A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

作者：Xu Guili^*; Zhao Yan; Guo Ruipeng; Wang Biao; Tian Yupeng; Li Kaiyu

来源：Chinese Journal of Aeronautics, 2014, 27(1): 102-109.

DOI：10.1016/j.cja.2013.12.001

• Data reduction
• Edge detection
• Fuzzy clustering
• Kernel clustering
• Possibility fuzzy C-means (PFCM)

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means (PFCM) kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means (FCM) clustering is sensitive to noises and possibility C-means (PCM) clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.

• 出版日期2014-2
• 单位南京航空航天大学