Spatial positioning fuzzy C-means algorithm in segmentation of range-gated image

A fuzzy C-means algorithm based on spatial positioning was proposed to do the segmentation for range-gated image, which had the feature of low contrast, uneven illumination, and blurring. Object extraction is essential in image processing, providing the basic and necessary information for other methods. Traditional FCM algorithm needs the number of classes to cluster the data, which limits its adaptability. It also lacks in sensitivity of spatial information, resulting in misclassification as well as incomplete extraction of objects. For the above defects, the traditional algorithm was improved by pre-positioning. Firstly, median filter, Otsu method, and mathematical morphology method were applied to do the initial segmentation, obtaining the centroid and grayscale information of all targets, which took very short time. Then both of the centroid and grayscale information were used in clustering process, accomplishing the classification with fewer iterations and less time consuming than traditional FCM. Experiments indicate that the the Spatial Positioning FCM (SPFCM) is effective in segmentation of range-gated image, the targets can be extracted more completely and faster than traditional FCM algorithm. This new method can be applied to navigation, tracking and surveillance with range-gated imaging system.