Many applications consecrate the use of asymmetric distributions, and practical situations often require robust parametric inference. This paper presents the derivation of M-estimators with asymmetric influence functions, motivated by the G 0 A distribution. This law, regarded as the universal model for speckled imagery, can be highly skewed and maximum likelihood estimation can be severely hampered by small percentages of outliers. These outliers appear mainly because the hypothesis of independence and equal distribution of observations are seldom satisfied in practice; for instance, in the process of filtering, some pixels within a window frequently come from regions with different underlying distributions. Traditional robust estimation methods, on the basis of symmetric robustifying functions, assume that the distribution is symmetric, but when the data distribution is asymmetric, these methods yield biased estimators. Empirical influence functions for maximum likelihood estimators are computed, and based on this information we propose the asymmetric M-estimator (AM-estimator), an M-estimator with asymmetric redescending functions. The performance of AM estimators is assessed, and it is shown that they either compete with or outperform both maximum likelihood and Huber-type M-estimators.
Abstract. In this paper we explore the use of the cluster analysis in segmentation problems, that is, identifying image points with an indication of the region or class they belong to. The proposed algorithm uses the well known agglomerative hierarchical cluster analysis algorithm in order to form clusters of pixels, but modified so as to cope with the high dimensionality of the problem. The results of different stages of the algorithm are saved, thus retaining a collection of segmented images ordered by degree of segmentation. This allows the user to view the whole collection and choose the one that suits him best for his particular application.
Abstract. We present a method for image segmentation, that is, to identify image points with an indication of the region or class they belong to. The proposed algorithm basically consists of two stages. First it starts by restoring the image from possible contamination. In the second stage it analyzes each pixel using a 3x3 sliding window. For the first pixel, it creates an objets consisting of that same pixel, and registers this object in an array. In the subsequent steps, a cluster analysis is applied to the surrounding eight pixels, an determines whether the central pixel belongs to one of the existing objects, or a new object has to be created, and registered in the array of objects.
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