Fitness random search behavior-based multi-threshold image segmentation method

Abstract

The invention provides a fitness random search behavior-based multi-threshold image segmentation method, which comprises the following steps of: establishing a multi-threshold segmentation fitness function and calculating the optimal segmentation threshold; according to the optimal segmentation threshold, establishing and initializing the first generation of particle swarms; according to the multi-threshold segmentation fitness function, calculating a fitness value of each particle, and calculating the individual optimal position of each particle and the global optimal position of all particles; updating a speed and a position vector of each particle, the individual optimal position of each particle and the global optimal position of all the particles by utilizing a particle swarm iterative formula; and repeatedly executing the steps until the condition that the number of iterations of the particle swarm iterative formula u=Umax is met. The method has the advantages of good segmentation stability, high speed and high segmentation accuracy, greatly improves the segmentation speed and accuracy, and makes the subsequent work of image processing possible.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a multi-threshold image segmentation method based on moderate random search behavior. Background technique [0002] Threshold segmentation method is a region-based image segmentation technology. Its basic principle is to divide image pixels into several categories by setting different feature thresholds. Commonly used features include: grayscale or color features directly from the original image; features transformed from original grayscale or color values. [0003] The maximum between-class variance threshold segmentation method is one of the threshold segmentation algorithms. This method was proposed by Japan's Odotsu Exhibition in 1980. It uses the gray value of the image to dynamically obtain the closed value of the image segmentation by calculating the maximum variance between the two categories of the target and the background, and then performs image segmentation based on ...

AI Technical Summary

Problems solved by technology

Specifically, both the genetic algorithm and the ant colony algorithm have the disadvantage of slow convergence.

Although the particle swarm optimization algorithm converges quickly, it is easy to fall into the local optimal solution because it is not a global optimization algorithm.

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