Image segmentation and object recognition by Bayesian grouping

We propose a Bayesian grouping approach for recognition and segmentation of large-scale structures representing objects in images. It is based on detection of local image properties, extraction of simple geometrical primitives, and grouping these primitives according to probability rules and prior models. As opposed to the various template matching techniques, our method does not rely on a fixed set of input data to generate the prior with a maximum likelihood. Instead, it selects a list of subsets of the local primitives and finds the optimum set of model priors that maximizes the likelihood of the model samples representing the selected subsets. In contrast with global recognition methods that classify the whole image, our approach aims at solving the recognition task together with the segmentation task. As an illustration we give a medical data example of feature grouping in 2D images involving vessel detection from local ridges.