Leila Shafarenko

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A new method is proposed for processing randomly textured color images. The method is based on a bottom-up segmentation algorithm that takes into consideration both color and texture properties of the image. An LUV gradient is introduced, which provides both a color similarity measure and a basis for applying the watershed transform. The patches of(More)
In this work, we present a segmentation algorithm for color images that uses the watershed algorithm to segment either the two-dimensional (2-D) or the three-dimensional (3-D) color histogram of an image. For compliance with the way humans perceive color, this segmentation has to take place in a perceptually uniform color space like the Luv space. To avoid(More)
Color image segmentation is an important task for computer vision. The segmented RGB color space is not more reliable and accurate for computer vision applications. For this purpose, the proposed approach combines different color spaces such as RGB, HSV, YIQ and XYZ for image segmentation. The combine segmentation of various color spaces to give more(More)
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