Infrared image segmentation method based on improved FCM (fuzzy C-means) and mean drift

Abstract

The invention discloses an infrared image segmentation method based on improved FCM (fuzzy C-means) and mean drift, and mainly solves the problems over-segmentation of a segmentation result due to the fact that local convergence is easily caused in a segmentation process in a conventional mean drift segmentation method. The infrared image segmentation method comprises steps as follows: (1), an original infrared image is input; (2), the original infrared image is subjected to primary segmentation with a mean drift algorithm; (3), a clustering center and a clustering number which are required by secondary image segmentation are determined with a minimum / maximum method; (4), the result image after primary segmentation is converted into an initial value of secondary segmentation; (5), pixel points of the initial value of secondary segmentation are subjected to fuzzy classification; and (6), boundaries of different areas are sketched, and an image segmentation result is output. According to the method, the segmentation accuracy is improved while the segmentation efficiency is guaranteed, and the method has the advantages of smooth edges and clear contour of the segmentation result and can be effectively applied to military or civil aspects of infrared precision guide, target recognition and tracking and the like.

Description

technical field [0001] The invention belongs to the field of image information processing, relates to an infrared image segmentation method, and can be applied to military or civilian systems such as infrared target detection and tracking. Background technique [0002] Image segmentation refers to decomposing an image into meaningful parts or objects. It is the lowest processing technology in the field of computer vision and image information processing. Image segmentation plays an important role in image analysis and pattern recognition, and is the basis for feature extraction, recognition, tracking and classification of image objects. Among them, infrared image segmentation plays a special role in the automatic recognition of target objects. In recent years, Chinese and foreign scholars have made a lot of contributions to the technical exploration of infrared image segmentation, and proposed many methods, such as edge detection method, threshold segmentation method, regio...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that although the spatial structure of the image to be segmented is analyzed, in the mean shift image segmentation method adopted later, iterative calculation is still required, so that the segmentation falls into local convergence and over-segmentation occurs, which cannot be guaranteed Segmentation results are optimal

The disadvantage of this method is that, although the entire image to be segmented is divided into blocks to enhance the processing efficiency, it still does not take into account the shortcoming that the algorithm is prone to local convergence when performing mean shift filtering on each pixel. In the case of ensuring the efficiency of the algorithm operation, the accuracy of the segmentation result cannot be guaranteed

Images