Hybrid active contour model based on edge gradients and regional multi-features for infrared image segmentation

Infrared image segmentation is always a tough task due to blurred boundaries, low contrasts and noises. Active contour model (ACM) is an efficient tool which has been proved to be useful when applied to image segmentation, but still has lots of drawbacks. In this paper, a hybrid ACM for infrared image segmentation is presented via combing both edge gradients and regional multi-features which are seldom considered in previous researches, meaning that its level set formulation (LSF) is made up of an edge-based term, a region-related term and a regularization term. The first term steams from intensity gradients and promotes the contour to approach the object boundary. The second term is constructed by means of integrating a novel multi-feature signed pressure function (MSPF) with a traditional signed pressure function (SPF) through an adaptive weight coefficient. In this case, both local and global regional information are considered and challenges caused by inhomogeneity are thus overcome. Lastly, the third term provides a stable evolution for the contour. In addition, a Gaussian filter is introduced to avoid computationally expensive re-initializations of the LSF efficiently. Both qualitative and quantitative experiments demonstrate the effectiveness and robustness of the proposed method with the initial contour being set randomly.