Infrared Image Segmentation Method Based on Multi-Threshold and Adaptive Fuzzy Clustering

Abstract

The invention discloses an infrared image segmentation method based on multiple threshold values and self-adaptation fuzzy clustering. The main problem that according to an existing multi-threshold-value segmentation method, in the segmentation process, because false peak interference exists, the segmentation result is not ideal is solved. The method includes the steps that (1), an original infrared image is input; (2), a multi-threshold-value algorithm to which a control factor is introduced is used for coarsely segmenting the original infrared image; (3), morphology smoothing is performed on the coarsely segmented image; (4), a clustering center needed for fine segmenting the image is randomly selected, and the clustering number is determined according to a self-adaptation function; (5), fuzzy clustering is performed on pixel points of the smoothed image, so that a final segmentation result image is obtained. While the segmentation efficiency is guaranteed, the segmentation accuracy can be improved, and the infrared image segmentation method has the advantages of achieving a clear outline of the segmentation result and a complete target, and can be effectively applied to precise infrared guiding and target recognizing and tracking.

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 an infrared target detection and tracking system. 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, region growing method and so on....

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that although the criterion of independent peaks is introduced, it can filter out certain false peak interference, but the selection of its characteristic quantities cannot be adaptively obtained, which is not conducive to real-time processing.

The disadvantage of this method is that although the optimal threshold of the image is found with the idea of ​​the largest variance between classes, the phenomenon of wrong segmentation or over-segmentation is improved, but the algorithm is based on the idea of ​​local optimality and does not take into account the image global information, and the number of thresholds has to be set in advance. In the case of ensuring the efficiency of the algorithm, the real-time performance of the algorithm and the accuracy of the segmentation results cannot be guaranteed.

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