Description: Maximum between-class variance method is a popular image segmentation threshold segmentation method has a significant effect for the single-threshold segmentation, but
The multi-threshold segmentation, the computational complexity of large, time-consuming. In this paper, particle swarm optimization combined with maximum between-class variance method, a new image
Cut method, the method using particle swarm optimization algorithm optimizing the efficiency of the maximum between-class variance by the gray-scale image as the fitness value, search for the optimal split
Threshold, multi-threshold image segmentation. The experimental results show that the new method greatly shorten the time to find the optimal threshold, reducing the computational complexity, to mention
The high speed of image segmentation, image segmentation algorithm based on particle swarm optimization algorithm is feasible and effective.
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