CN101923710A - Image tilt correction method and device - Google Patents

Image tilt correction method and device Download PDF

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Publication number
CN101923710A
CN101923710A CN 201010221775 CN201010221775A CN101923710A CN 101923710 A CN101923710 A CN 101923710A CN 201010221775 CN201010221775 CN 201010221775 CN 201010221775 A CN201010221775 A CN 201010221775A CN 101923710 A CN101923710 A CN 101923710A
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gray level
image
level image
described gray
carried out
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李挺
裴雷
刘微
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Qingdao Hisense Network Technology Co Ltd
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Qingdao Hisense Network Technology Co Ltd
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Priority to CN 201010221775 priority Critical patent/CN101923710A/en
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Priority to PCT/CN2010/080304 priority patent/WO2012000296A1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image

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Abstract

The invention provides an image tilt correction method and a device. The image tilt correction method comprises the following steps: performing gray scale processing on the acquired image to obtain a gray image; and performing projection processing on the gray image to judge whether the gray image is tilted, if so, performing tilt correction processing on the gray image. The gray image is subjected to projection processing so as to judge whether the gray image is tilted; if the gray image is tilted, the gray image is subjected to tilt correction processing, and the gray image which does not tilt is not subjected to the tilt correction processing, thereby improving the correction efficiency of the image tilt correction method.

