Model Based Segmentation Of Flir Images

Abstract

In the automatic recognition of tactical targets in FLIR images, it is desired to obtain an accurate and precise representation of the boundary of the targets. It is very important since the features used in the classification of the target are normally based on the shape and gray scale of the segmented target and therefore the performance of a statistical or a structural classifier critically depends on the results of segmentation. Generally, only the gray scale of the image is used to extract the target from the background. The segmentation thus obtained normally depends upon several parameters of the technique used. It is possible to obtain better segmentation by using other sources of information present in the image such as contextual cues, temporal cues, gradient, a priori information etc. In this paper we consider specifically the use of gray scale together with the edge information present in the image to obtain more precise segmentation of the target than obtained by using gray scale or edge information alone. A model of FLIR images based on gray scale and edge information is incorporated in a gradient relaxation technique which explicitly maximizes a criterion function based on the inconsistency and ambiguity of classification of pixels with respect to its neighbors. Four variations of the basic relaxation technique are considered which provide automatic selection of threshold to segment FLIR images. A comparison of these methods is discussed.