Colour in Context
Research group
Computer Vision Center



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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 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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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 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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Chromatic Settings: new colour constancy paradigm
In our study, we developed a new colour constancy paradigm, termed chromatic setting, which allows measuring the precise location of nine categorically-relevant points in colour space under immersive illumination. Additionally, we derived from these measurements a new colour constancy index which takes into account the magnitude and orientation of the chromatic shift, memory effects and the interrelations among colours and a model of colour naming tuned to each observer/adaptation state.
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Image Compression
The aim of this work is to apply perceptual concepts to defina a perceptual pre-quantizer in order to improve image compression algorithms.
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