基于区域定位与轮廓分割的红外目标检测

红外图像受随机噪声干扰严重.传统的基于高斯混合模型的检测算法检测得到的红外目标受虚假轮廓影响,不易准确辨识.为了准确识别红外目标,采用了一种基于脉冲耦合神经网络和高斯混合模型的红外目标检测算法.首先利用高斯混合模型定位红外目标区域的位置,然后利用基于空间信息的分水岭算法得到闭合区域,再利用基于脉冲耦合神经网络的分割算法剪切其虚影,最终检测到完整的运动目标.结果表明,该方法能够消除在传统方法中产生的虚影现象,得到精确的红外运动目标.通过比较,实验结果优于传统方法.

Infrared target detection based on regional location and contour segmentation

Infrared images are usually interfered by random noise seriously. Infrared targets detected by the traditional detection algorithm based on Gaussian mixture model are difficult to be identified because of false contour. In order to identify the infrared target accurately, an infrared target detection algorithm based on pulse coupled neural network(PCNN) and Gaussian mixture model was proposed. Firstly, Gaussian mixture model was used to locate the approximate location of moving targets. And then, a closed region was obtained by using watershed algorithm based on spatial information. Segmentation algorithm based on PCNN was used to shear the pseudo-target and the complete moving target was detected. The experimental results show that this method can eliminate the pseudo target of the traditional methods and detect the infrared moving targets accurately. The new algorithm is superior to the other conventional algorithms.