Bimodal histogram transformation based on maximum likelihood parameter estimates in univariate Gaussian mixtures
This paper presents a bimodal histogram transformation procedure where conjugate gradient optimization is used for estimating maximum likelihood parameters of univariate Gaussian mixtures. The paper only deals with bimodal distributions but extension to multimodal distributions is fairly straightforward. The transformation is suggested as a preprocessing step that provides a standardized input to e.g. a classifier. This approach is used for pixelwise classification in RGB-images of meat.
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