A Novel Edge Preserving Active Contour Model Using Guided Filter and Harmonic Surface Function for Infrared Image Segmentation

Abstract

Infrared image segmentation is a useful and challenging research subject due to its inherent limitations, such as complicated noises, vague edges, and low contrast in infrared images. Active contour models have a wide range of applications for infrared image segmentation. A gradient vector flow (GVF) model and other external force field models have been proposed to improve the noise robustness and weak edge protection to a certain extent. To further address these issues, this paper presents a novel edge-preserving active contour model using guided filter and harmonic surface function for infrared image segmentation. Guided filter is applied to obtain the edge map, reducing the noise interference effectively and collecting more detailed information of the infrared images such as the edges. Then, a harmonic surface function is added into the smoothness term to make the proposed model possess both the abilities of fast convergence and weak edge protection. Besides, we incorporate the information of external force field into the proposed model to preserve the weak boundaries. Compared with adaptive diffusion flow, the proposed method has increased 4% and 2% in precision and F1 measure, respectively.