Two-dimensional Otsu image segmentation method in combination with fruit fly optimization algorithm

Abstract

The invention discloses a two-dimensional Otsu image segmentation method in combination with a fruit fly optimization algorithm. The method adopts a two-dimensional histogram area divided by two straight lines passing through a threshold vector point and a gray-scale axis to form angles alpha and beta, and comprises the steps of calculating a joint probability density first, then calculating probabilities and mean vectors of a target area and a background area, calculating a between-class variance and a within-class variance of the target area and the background area, weighting the background area in a threshold solving formula according to the proportions of the target and the background in the image, and seeking an optimal two-dimensional threshold vector by adopting the fruit fly optimization algorithm. The method has the advantages of more accurate segmentation on pictures, better noise suppression and low running time consumption.

Description

technical field [0001] The invention belongs to the field of intelligent image segmentation in digital image processing technology, and relates to a method for segmenting two-dimensional Otsu images, in particular to a method for segmenting two-dimensional Otsu images combined with a fruit fly optimization algorithm. Background technique [0002] Image segmentation is to extract the target of interest in the complex background of an image, and it is one of the basic technologies of image processing and computer vision. [0003] Document 1: Acta Automatica Sinica, Volume 19, Issue 1, 1993, Liu Jianzhuang et al. proposed a two-dimensional Otsu segmentation method, which considers grayscale and domain space information, and selects the optimal two-dimensional threshold based on the direct division of the two-dimensional histogram, which has better anti-noise ability, but the exhaustive method makes the amount of calculation very large, and the algorithm assumes that the probabi...

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Problems solved by technology

[0007] The purpose of the present invention is to provide a kind of two-dimensional Otsu image segmentation method combined with the fruit fly optimization algorithm for the insufficient segmentation precision of the prior art and the deficiency of anti-noise performance. The straight line of α and β divides the two-dimensional histogram area, improves the joint probability density according to the gray level, considers the effect of inter-class variance and intra-class variance on the image segmentation effect, and calculates the threshold according to the proportion of the target and background in the image. Take the formula for weighting, and then use the fruit fly optimization algorithm to find the optimal two-dimensional threshold vector

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