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Abstract. Motivated by the method for color image segmentation based on intensity and hue clustering proposed in [26] we give some theoretical explanations for this method that directly follows from the natural connection between the maximum likelihood approach and Least Squares or Least Absolute Deviations clustering optimality criteria. The method is tested and illustrated on a few typical situations, such as the presence of outliers among the data.

This paper describes an R package LeArEst that can be used for estimating object dimensions from a noisy image. The package is based on a simple parametric model for data that are drawn from uniform distribution contaminated by an additive error. Our package is able to estimate the length of the object of interest on a given straight line that intersects it, as well as to estimate the object area when it is elliptically shaped. The input data may be a numerical vector or an image in JPEG format. In this paper, background statistical models and methods for the package are summarized, and the algorithms and key functions implemented are described. Also, examples that demonstrate its usage are provided.

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