基于灰度和信息熵融合的金属疲劳偏振热像分割算法

Polarization Thermal Image Segmentation Algorithm of Metal Fatigue Based on Gray Level and Information Entropy Fusion

提出了一种基于图像灰度和信息熵融合的红外偏振热像分割算法。首先运用图像局部平均灰度值与方差加权信息熵,寻找多偏振方位角热像的潜在目标区域并配准;其次用改进的模糊C均值聚类(FCM)算法进行逐一分割,将分割后的热像经集合运算后的结果作为支持向量机(SVM)的标签;然后对目标区域和背景区域的数据进行训练得到SVM模型并重新划分模糊区域;最后通过形态学处理去除误分割得到最终分割热像。实验结果表明,所提算法相较于最大熵法、最大类间方差(OTSU)算法、FCM算法,能够得到更高的分割精度,有效地改善图像的错分割现象。

An infrared polarized thermal image segmentation algorithm based on image gray and information entropy fusion is proposed. First, the local average gray and variance weighted information entropy of the image are used to find out the potential region of multi polarization azimuth thermal image and register it;second, the improved fuzzy C-means(FCM) algorithm is used to segment the thermal image separately, and the result of set operation is used as the label of support vector machine(SVM);then, the data of target region and background region are trained to obtain SVM model and redivide the fuzzy region;finally, the segmented thermal image is achieved by morphological processing synthetically. Experimental results show that compared with the maximum entropy algorithm, OTSU algorithm, and FCM algorithm, the proposed algorithm can get higher segmentation accuracy and effectively improve the phenomenon of wrong segmentation.