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NICE : A Computational solution to close the gap from colour perception to colour categorization
Pàrraga, Alejandro (Centre de Visió per Computador)
Akbarinia, Arash (Centre de Visió per Computador)

Data: 2016
Resum: The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms.
Nota: Número d'acord de subvenció TIN2013-41751-P
Nota: Número d'acord de subvenció TIN2013-49982-EXP
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: article ; recerca ; publishedVersion
Matèria: Paraules clau fora catàleg
Publicat a: PLoS one, Vol. 11, Num. 3 (2016) , p. e0149538-e0149538, ISSN 1932-6203

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DOI: 10.1371/journal.pone.0149538

32 p, 6.6 MB

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