Description

Image tilt correction method and device
Technical field
The present invention relates to technical field of image processing, relate in particular to a kind of image tilt correction method and device.
Background technology
At present, along with development of ITS, image processing techniques is widely used in the intelligent transportation system.In intelligent transportation system, adopt image recognition technology that the car plate of vehicle is carried out image recognition usually, thereby realize obtaining automatically the license plate number of vehicle.
Because the influence of different road environments, the situation that the vehicle on road of travelling can occur tilting.In order to obtain the license board information of vehicle accurately, license plate image recognition system of the prior art need be carried out image correction process to the car plate of all detected vehicles, makes the image of car plate can be convenient to the number that image identification system identifies car plate.
As from the foregoing, image correction process method of the prior art is all carried out treatment for correcting to all detected images, and also carry out image correction process for the vehicle that does not have run-off the straight, increased the burden of image correction process greatly, influence the speed of the vehicle of car plate run-off the straight being carried out image correction process, reduced the correction efficient of image correction process.Therefore, the treatment effeciency of method for correcting image of the prior art is low.
Summary of the invention
The invention provides a kind of image tilt correction method and device,, realize improving the correction efficient of image tilt correction method in order to solve the inefficient defective of image tilt correction method in the prior art.
The invention provides a kind of image tilt correction method, comprising:
The image that obtains is carried out gray scale handle, to obtain gray level image;
Described gray level image is carried out projection process, whether tilt to judge described gray level image;
If described gray level image tilts, then described gray level image is carried out slant correction and handle.
The invention provides a kind of image inclination means for correcting, comprising:
The gray scale processing module is used for that the image that obtains is carried out gray scale and handles, to obtain gray level image;
Whether the projection process module is used for described gray level image is carried out projection process, tilt to judge described gray level image;
The treatment for correcting module is used for then described gray level image being carried out slant correction and handling if described gray level image tilts.
Image tilt correction method provided by the invention and device, by gray level image is carried out projection process, whether tilt to judge gray level image, if being in heeling condition, gray level image then this gray level image is carried out the slant correction processing, do not handle and do not carry out slant correction, improved the correction efficient of image tilt correction method for the gray level image that does not have to tilt.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, to do one to the accompanying drawing of required use in embodiment or the description of the Prior Art below introduces simply, apparently, accompanying drawing in describing below is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the process flow diagram of image tilt correction method embodiment of the present invention;
Fig. 2 is the particular flow sheet of step 102 among the image tilt correction method embodiment of the present invention;
Fig. 3 is the particular flow sheet of step 103 among the image tilt correction method embodiment of the present invention;
Fig. 4 is the structural representation of image inclination means for correcting embodiment of the present invention.
Fig. 5 is the structural representation of projection process module among the image inclination means for correcting embodiment of the present invention;
Fig. 6 is for proofreading and correct the structural representation of processing module among the image inclination means for correcting embodiment of the present invention.
Embodiment
For the purpose, technical scheme and the advantage that make the embodiment of the invention clearer, below in conjunction with the accompanying drawing in the embodiment of the invention, technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that is obtained under the creative work prerequisite.
Fig. 1 is the process flow diagram of image tilt correction method embodiment of the present invention.As shown in Figure 1, the present embodiment image tilt correction method comprises:
Step 101, the image that obtains is carried out gray scale handle, to obtain gray level image.
Particularly, pair image information of obtaining of the step 101 in the present embodiment is handled, and makes the image after the processing become gray level image.Present embodiment is that example describes in the intelligent transportation system vehicle license plate is detected, by the first-class image acquisition equipment of the shooting that is provided with on the road, obtain the image information of the vehicle license plate of travels down, then, carry out gray scale by the image information of step 101 pair car plate and handle, to obtain the gray level image of license plate image.
Step 102, gray level image is carried out projection process, whether tilt to judge gray level image.
Particularly, obtain the gray level image of car plate by step 101 after, the gray level image by step 102 pair acquisition carries out projection process, this judges whether gray level image is heeling condition the image information that obtains according to projection.
Step 103, if gray level image tilts, then gray level image is carried out slant correction and handles.
Particularly, after gray level image learns that by step 102 projection process this gray level image is for the image that tilts, then carry out slant correction and handle by step 103 pair this gray level image.For example: after the gray level image of the car plate of learning acquisition by step 102 is heeling condition, then can judge the car plate of car plate for tilting of detected vehicle, need carry out slant correction to the car plate that tilts handles, then the gray level image by step 103 pair car plate carries out treatment for correcting, with the gray level image of the car plate that obtains no angle of inclination, so that down-stream is according to the gray level image acquisition license plate number at no angle of inclination.
Wherein, the present embodiment image tilt correction method can be applied in the Vehicle License Plate Recognition System of intelligent transportation system, and also can be used for other need carry out the occasion that slant correction is handled to image.
