A method of simultaneous image segmentation and edge detection based on grey-level co-occurrence matrices is described. An analysis of the distributions within a co-occurrence matrix defines an initial pixel classification into both region and interior or boundary classes. Local consistency of pixel classification is enforced by minimising the entropy of local region and boundary information, where region information is expressed by conditional probabilities, estimated from the co-occurrence matrices, and boundary information by conditional probabilities which are determined a priori. The method robustly segments an image into homogeneous areas and generates an edge map. The technique extends easily to general edge operators; examples are given of the techniques applied to both synthetic and infrared imagery for the [1 –1] and Canny edge operators. The results are compared with other techniques.

Unification of image segmentation and edge detection

An initial pixel classification into both region class and interior or boundary designation is made based on an analysis of the distributions within the grey level cooccurrence matrices of an image. Local consistency of classification is then implemented by minimising the local entropy of region and boundary information. This is a robust way of simultaneously segmenting an image into texturally homogeneous areas with a thresholded edge map. Examples are shown using forward looking infrared (FLIR) images. The technique has been extended to cover any digital edge operator and an example is shown for the Spacek operator.

The Unification Of Region And Edge Information For Image Segmentation

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The Unification Of Region And Edge Information For Image Segmentation