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Abstract

An adaptive image segmentation system and methodology based on Mixed Raster Content (MRC) format. A L*a*b* color image is processed into an object-based MRC representation. By using L*a*b* data, an expectation-maximization algorithm is used to estimate a mixture of two 3-D Gaussians, with one Gaussian representing the background pixels and the other the foreground pixels. A resultant-quadratic decision surface is calculated and all image pixels are compared against it. Depending on which side of the decision surface any given pixel falls, that pixel goes to either the background or foreground plane. The pixel-by-pixel decisions are used to comprise a mask plane. The mask plane is converted into run lengths, which are 'cleaned', and regions are merged. Large connected components are reserved as windows and are used to mask out portions of the foreground. The result is a background plane, a mask plane, a foreground plane and any number of foreground/mask pairs, consistent with the ITU T.44 MRC specification. Using 3-D calculations in L*a*b* as opposed to just 1-D calculations in L*, and applying a quadratic surface provides a more robust solution to scanner choice and resolution. The methodology may also be combined with other processing steps such as compression, hints generation, and object classification.

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