title: Lateral inhibition pyramidal neural network for image classification.
abstract: The human visual system is one of the most fascinating and complex mechanisms of the central nervous system that enables our capacity to see. It is through the visual system that we are able to accomplish from the most simple task such as object recognition to the most complex visual interpretation, understanding and perception. Inspired by this sophisticated system, two models based on the properties of the human visual system are proposed. These models are designed based on the concepts of receptive and inhibitory fields. The first model is a pyramidal neural network with lateral inhibition, called lateral inhibition pyramidal neural network. The second proposed model is a supervised image segmentation system, called segmentation and classification based on receptive fields. This work shows that the combination of these two models is beneficial, and the results obtained are better than that of other state-of-the-art methods.
authors.forename: Bruno José Torres
authors.initials: BJ
authors.lastname: Fernandes
authors.forename: George D C
authors.initials: GD
authors.lastname: Cavalcanti
authors.forename: Tsang Ing
authors.initials: TI
authors.lastname: Ren
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual / Q000502:physiology*; D017966:Pyramidal Cells / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*