Disparity energy model using a trained neuronal population

• Autor(es): Martins, Jaime A. ; Rodrigues, J. M. F. ; du Buf, J. M. H.
• Data: 2011
• Identificador Persistente: http://hdl.handle.net/10400.1/2078
• Origem: Sapientia - Universidade do Algarve
• Assunto(s): Visão humana; Córtex; Disparity; Biological model; Learning; Population coding
• Tipo de Documento: Documento de conferência
• Idioma: Inglês

Descrição

Depth information using the biological Disparity Energy Model can be obtained by using a population of complex cells. This model explicitly involves cell parameters like their spatial frequency, orientation, binocular phase and position difference. However, this is a mathematical model. Our brain does not have access to such parameters, it can only exploit responses. Therefore, we use a new model for encoding disparity information implicitly by employing a trained binocular neuronal population. This model allows to decode disparity information in a way similar to how our visual system could have developed this ability, during evolution, in order to accurately estimate disparity of entire scenes