CN110322450A - The method for improving binarization threshold selection accuracy - Google Patents

The method for improving binarization threshold selection accuracy Download PDF

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Publication number
CN110322450A
CN110322450A CN201810275496.1A CN201810275496A CN110322450A CN 110322450 A CN110322450 A CN 110322450A CN 201810275496 A CN201810275496 A CN 201810275496A CN 110322450 A CN110322450 A CN 110322450A
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brightness
image
carries out
processing
accuracy
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CN201810275496.1A
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张�杰
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张�杰
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Priority to CN201810275496.1A priority Critical patent/CN110322450A/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/11Region-based segmentation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/136Segmentation; Edge detection involving thresholding

Abstract

The invention discloses the methods for improving binarization threshold selection accuracy, are related to image grayscale binary conversion treatment, including Threshold Segmentation Algorithm, further comprising the steps of: S1 obtains 3 or more images, and carries out gray processing processing;S2 carries out brightness detection to gray level image, obtains 3 or more brightness of image mean values, take the brightness population mean of 3 images above;S3 takes the image closest to brightness population mean to carry out luminance segmentation processing, coefficient of the part as brightness regulation higher than 128;S4 carries out binary conversion treatment using Threshold Segmentation Algorithm using coefficient as threshold value.The brightness detection can also be using contrast, saturation degree as target.The present invention can play inhibiting effect to excessive lightness or darkness image, improve the accuracy of binarization threshold selection, improve the accuracy of later image processing.

Description

The method for improving binarization threshold selection accuracy
Technical field
The present invention relates to a kind of image grayscale binary conversion treatments, and in particular to improves the side of binarization threshold selection accuracy Method.
Background technique
Threshold value selection is always the problem of field of image processing, and threshold value determines the journey of information loss after binary image Degree, and then influence the effect of image procossing.
Summary of the invention
Algorithm is insufficient when background too dark or too bright the technical problem to be solved by the present invention is to conventional images binary conversion treatment, It is designed to provide the method for improving binarization threshold selection accuracy, is solved the above problems.
The method for improving binarization threshold selection accuracy, including Threshold Segmentation Algorithm, further comprising the steps of:
S1, obtains 3 or more images, and carries out gray processing processing;
S2 carries out brightness detection to gray level image, obtains 3 or more brightness of image mean values, take the bright of 3 images above Spend population mean;
S3 takes the image closest to brightness population mean to carry out luminance segmentation processing, and the part higher than 128 is as brightness tune The coefficient of section;
S4 carries out binary conversion treatment using Threshold Segmentation Algorithm using coefficient as threshold value.
S1,3 images above can be avoided random error;S2, population mean is for ensuring that image is not in mutation feelings Condition;S3,128 be the center of pixel region 0-255, and population mean can effectively represent deviation post with 128 difference, is obtained most Good threshold condition.
Further, the brightness detection can also be using contrast, saturation degree as target.Contrast and saturation degree and Brightness detection has corresponding relation, therefore can also be used as the reference frame of binarization threshold choice accuracy.
Compared with prior art, the present invention having the following advantages and benefits:
The method that the present invention improves binarization threshold selection accuracy can play inhibition to excessive lightness or darkness image and make With the accuracy of raising binarization threshold selection improves the accuracy of later image processing.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below with reference to embodiment, the present invention is made Further to be described in detail, exemplary embodiment of the invention and its explanation for explaining only the invention, are not intended as to this The restriction of invention.
Embodiment
The method that the present invention improves binarization threshold selection accuracy, including Threshold Segmentation Algorithm, further comprising the steps of:
S1, obtains 3 or more images, and carries out gray processing processing;
S2 carries out brightness detection to gray level image, obtains 3 or more brightness of image mean values, take the bright of 3 images above Spend population mean;
S3 takes the image closest to brightness population mean to carry out luminance segmentation processing, and the part higher than 128 is as brightness tune The coefficient of section;
S4 carries out binary conversion treatment using Threshold Segmentation Algorithm using coefficient as threshold value.S1,3 images above can be kept away Exempt from random error;S2, population mean is for ensuring that image is not in catastrophe;S3,128 is in pixel region 0-255 The heart, population mean can effectively represent deviation post with 128 difference, obtain optimal threshold condition.
The brightness detection can also be using contrast, saturation degree as target.Contrast and saturation degree and brightness detect There is corresponding relation, therefore can also be used as the reference frame of binarization threshold choice accuracy.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include Within protection scope of the present invention.

Claims (2)

1. the method for improving binarization threshold selection accuracy, including Threshold Segmentation Algorithm, which is characterized in that further include following step It is rapid:
S1, obtains 3 or more images, and carries out gray processing processing;
S2 carries out brightness detection to gray level image, obtains 3 or more brightness of image mean values, take the brightness of 3 images above total Body mean value;
S3 takes the image closest to brightness population mean to carry out luminance segmentation processing, and the part higher than 128 is as brightness regulation Coefficient;
S4 carries out binary conversion treatment using Threshold Segmentation Algorithm using coefficient as threshold value.
2. the method according to claim 1 for improving binarization threshold selection accuracy, which is characterized in that the brightness inspection Surveying can also be using contrast, saturation degree as target.
CN201810275496.1A 2018-03-30 2018-03-30 The method for improving binarization threshold selection accuracy Withdrawn CN110322450A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810275496.1A CN110322450A (en) 2018-03-30 2018-03-30 The method for improving binarization threshold selection accuracy

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Application Number Priority Date Filing Date Title
CN201810275496.1A CN110322450A (en) 2018-03-30 2018-03-30 The method for improving binarization threshold selection accuracy

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CN110322450A true CN110322450A (en) 2019-10-11

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111739110A (en) * 2020-08-07 2020-10-02 北京美摄网络科技有限公司 Method and device for detecting image over-darkness or over-exposure

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111739110A (en) * 2020-08-07 2020-10-02 北京美摄网络科技有限公司 Method and device for detecting image over-darkness or over-exposure
CN111739110B (en) * 2020-08-07 2020-11-27 北京美摄网络科技有限公司 Method and device for detecting image over-darkness or over-exposure

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Application publication date: 20191011