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The CVC colour group focuses its research in areas related to computational colour within computer vision. Our long term objective is to create computer algorithms that simulate human perception and categorisation of colour. To achieve this aim, we study colour as a visual cue in its context. Our main research lines are colour constancy, induction, saliency, texture,segmentation and naming.

Color Feature Detection for Object Recognition Luminance edges are still the main source of information in the state-of-the-art methods for feature detection. We propose to exploit the statistical structure of luminance and color in natural images to extract the most discriminative features from the viewpoint of information theory for object recognition.

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Parametric model for Color Naming A full parametric model has been defined on the CIE lab space. Each of the 11 basic colour categories is modelled with a fuzzy set characterized by a combination of sigmoids as membership functions.

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Color-Texture descriptors We propose color-texture descriptors that are directly based on a perceptual theory of texture discrimination (Julesz’s Texton Theory).

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Psychophysical Error Measure for Colour Constancy In this paper we propose a new evaluation in order to compare solutions of different colour constancy algorithms. this new approach is based on a psychophysical experiment to relate this new evaluation with human perception instead of physical properties.

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Color attention for object recognition We propose a novel image representation where color attention is used to sample the shape description of the image.

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