AUTOMATIC SEGMENTATION AND CLASSIFICATION USING A CO-OCCURRENCE BASED APPROACH

This paper presents an algorithm based on co-occurrence matrices for the automatic analysis of images. This includes the simultaneous segmentation of images into key regions and the detection of the main boundaries. The texture is described using discrete Hermite functions to decompose co-occurrence matrices of the segmented regions and are subsequently labelled using neural network based classifiers. Local consistency of interpretation of the analysis is imposed using relaxation labelling techniques. The algorithm presented is generally applicable to a wide range of imaging domains and applications with only minor variations on the basic theme, for example, the incorporation of a priori knowledge in an intelligent manner to ensure that parameters are adapted to the particular applications. Experimental results are presented for 2 scenarios: scene analysis of a sequence of infrared images taken from a low flying aircraft and the analysis of snow profiles for the assessment of snowpack stability.