Colour in Context
Research group
Computer Vision Center


Research projects

Description

Publications


Camera characterization
There seems to be a feature of our work as computer vision or visual perception scientists that we sooner or later run into the need of a reliable database of calibrated natural images.
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Visual and Semantic Image Retrieval
We have implemented a prototype to evaluate the influence of semantic labels for image retrieval. The semantic labels are learned with a bag-of-words approach on a training set. The classifier is then used to automatically label all data in the image retrieval experiment.
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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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High Dynamic Range
We try to perform a High Dynamic Range compression of color images using perceptual criterias.
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Bottom-up visual saliency
In this project, we obtain saliency maps from color images using perceptual characteristics.
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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 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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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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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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Chromatic Induction
We propose a computational model that reprodce chromatic induction processes unifying chromatic assimilation and chromatic contrast into a single perceptual process.
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Physics-based color image segmentation
Based on an analysis of the bi-directional reflection model we propose a method which is particularly suited for segmentation in the presence of shadow and highlight edges.
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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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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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