COLOR MORPHOLOGY CENG 566 FINAL PROJECT Sezen ERDEM

Mathematical Morphology Mathematical morphology uses concepts from set theory, geometry and topology to analyze geometrical structures in an image. non linear operators are applied to images successively in order to make certain features apparent, distinguishing meaningful information from irrelevant distortions.

Operations on Binary & Gray Scale Images Binary Image has lattice structure It is suitable for set operations such as union or intersection It is easy to apply morphological operations on binary images

Operations on Binary & Grey Scale Images Grayscale morphological operations are an extension of binary morphological operations We have a partial ordering between the pixel color values It is also easy for grey scale images.

Color Morphology No lattice structure partial ordering is not as easy as it is for binary or grey scale images. R,G,B components are independent from each other

Approaches to Color Morphology Mathematical Morphology on RGB Components Mathematical Morphology on RGB Components as Vector Mathematical Morphology on L*m*p* Components

Mathematical Morphology on RGB Components Apply morphological operators on each color value of the RGB image. Combine the result values to get the value of the pixel

Problems May get a result pixel which does not exists. Violates characteristics of morphological operations.

Mathematical Morphology on RGB Components as Vector Consider the color values as components of a vector Make partial ordering accordigly Problem : Favors a color component

Mathematical Morphology on L*m*p* Components Convert color values into a format so that they can be viewed as vector. Eliminates the limitations of ordering and the problems in RGB morphology

Steps to follow Convert RGB to L*m*p* where L* represents the lightness (luminance). m* encodes the red-green sensation p* encodes the yellow-blue sensation

Steps to follow Normalize Image Transform normalized RGB to XYZ Compute

Steps to follow Compute

Steps to follow After these steps, we can define an ordering between the values of the pixels. Compare p*, then m*, then L* to decide

Conclusion There are several approaches Each has some problems Vector Comparison Algorithms performs better

Sample Images The result samples will be shown from CD.

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