Comparative experiment with colour texture classifiers using the CCR feature space
Pattern Recognition Letters
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
An appearance-based approach to assistive identity inference using LBP and colour histograms
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
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Describes a visual monitoring system that performs scene segmentation based on color and texture information. Color information is combined with texture, and corresponding segmentation algorithms are developed to detect and measure changes (loss/gain) in a given scene or environment over a period of time. The xyY color space is used to represent the color information. The two chromaticity coordinates (x, y) are combined into one, thus providing the chrominance (spectral) part of the image, while Y describes the luminance (intensity) information. The proposed color/texture segmentation system processes luminance and chrominance separately. Luminance is processed in three stages: filtering, smoothing and boundary detection. Chrominance is processed in two stages: histogram multi-thresholding and region growing. Two or more images may be combined at the end in order to detect scene changes, using logical pixel operators. As a case study, the methodology is used to determine wetland loss/gain. For comparison purposes, results in both the xyY and HIS (hue, intensity, saturation) color spaces are presented