Image division method based on watershed-quantum evolution clustering algorithm

Abstract

The invention discloses an image division method based on a watershed-quantum evolution clustering algorithm. The method has the following processes: (1) blocking and processing an input image to be divided, and seeking characteristics of a regional block as a clustering dataset; (2) setting population scale, number of distinct categories k and halt conditions, and randomly generating an initial quantum chromosome Q(t) as an initial clustering center; (3) observing the Q(t) to be a binary system chromosome p(t), calculating a fitness function value fk of each chromosome and reserving individuals in the current group; (4) carrying out mutation operation on the Q(t) to obtain QM(t); (5) quantum crossover QM(t) to obtain the QC(t); (6) observing the QC(t) to be a binary system chromosome pc(t) to obtain offspring chromosomes; (8) judging the halt condition of the offspring chromosomes, dividing an image kind which the chromosome with the highest affinity degree in the offspring chromosomes is corresponding to as output results if the halt condition is met, otherwise returning the process (3). The method has the advantages of good regional consistency, accurate edge preservation and can be used for target recognition in the image processing field.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image segmentation method, which can be used for target recognition in image processing. Background technique [0002] Image processing is an interdisciplinary field. With the continuous development of computer science and technology, image processing and analysis has gradually formed its own scientific system. Image segmentation is a very important step in image processing, which divides the image into sub-regions or objects with strong correlation. Image segmentation is the process of dividing an image into several regions according to a certain criterion, requiring the pixels in the same region to have a certain consistency, and there is no such consistency between pixels in different regions. Image segmentation method has always been one of the basic and key technologies in image processing and analysis. The result of image segmentation contains a mor...

AI Technical Summary

Problems solved by technology

However, the existing optimization methods often take a long time to deal with optimization problems, and are easy to fall into local optimum during the search process. At the same time, for complex image segmentation problems, there are often shortcomings of inaccurate edge positioning, which will inevitably affect Region Consistency and Edge Preservation Performance of Image Segmentation

Images