sciences, United Kingdom;3. Amsterdam University Medical Centre, Department of Psychiatry, Netherlands;4. Amsterdam Brain and Cognition, Netherlands;1. Henley Business School, Business Informatics Systems and Accounting, Informatics Research Centre, University of Reading, RG6 6UD, United Kingdom;2. School of Psychology and Clinical Language Sciences, University of Reading, Whiteknights Road, RG6 6AL, United Kingdom;3. Glasgow School for Business and Society, Glasgow Caledonian University, Cowcadden Road, G4 0BA, United Kingdom;1. Department of Computer Science, Cinvestav-IPN Unidad Guadalajara, Av. del Bosque #1145, 45019 Zapopan Mexico;2. Department of Computer Science, Universidad Autónoma del Estado de México, Cerro de Coatepec, Paseo Universidad s/n, Universitaria, 50130 Toluca, Mexico;3. Department of Well-Being and Sustainable Development, Centro Universitario del Norte de la Universidad de Guadalajara, Guadalajara, Mexico |
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| Abstract: | Human beings can effortlessly perceive stimuli through their sensory systems to learn, understand, recognize and act on our environment or context. Over the years, efforts have been made to enable cybernetic entities to be close to performing human perception tasks; and in general, to bring artificial intelligence closer to human intelligence.Neuroscience and other cognitive sciences provide evidence and explanations of the functioning of certain aspects of visual perception in the human brain. Visual perception is a complex process, and its has been divided into several parts. Object classification is one of those parts; it is necessary for carrying out the declarative interpretation of the environment. This article deals with the object classification problem.In this article, we propose a computational model of visual classification of objects based on neuroscience, it consists of two modular systems: a visual processing system, in charge of the extraction of characteristics; and a perception sub-system, which performs the classification of objects based on the features extracted by the visual processing system.With the results obtained, a set of aspects are analyzed using similarity and dissimilarity matrices. Also based on the neuroscientific evidence and the results obtained from this research, some aspects are suggested for consideration to improve the work in the future and bring us closer to performing the task of visual classification as humans do. |
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| Keywords: | Perception Cognitive architecture Object recognition Computer vision Visual sensory system |
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