The present embodiment image tilt correction method, by gray level image is carried out projection process, whether tilt to judge gray level image, if being in heeling condition, gray level image then this gray level image is carried out the slant correction processing, do not handle and do not carry out slant correction, improved the correction efficient of image tilt correction method for the gray level image that does not have to tilt.In addition, the present embodiment image tilt correction method only carries out slant correction to the image that is in heeling condition to be handled, but not tilt image need not to carry out slant correction and handle, thereby effectively avoid aclinal image image information loss to occur, help improving the efficient of image entire process process.
Based on technique scheme, optionally, as shown in Figure 2, the step 102 in the present embodiment specifically comprises the steps:
Step 1021, gray level image is carried out binary conversion treatment, to obtain binary image.Concrete, the gray level image that step 1021 pair step 101 obtains carries out binary conversion treatment, thereby gray level image is converted into binary image.For example: the drawing image of car plate is through after the binary conversion treatment, and license plate number will become white, and background will become black, thereby forms the binary image of black and white.
Step 1022, binary image is carried out vertical projection, to obtain the projected length of binary image.Concrete, will obtain the binary image of gray level image by step 1021, step 1022 will be carried out vertical projection to binary image, thereby can obtain the projected length of this binary image.For example: after the binary image of car plate carried out vertical projection, can on X-direction, form black and white projection at interval, and projected length can be the total length of white projection or the total length of the black projection between the white projection.
Step 1023, projected length and default projected length threshold values are compared.Concrete, by step 1022 binary image is carried out vertical projection after, will obtain the length of white view field of this binary image and the length of black view field.Because the projection of binary image comprises black and white two parts, then the projected length threshold values also correspondence include black length threshold values and white length threshold values.Wherein, black length threshold values is the length value that the image that is in non-heeling condition carries out the black region that obtains after the projection process, carries out the length value of the white portion that obtains after the projection process and white length threshold values is the image that is in non-heeling condition.The length in white view field to this binary image can compare with the white length threshold values of projected length threshold values; Length for the black view field of this binary image can compare with the black length threshold values of projected length threshold values.
Step 1024, if the projected length of the black of binary image less than the black length threshold values of projected length threshold values, determines that then gray level image is a heeling condition; Perhaps, if the projected length of the white of binary image, determines then that gray level image is a heeling condition greater than the white length threshold values of projected length threshold values.Concrete, the projected length that obtains after the binary image vertical projection with car plate describes.In the binary image of car plate, the image of license plate number is a white, and the image of background is a black.Because the fixed interval between the license plate number is constant, when license plate sloped, the white projected length of license plate number can increase, and the black projected length of the background area between the license plate number can shorten, thereby when the projected length of the black of the binary image of car plate black length threshold values less than the projected length threshold values, determine that then gray level image is a heeling condition, thereby determine that car plate is what tilt.Perhaps,, determine that then gray level image is a heeling condition, thereby determine that car plate is for tilting when when the projected length of the white of the binary image of car plate white length threshold values greater than the projected length threshold values.
The present embodiment image tilt correction method, by gray level image is carried out the projected length that projection process is learnt this gray level image, then projected length and pre-set length threshold values are compared, just can learn according to projected length whether this gray level image is what tilt easily, thereby more help improving the correction efficient of present embodiment image tilt correction method.
Based on technique scheme, optionally, as described in Figure 3, the step 103 in the present embodiment specifically comprises the steps:
Step 1031, obtain the marginal information of gray level image.Concrete, learn that by step 102 gray level image tilts after, need carry out slant correction by step 103 and handle.Step 1031 will be handled this gray level image, to obtain the marginal information of gray level image.In order to obtain the marginal information of gray level image more reliably, step 1031 in the present embodiment can obtain the marginal information of gray level image by the Canny operator, because the Canny operator can improve the scope of application of rim detection in a big way, thereby more helps obtaining accurately and reliably the marginal information of gray level image.
Step 1032, the pitch angle of calculating gray level image according to marginal information.Concrete, after the marginal information by step 1031 acquisition gray level image, calculate the pitch angle of this gray level image according to marginal information by step 1032.In order to extract the pitch angle of gray level image more accurately and effectively, the marginal information of the both sides that the step 1032 in the present embodiment can be by Hough transfer pair gray level image is carried out control treatment, to calculate the horizontal tilt angle of gray level image.For example: for the pairing marginal information of inclination car plate, respectively the efficient frontier information of two parts car plate is up and down extracted by the Hough conversion, and carry out control treatment, can calculate the level inclination of car plate fast and accurately, effectively avoid the influence of image information interfering lines such as car plate middle part, improved the accuracy of extracting the car plate inclination angle.
Step 1033, according to pitch angle rotation gray level image, with the gray level image at the no angle of inclination of output.Concrete, after calculating the pitch angle of gray level image correspondence, can be rotated processing according to the pitch angle to gray level image, with the gray level image at the no angle of inclination of output by step 1033 by step 1032.For rapid and reliable gray level image is rotated, and reduce the information loss of gray level image in the rotary course, step 1033 in the present embodiment can be according to the pitch angle, by the bilinear interpolation algorithm gray level image is rotated correction, by the bilinear interpolation algorithm to after the gray level image rotation, the gray level image at angle of inclination can be obtained not have, thereby the carrying out of subsequent image processing process can be made things convenient for.
