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)
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)
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)
Nowadays we are finding that mammography technique is best available technique for breast cancer detection. Breast abnormalities are defined over wide range of features and it may happen that radiologist might be easily missed or misinterpreted it. The ability to improve diagnostic information from medical images can be enhanced by designing image(More)
In this paper we present a segmentation algorithm for colour images that uses the watershed algorithm to segment either the 2D or the 3D colour histogram of an image. For compliance with the way humans perceive colour, this segmentation has to take place in a perceptually uniform colour space like the Luv space. To avoid oversegmentation, the watershed(More)
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