Adaptive fuzzy C-means image segmentation method based on potential function

Abstract

The invention provides an adaptive fuzzy C-means image segmentation method based on a potential function and mainly aims at solving the problems that classifying number needs to be previously set and the segmentation efficiency is low in the existing fuzzy C-means segmentation method. The method is realized by the following steps: (1) inputting a to-be-segmented image; (2) obtaining the histogram potential function and the maximum potential remnant height of the to-be-segmented image; (3) obtaining the histogram c-factorial remnant potential function of the to-be-segmented image; (4) combining pseudo potentials; (5) obtaining the clustering center and the classifying number of the to-be-segmented image; (6) carrying out fuzzy classification on the pixel dots of the to-be-segmented image; and (7) outputting an image segmentation result. The adaptive fuzzy C-means image segmentation method has the advantages of capability of obtaining optimal image classifying number in an adaptive way, high segmentation efficiency, strong region consistency in the segmentation result, smooth margin and the like. The adaptive fuzzy C-means image segmentation method can effectively segment natural gray images and infrared images and can be used for target recognition and tracking.

Description

technical field [0001] The invention belongs to the technical field of computers, and further relates to a potential function-based adaptive fuzzy C-means image segmentation method in the technical field of image processing. The invention obtains the cluster center and the optimal classification number of the image to be segmented through the potential function method, classifies and marks the pixel points of the image, realizes the segmentation of the natural grayscale image and the infrared image, and can be applied to target recognition and tracking. Background technique [0002] Image segmentation can be considered as the process of clustering the pixels in the image. According to the gray feature of each pixel in the image, it is judged which category the pixel belongs to, and the pixels belonging to the same category in the image are marked. Complete the segmentation of the image. Because the fuzzy segmentation method conforms to the characteristics of human cognition...

AI Technical Summary

Problems solved by technology

The disadvantage of the standard fuzzy C-means segmentation method is that, first of all, before the image is segmented, the method needs to pre-set the number of categories of the image, and the optimal number of categories of the image cannot be determined in advance before the image is segmented. The number of image classifications usually cannot make the image segmentation effect optimal; secondly, the standard fuzzy C-means segmentation method updates the cluster center of the image through an iterative operation method. The segmentation speed is very slow, which is not conducive to the real-time segmentation of images

The disadvantage of this method is that although the influence of noise is suppressed in the feature selection of the image, the subsequent fuzzy C-means segmentation method still needs to manually set the classification number of the image, which cannot guarantee the best segmentation effect of the image. Excellent, at the same time, the fuzzy C-means segmentation method updates the clustering center of the image through iterative operation, which is complex and time-consuming, which is not conducive to the realization of real-time image segmentation

The disadvantage of this method is that although this method uses the fusion of multiple membership matrices, it can more accurately obtain the category labels of pixels in the image, making the segmentation effect better, but the premise is that the method to be segmented The number of categories of images should be optimal. If the number of categories is set improperly, this method still cannot obtain better segmentation results, and this method needs to calculate multiple membership degree matrices during the operation process, and match the membership degrees. Quasi-fusion, high computational complexity, resulting in slow segmentation speed, which is not conducive to real-time image segmentation

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