The present embodiment image tilt correction method, by obtaining the marginal information of gray level image, and calculate the pitch angle according to marginal information, at last, according to the gray level image of rotation gray level image in pitch angle with the no angle of inclination of acquisition, can handle the gray level image that needs slant correction to handle fast and effectively, more help improving the correction efficient of present embodiment image tilt correction method.
Fig. 4 is the structural representation of image inclination means for correcting embodiment of the present invention.As shown in Figure 4, present embodiment image inclination means for correcting comprises: gray scale processing module 1, projection process module 2 and treatment for correcting module 3.
Gray scale processing module 1 is used for that the image that obtains is carried out gray scale to be handled, to obtain gray level image;
Whether projection process module 2 is used for gray level image is carried out projection process, tilt to judge gray level image;
Treatment for correcting module 3 is used for then gray level image being carried out slant correction and handling if gray level image tilts.
Particularly, the gray scale processing module 1 in the present embodiment is carried out the gray scale processing with the image that gets access to; Then, whether projection process module 2 will be handled the gray level image that generates to gray scale processing module 1 and carry out projection process, tilt to judge gray level image; At last, treatment for correcting module 3 will be carried out treatment for correcting to the gray level image that tilts, to obtain the aclinal gray level image of normal condition.Wherein, the concrete processing procedure of present embodiment image inclination means for correcting can not repeat them here referring to the record of image tilt correction method embodiment of the present invention.
Present embodiment image inclination means for correcting, by gray level image is carried out projection process, whether tilt to judge gray level image, if being in heeling condition, gray level image then this gray level image is carried out the slant correction processing, do not handle and do not carry out slant correction, improved the correction efficient of image tilt correction method for the gray level image that does not have to tilt.In addition, the present embodiment image tilt correction method only carries out slant correction to the image that is in heeling condition to be handled, but not tilt image need not to carry out slant correction and handle, thereby effectively avoid aclinal image image information loss to occur, help improving the efficient of image entire process process.
Based on technique scheme, optionally, as shown in Figure 5, the projection process module 2 in the present embodiment comprises: projection submodule 21, judgement submodule 22, first are determined submodule 23 and second definite submodule 24.
Projection submodule 21 is used for gray level image is carried out projection, to obtain the projected length of gray level image; Judge that submodule 22 is used to judge that whether projected length is less than the pre-set length threshold values; First determines that submodule 23 is used for if projected length, determines then that gray level image is a heeling condition less than the length threshold values; Second determines that submodule 24 is used for if projected length is not less than the length threshold values, determines that then gray level image is a normal condition.
Present embodiment image inclination means for correcting, by gray level image is carried out the projected length that projection process is learnt this gray level image, then projected length and pre-set length threshold values are compared, just can learn according to projected length whether this gray level image is what tilt easily, thereby more help improving the correction efficient of present embodiment image tilt correction method.
Based on technique scheme, optionally, as shown in Figure 6, the treatment for correcting module 3 in the present embodiment comprises: obtain submodule 31, calculating sub module 32 and gyrator module 33.
Obtain submodule 31 and be used for tilting, then obtain the marginal information of described gray level image as if described gray level image; Calculating sub module 32 is used for calculating according to described marginal information the pitch angle of described gray level image; Gyrator module 33 is used for rotating described gray level image according to described pitch angle, with the described gray level image at the no angle of inclination of output.
Wherein, in order to obtain the marginal information of gray level image more reliably, obtain the marginal information that submodule 31 also is used for obtaining by the Canny operator gray level image in the present embodiment; In order to extract the pitch angle of gray level image more accurately and effectively, calculating sub module 32 also is used for carrying out control treatment by the marginal information of the both sides of Hough transfer pair gray level image, to calculate the horizontal tilt angle of gray level image; For rapid and reliable gray level image is rotated, and reduces the information loss of gray level image in the rotary course, gyrator module 33 also is used for by the bilinear interpolation algorithm gray level image being rotated correction according to the pitch angle.
Present embodiment image inclination means for correcting, by obtaining the marginal information of gray level image, and calculate the pitch angle according to marginal information, at last, according to the gray level image of rotation gray level image in pitch angle with the no angle of inclination of acquisition, can handle the gray level image that needs slant correction to handle fast and effectively, more help improving the correction efficient of present embodiment image tilt correction method.
It should be noted that at last: above embodiment only in order to technical scheme of the present invention to be described, is not intended to limit; Although with reference to previous embodiment the present invention is had been described in detail, those of ordinary skill in the art is to be understood that: it still can be made amendment to the technical scheme that aforementioned each embodiment put down in writing, and perhaps part technical characterictic wherein is equal to replacement; And these modifications or replacement do not make the essence of appropriate technical solution break away from the spirit and scope of various embodiments of the present invention technical scheme.

