Standard multivariate statistical methods in medical applications are too sensitive to the assumption of multivariate normality and the presence of outliers in the data. This paper is devoted to robust statistical methods. In the context of medical image analysis they allow to solve the tasks of face detection and face recognition in a database of images. The results of the robust approaches in image analysis turn out to outperform those obtained with standard methods. Robust methods also have desirable properties appealing for practical applications, including dimension reduction and clear interpretability.
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The great number of object modelling computer techniques that alresdy exist and the new ones that are continuously appearing are the result of a multidisciplinary exchange of ideas. Within this area, objects with changing shapes are inherently more difficult to model than rigid objects. Our main goal has been to provide a classification of the algorithms involved in the modelling of objects that can be as diverse as water, clothes, faces,...The classification has been made from the starting poing of inorganic and organic categories. Inorganic objects are classified according to their material state while the organic ones are classified according to the realm they belong to. Within each category, works are generally presented in chronological order, providing an extensive bibliography.
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