In this research, we propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. Firstly, the number of colors which occur in a single natural image is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Secondly, we impose a statistical prior to the image values which are locally present within the filter window.
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We propose a CRF based method for multi-illuminant estimation. In addition we propose a data set with pixelwise ground-truth of both real scenes and laboratroy setting images.
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In this work we propose a number of discriminative color descriptors which are optimizing the discriminative power with respect to a classifciation problem. In addition, we propose a set of universal color descriptors which can be used without any prior training on any data set.
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We propose a synthetic image data set for intrinsic image evaluation.
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We evaluate the usage of color for action recognition in still images.
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We extend part-based object detection with color information. Results on VOC PASCAL are provided and code is available.
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We propose a novel approach for constructing multi-cue Portmanteau vocabularies for image classification.
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In this research we decompose the image into its intrinsic reflectance components with the aim to recolor scenes.
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