Claims (9)

1. an image tilt correction method is characterized in that, comprising:
The image that obtains is carried out gray scale handle, to obtain gray level image;
Described gray level image is carried out projection process, whether tilt to judge described gray level image;
If described gray level image tilts, then described gray level image is carried out slant correction and handle.
2. whether image tilt correction method according to claim 1 is characterized in that, described described gray level image is carried out projection process, tilt to judge described gray level image, is specially:
Described gray level image is carried out binary conversion treatment, to obtain binary image;
Described binary image is carried out vertical projection, to obtain the projected length of described binary image;
Described projected length and the projected length threshold values of presetting are compared;
If the projected length of the black of described binary image, determines then that described gray level image is a heeling condition less than the black length threshold values of described projected length threshold values; Perhaps, if the projected length of the white of described binary image, determines then that described gray level image is a heeling condition greater than the white length threshold values of described projected length threshold values.
3. image tilt correction method according to claim 1 and 2 is characterized in that, describedly described gray level image is carried out slant correction handles, and is specially:
Obtain the marginal information of described gray level image;
Calculate the pitch angle of described gray level image according to described marginal information;
Rotate described gray level image according to described pitch angle, with the described gray level image at the no angle of inclination of output.
4. image tilt correction method according to claim 3 is characterized in that, the described marginal information of obtaining described gray level image is specially: the marginal information that obtains described gray level image by the Canny operator.
5. image tilt correction method according to claim 3, it is characterized in that, described pitch angle of calculating described gray level image according to described marginal information, be specially: the described marginal information of the both sides by the described gray level image of Hough transfer pair is carried out control treatment, to calculate the horizontal tilt angle of described gray level image.
6. image tilt correction method according to claim 3 is characterized in that, describedly rotates described gray level image according to described pitch angle, is specially: according to described pitch angle, by the bilinear interpolation algorithm described gray level image is rotated correction.
7. an image inclination means for correcting is characterized in that, comprising:
The gray scale processing module is used for that the image that obtains is carried out gray scale and handles, to obtain gray level image;
Whether the projection process module is used for described gray level image is carried out projection process, tilt to judge described gray level image;
The treatment for correcting module is used for then described gray level image being carried out slant correction and handling if described gray level image tilts.
8. image inclination means for correcting according to claim 7 is characterized in that, described projection process module comprises:
The projection submodule is used for described gray level image is carried out projection, to obtain the projected length of described gray level image;
Judge submodule, be used to judge that whether described projected length is less than the pre-set length threshold values;
First determines submodule, is used for if described projected length, determines then that described gray level image is a heeling condition less than described length threshold values;
Second determines submodule, is used for if described projected length is not less than described length threshold values, determines that then described gray level image is a normal condition.
9 according to claim 7 or 8 described image inclination means for correctings, it is characterized in that described treatment for correcting module comprises:
Obtain submodule, be used for then obtaining the marginal information of described gray level image if described gray level image tilts;
Calculating sub module is used for calculating according to described marginal information the pitch angle of described gray level image;
The gyrator module is used for rotating described gray level image according to described pitch angle, with the described gray level image at the no angle of inclination of output.
10. image inclination means for correcting according to claim 9 is characterized in that, describedly obtains the marginal information that submodule also is used for obtaining by the Canny operator described gray level image;
Described calculating sub module also is used for carrying out control treatment by the described marginal information of the both sides of the described gray level image of Hough transfer pair, to calculate the horizontal tilt angle of described gray level image;
Described gyrator module also is used for by the bilinear interpolation algorithm described gray level image being rotated correction according to described pitch angle.
CN 201010221775 2010-06-30 2010-06-30 Image tilt correction method and device Pending CN101923710A (en)

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CN108052936A (en) * 2017-11-03 2018-05-18 中国科学院计算技术研究所 A kind of braille image wing drop bearing calibration and system
CN110849326A (en) * 2019-12-25 2020-02-28 深圳供电局有限公司 Telegraph pole monitoring method and monitoring equipment
CN110849326B (en) * 2019-12-25 2022-06-07 深圳供电局有限公司 Telegraph pole monitoring method and monitoring equipment
CN112001238A (en) * 2020-07-14 2020-11-27 浙江大华技术股份有限公司 Terminal block wiring state recognition method, recognition device, and storage medium

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