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

A fruit fly optimization algorithm and image segmentation technology, applied in image analysis, image enhancement, image data processing and other directions, can solve the problem of insufficient segmentation accuracy and anti-noise performance, and achieve good noise suppression, less running time, and accurate segmentation results. Effect

Active Publication Date: 2017-10-24
STATE GRID CORP OF CHINA +3
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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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  • Two-dimensional Otsu image segmentation method in combination with fruit fly optimization algorithm

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Embodiment

[0091] In order to verify the image segmentation effect and the superiority of the calculation speed of the present invention, a cameraman image, a columbia image, an eight image and a rice image are selected as experimental images, and the noises added to the four images are all N(0,0.01). The simulation experiment is carried out on a computer with 2.40GHz CPU and 2G memory under Matlab7.1 environment.

[0092] The gray level L is 256, and both M and N are 256. The parameters are set to α=-30°, β=120°. refer to image 3 , the segmented images from top to bottom are cameraman image A, columbia image B, eight image C, and rice image D; from left to right are the original image with noise (a), and the image segmented by the method of Document 1 (b ), the image (c) segmented by the method of Document 2, the image (d) segmented by the method of Document 3 and the image (e) segmented by the present invention. Each image is run 10 times, and the average calculation time, threshol...

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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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/136G06T7/194G06N3/00
CPCG06N3/006G06T7/136G06T7/194G06T2207/10004
Inventor 樊强彭启伟罗旺冯敏夏源崔漾赵高峰张天兵余磊王万国屠正伟马涛侯麟刘雄毛大鹏邢雅菲吴翔琚小明
Owner STATE GRID CORP OF CHINA
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