为有效分割红外图像中边界模糊、对比度低的感兴趣目标，提出了一种基于变分的红外图像分割模型。针对测地线活动轮廓模型(Geodesic Active Contour, GAC)对噪声敏感的问题，假设图像中的目标和背景服从Gaussian分布，再根据像素属于红外目标的概率构造区域能量项，以提高模型的鲁棒性。在模型中引入有符号距离约束，以避免曲线在演化过程中重新初始化，提高模型执行的效率。实验结果表明，本文方法能够有效地分割红外图像中的感兴趣目标。

To effectively segment the targets of interest with blurred boundaries and low contrast in infrared images, an infrared image segmentation model based on region competition is proposed. For the Geodesic Active Contour (GAC) model sensitive to noise, assuming that the object and background in an image obey Gaussian distribution. Then, according to the probability of pixels belonging to the infrared object, a region energy term is constructed so as to improve the robustness of the model. Finally, a signed distance function is introduced to avoid the re-initialization of the curve in the process of evolution and improve the efficiency of the model. The experimental results show that the proposed method can segment the targets of interest in an infrared image effectively.