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Infrared (IR) image segmentation is essential in many urban defenceapplications, such as pedestrian surveillance, vehicle counting, securitymonitoring, etc. Active contour model (ACM) is one of the most widely usedimage segmentation tools at present, but the existing methods only utilize thelocal or global single feature information of image to minimize the energyfunction, which is easy to cause false segmentations in IR images. In thispaper, we propose a multi-feature driven active contour segmentation model tohandle IR images with intensity inhomogeneity. Firstly, an especially-designedsigned pressure force (SPF) function is constructed by combining the globalinformation calculated by global average gray information and the localmulti-feature information calculated by local entropy, local standard deviationand gradient information. Then, we draw upon adaptive weight coefficientcalculated by local range to adjust the afore-mentioned global term and localterm. Next, the SPF function is substituted into the level set formulation(LSF) for further evolution. Finally, the LSF converges after a finite numberof iterations, and the IR image segmentation result is obtained from thecorresponding convergence result. Experimental results demonstrate that thepresented method outperforms the state-of-the-art models in terms of precisionrate and overlapping rate in IR test images.

Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity