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improved robust two-dimensional OTSU threshold image segmentation method

An image segmentation and thresholding technology, which is applied in image analysis, image enhancement, image data processing and other directions, can solve the problem of algorithm noise resistance and deal with uneven background brightness, and the two-dimensional OTSU algorithm has poor noise resistance and threshold. The problem of poor segmentation effect, etc., can achieve the effect of good segmentation effect, enhanced robustness, and improved efficiency.

Active Publication Date: 2019-04-19
MINZU UNIVERSITY OF CHINA
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AI Technical Summary

Problems solved by technology

The traditional OTSU algorithm only uses the gray histogram of pixels as the basis, ignoring the effect of the spatial relationship of pixels on the foreground and background discrimination.
The two-dimensional OTSU threshold segmentation algorithm has a better segmentation effect than the one-dimensional OTSU algorithm, but the introduction of a two-dimensional histogram greatly increases the complexity of algorithm calculation and search
At present, most of the improvement of the two-dimensional OTSU algorithm only focuses on how to reduce the complexity of the algorithm, and less consideration is given to improving the noise resistance of the algorithm and dealing with uneven background brightness.
Therefore, it is necessary to solve the problem of poor noise resistance of the two-dimensional OTSU algorithm and poor threshold segmentation effect on images with uneven background brightness.

Method used

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Embodiment approach

[0035] The present embodiment provides an improved robust two-dimensional OTSU threshold image segmentation method, comprising the following steps:

[0036] Step 1, perform median filtering on the original grayscale image, and use the image (f(x, y)) processed by median filtering to replace the original two-dimensional grayscale image;

[0037] Step 2, divide the median filter image and neighborhood mean image (g(x,y)) of the original image into M regions of the same size in the same way, and number them as 1, 2,..., M; M The value is determined according to the actual situation;

[0038] Step 3. Construct a two-dimensional histogram for each divided sub-region. For a given segmentation threshold (s, t), use the two-dimensional OTSU threshold segmentation method to calculate the inter-class distance measurement function of each sub-region l

[0039] The process of calculating the inter-class distance of each sub-region l is as follows:

[0040] Let the median filter image ...

Embodiment

[0059] The experimental environment used in this embodiment is: Win7 64-bit Ultimate Edition, Core(TM) i5-2415MCPU@2.30GHz, RAM 8.00GB, simulation software is MATLAB R2012b. Experiments use images for classic Rice, Coins, Lena and Cameraman. Except for the coins image resolution which is 246*300, the resolution of the other three images is 256*256.

[0060] A comparative test is carried out on four classic experimental images, and the images are segmented according to the specific implementation method. The comparison results are as follows figure 1 As shown, the first column is the original image, the second column is the segmentation result of the original two-dimensional OTSU algorithm; the third column is the segmentation result of the method of the present invention. Compared with the original two-dimensional OTSU algorithm, the method of the present invention has a better segmentation effect. In the segmentation result of the Rice image, in the lower part of the image...

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Abstract

The invention discloses an improved robust two-dimensional OTSU threshold image segmentation method. By using median filtering on an original image in an original two-dimensional OTSU algorithm, the robustness of the algorithm to noises such as salt and pepper is enhanced; Partitioning the to-be-processed image, and redefining inter-class variance measurement of a two-dimensional OTSU algorithm based on a plurality of regions to realize threshold segmentation of the image with the non-uniform background gray scale; The efficiency of the algorithm is improved by replacing a threshold exhaustionstrategy in a two-dimensional OTSU algorithm with univariate iteration. The method has a good segmentation effect on a noise-added image and an image with non-uniform background brightness, and the threshold search algorithm can reduce the time cost of the search process.

Description

technical field [0001] The invention relates to the technical field of image threshold segmentation, in particular to an improved robust two-dimensional OTSU threshold image segmentation method. Background technique [0002] Image segmentation is a widely used image processing technology, and threshold segmentation method is a typical image segmentation algorithm. The key to the threshold segmentation method is the selection of the segmentation threshold. Among many threshold selection methods, the OTSU algorithm is still widely used because of its simplicity and effectiveness. The OTSU algorithm divides the image into two categories, foreground and background, and selects the gray value corresponding to the maximum variance between the two categories as the segmentation threshold. The traditional OTSU algorithm only uses the gray histogram of pixels as the basis, ignoring the effect of the spatial relationship of pixels on the discrimination of foreground and background. ...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/136G06T7/194G06T5/00
CPCG06T7/11G06T7/136G06T7/194G06T2207/20032G06T5/92G06T5/70
Inventor 宋伟杨培郑睿赵小兵
Owner MINZU UNIVERSITY OF CHINA
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