Abstract: Method for classifying a two- or higher dimensional image, where each pixel is associated with M property measures, includes identifying firstly a certain predetermined, variable geometric structure, the extension of which in at least two of the N dimensions in the dataset is determined in relation to a single element in the dataset and by at least one variable parameter, and secondly at least one geometric measure associated with the variable geometric structure, which geometric measure is arranged to measure a geometric property of a specific geometric structure in relation to other specific such geometric structures, and in that a main classification is conducted of the dataset, which main classification is based upon a comparative measure between the respective sets of associated geometric measures of two elements, calculated from a respective maximal geometric structure for each element.

Abstract: The method is characterized in that the method comprises the steps that a computer or several interconnected computers are caused to a) store, in the form of a pixel set in which set each pixel is associated with image information in at least one channel for light intensity, a first image to be classified onto a digital storage medium; b) carry out a first classification of the image, which classification is caused to be based upon the image information of each respective pixel and which classification is caused to associate each pixel with a certain class in a first set of classes, and to store these associations in a first database; c) calculate, for each pixel and for several classes in the first set of classes, the smallest distance in the image between the pixel in question and the closest pixel which is associated with the class in question in the database, and to store an association between each pixel and the calculated smallest distance for the pixel in a second database for each class for which a di

Abstract: Method for classifying a two- or higher dimensional image, where each pixel is associated with M property measures, includes identifying firstly a certain predetermined, variable geometric structure, the extension of which in at least two of the N dimensions in the dataset is determined in relation to a single element in the dataset and by at least one variable parameter, and secondly at least one geometric measure associated with the variable geometric structure, which geometric measure is arranged to measure a geometric property of a specific geometric structure in relation to other specific such geometric structures, and in that a main classification is conducted of the dataset, which main classification is based upon a comparative measure between the respective sets of associated geometric measures of two elements, calculated from a respective maximal geometric structure for each element.

Abstract: The method is characterised in that the method comprises the steps that a computer or several interconnected computers are caused to a) store, in the form of a pixel set in which set each pixel is associated with image information in at least one channel for light intensity, a first image to be classified onto a digital storage medium; b) carry out a first classification of the image, which classification is caused to be based upon the image information of each respective pixel and which classification is caused to associate each pixel with a certain class in a first set of classes, and to store these associations in a first database; c) calculate, for each pixel and for several classes in the first set of classes, the smallest distance in the image between the pixel in question and the closest pixel which is associated with the class in question in the database, and to store an association between each pixel and the calculated smallest distance for the pixel in a second database for each class for which a di