针对基于图像融合的隐藏在人衣服下面的**检测技术存在的未充分保护人体的隐私权、色彩失真等问题，提出了一种基于模糊C 均值 (Fuzzy C-means Clustering，FCM) 聚类分割的隐藏**检测方法，该方法采用FCM聚类、数学形态学对红外图像进行**分割与提取，并将**融合到彩色可见光图像HSI 空间的I 分量中，通过逆变换得到RGB 彩色融合图像。该方法的融合过程只有**参与，避开了人身体的其他部位，保护了人体的隐私权，且融合图像维持了源可见光图像的真彩色、**目标清晰。经过对比实验，本文方法得到的融合图像具有更丰富的信息，在主观上具有更好的视觉特性，在客观评价指标上也取得了良好的评价。

A detection algorithm of weapon concealed underneath person’s clothes based on Fuzzy C-means (FCM) clustering is presented, aiming at the problem of image fusion, for example, no protecting the personal privacy, no true color. The main idea of the algorithm is to segment and extract the shape of concealed weapon from the infrared image by using FCM clustering and mathematical morphology. And the weapon is embed into I component of visual image in HSI color space. At last, the image is converted into RGB color space. In this process, only the concealed weapon is participated in the fusion and the other parts of the human body are avoided, which protect the personal privacy. The fused image maintains true color of the visual image, and the concealed weapon is very clear. By comparison experiment, the results show that the fused image obtained from the proposed algorithm can preserve a large amount of information and have the better visual quality and objective evaluation index.