Abstract
In this paper, automatic segmentation of multi-class images problem is considered. The 1D histogram of the multi-class image is approximated using Gaussian functions and the unknown parameters of the Gaussian functions are estimated using Non-linear Least Squares (NLS) optimisation; thereby the problem of segmentation of unknown image class is modelled as an optimisation problem. Further, the parameter estimation accuracy is improved by using the Pearson linear correlation coefficient as a regularisation term of the objective function. The experimental results demonstrate the NLS algorithm ability to estimate the parameters of the Gaussian functions and thereby automatically determine the multi-thresholds for segmentation.
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