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

TL;DR

This paper introduces a multi-feature driven active contour segmentation model for infrared images with intensity inhomogeneity, combining global and local features to improve segmentation accuracy in challenging IR images.

Contribution

The proposed model integrates global average gray information with local entropy, standard deviation, and gradient features, using an adaptive weight to enhance IR image segmentation.

Findings

Outperforms state-of-the-art models in precision rate

Achieves better overlapping rate in IR image segmentation

Converges after finite iterations

Abstract

Infrared (IR) image segmentation is essential in many urban defence applications, such as pedestrian surveillance, vehicle counting, security monitoring, etc. Active contour model (ACM) is one of the most widely used image segmentation tools at present, but the existing methods only utilize the local or global single feature information of image to minimize the energy function, which is easy to cause false segmentations in IR images. In this paper, we propose a multi-feature driven active contour segmentation model to handle IR images with intensity inhomogeneity. Firstly, an especially-designed signed pressure force (SPF) function is constructed by combining the global information calculated by global average gray information and the local multi-feature information calculated by local entropy, local standard deviation and gradient information. Then, we draw upon adaptive weight coefficient calculated by local range to adjust the afore-mentioned global term and local term. Next, the SPF function is substituted into the level set formulation (LSF) for further evolution. Finally, the LSF converges after a finite number of iterations, and the IR image segmentation result is obtained from the corresponding convergence result. Experimental results demonstrate that the presented method outperforms the state-of-the-art models in terms of precision rate and overlapping rate in IR test images.