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Article Dans Une Revue Pattern Recognition Letters Année : 2014

Color Adaptive Neighborhood Mathematical Morphology and its application to pixel-level classification

Résumé

In this paper spatially adaptive Mathematical Morphology (MM) is studied for color images. More precisely, the General Adaptive Neighborhood Image Processing (GANIP) approach is generalized to color images. The basic principle is to define a set of locally Color Adaptive Neighborhoods (CAN), one for each point of the image, and to use them as adaptive structuring elements (ASE) for morphological operations. These operators have been applied to images in different color spaces and compared them with other kinds of ASEs extended to color images. Results show that the proposed method is more respectful with the borders of the objects, as well as with the color transitions within the image. Finally, the proposed adaptive morphological operators are applied to the classification of color texture images.
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Dates et versions

hal-01079559 , version 1 (03-11-2014)

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Víctor González-Castro, Johan Debayle, Jean-Charles Pinolí. Color Adaptive Neighborhood Mathematical Morphology and its application to pixel-level classification. Pattern Recognition Letters, 2014, 47, pp.50 - 62. ⟨10.1016/j.patrec.2014.01.007⟩. ⟨hal-01079559⟩
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