Method for setting a tridimensional shape detection classifier and method for tridimensional shape detection using said shape detection classifier

Method for setting a tridimensional shape detection classifier for detecting tridimensional shapes from depth images in which each pixel represents a depth distance from a source to a scene, the classifier comprising a forest of at least a binary tree (T) for obtaining the class probability (p) of a given shape comprising nodes associated with a distance function (f) that taking at least a pixel position in a patch calculates a pixel distance. The method comprises per each leaf (L) node of the binary tree the configuration steps of creating candidate groups of parameters; obtaining positive patches (Ip) containing part of the shape to be detected; obtaining negative patches (In) not containing part of the shape to be detected; calculating in the leaf node the distance function of the obtained positive and negative patches comparing the result of the distance function with its pixel distance threshold and computing its statistics; and selecting for the leaf node the candidate group of parameters that best separate the positive and negative patches into two groups for calculating the class probability of the shape in that leaf node using the distance function. It is also disclosed a method for shape detection from a depth image using the shape detection classifier; a data processing apparatus comprising means for carrying out the methods; and a computer program adapted to perform the methods.