IJESS

COLOR CONVERSION AND WATER SHED SEGMENTATION FOR RGB IMAGES

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Abstract

In this paper we describes the conversion preserves feature discriminability and reasonable color ordering, while respecting the original lightness of colors, by simple optimization of a nonlinear global mapping. Experimental results show that our method produces convincing results for a variety of color images. The required luminance adjustments are small and always lie within 1% of the mean luminance. Since all adapting lights are of the same luminance, zero luminance adjustments (dashed lines) are predicted for the asymmetric color matches under the hypothesis that adaptation is confined to the L–2M, the S – (L + M) and the L + 2M.The recovery of shape from texture under perspective projection. This is made possible by imposing a notion of homogeneity for the original texture, according it which the deformation gradient is equal to the velocity of the texture gradient equation this work studies a method called Normalized Cut and proposes an image segmentation strategy utilizing two ways to convert images into graphs: Pixel affinity and watershed transform.

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