CN104850851A - ORB feature point matching method with scale invariance - Google Patents
ORB feature point matching method with scale invariance Download PDFInfo
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Abstract
The present invention relates to an ORB feature point matching method with scale invariance. The method is characterized by comprising the steps of a step S1 of inputting an image to be detected, performing improved SURF feature point detection on the image, and determining coordinates of the feature points; a step S2 of establishing an image pyramid for the image in the step S1; a step S3 of removing the feature points close to edges of the image; a step S4 of calculating directions of centers of mass of the remained feature points; a step S5 of calculating ORB feature point descriptors; a step S6 of adopting a K-nearest neighbor algorithm to carry out feature point matching; and a step S7 of screening feature point matching pairs and outputting the detected image. According to the method provided by the present invention, the SURF with the scale invariance and the ORB are combined, the image pyramid is introduced, and an ORB feature point matching algorithm is improved, so as to enable the method to have the scale invariance and maintain the characteristic of fastness of the ORB algorithm.
Description
Technical field
The invention belongs to
figurepicture process field, particularly a kind of ORB characteristic point matching method with scale invariability.
Background technology
figurethe picture detection of unique point, description and coupling, be
figurean extremely important branch in picture process field.
figurepicture identification, video tracking,
figurethe realization of many technology such as picture splicing, three-dimensional reconstruction, all will rely on
figurethe picture detection of unique point, description and coupling.
ORB is a kind of outstanding feature point detection, description and matching algorithm, uses FAST Corner Detection and binary-coded Brief descriptor fast, its fast operation, illumination robustness is good, affinity is also good, but does not have scale invariability, causes ORB to have limitation in application.SURF is steadily and surely a kind of
figurepicture identifies and describes algorithm, adopts integration
figurepicture and box filter carry out convolution, using gaussian kernel function to build metric space, by solving quick Hessian response extraction unique point, utilizing moving window to build to the unique point extracted
vertical maindirection, generates the feature descriptor of a 64bit.SURF has good scale invariability, but arithmetic speed is comparatively slow, is difficult to be applied in in the higher system of requirement of real-time.
Summary of the invention
In view of this, the object of this invention is to provide a kind of ORB characteristic point matching method with scale invariability, in conjunction with SURF, ORB is improved, introduce simultaneously
figurepicture pyramid, makes it the outstanding illumination robustness of existing ORB, fast arithmetic capability, has again the scale invariability of SURF.
The present invention adopts following scheme to realize: a kind of ORB characteristic point matching method with scale invariability, and it comprises the following steps:
Step S1: input to be detected
figurepicture, right
figurepicture carries out the SURF feature point detection improved, and determines unique point coordinate;
Step S2: in described step S1
figurepicture is set up
figurepicture pyramid;
Step S3: remove close
figurethe unique point at picture edge;
Step S4: the barycenter direction calculating residue character point;
Step S5: calculate ORB unique point descriptor;
Step S6: adopt k nearest neighbor algorithm to carry out Feature Points Matching;
Step S7: screening Feature Points Matching to and output detections after
figurepicture.
Further, the SURF feature point detection improved in described step S1 adopts integration
figurepicture and box filter carry out convolution, build metric space solve quick Hessian response extraction unique point by gaussian kernel function, and at the 3*3*3 neighbor interpolation of described unique point, determine described unique point coordinate.
Further, described metric space is 16 layers of metric space structure of not dividing into groups.
Further, set up in described step S2
figureas pyramidal concrete grammar be: according to the size of Filtering Template in the SURF feature point detection improved, to be detected to what input
figureit is down-sampled that picture carries out Gauss, and the down-sampled mesoscale value of described Gauss is
wherein N is the size of Filtering Template.
Further, the square by calculating unique point in described step S4 draws the barycenter direction of unique point, and concrete grammar is: definition
figurein picture, (p+q) rank square of any unique point neighborhood is:
Then the center-of-mass coordinate C of its neighborhood is:
Can computing formula
draw the barycenter direction θ of this unique point; Described formula is used to draw the barycenter direction of all residue characters point.
Further, the concrete grammar calculating ORB unique point descriptor in described step S5 is: step S51: adopt exhaust algorithm calculate n related coefficient close to 0.5 random point pair; Step S52: the random point in step S51 is rotated the barycenter direction according to unique point described in step S4, at Feature point correspondence
figurein picture pyramidal layer, adopt formula
Generate scale-of-two descriptor, in formula, (x, y) is postrotational random point pair, and p (x) is
figurethe gray-scale value of picture block p at pixel x=(u, v) place; Show that n position binary bits string is unique point descriptor according to described formula.
Further, in described step S6, k nearest neighbor algorithm is specially: set K as 2, calculate each unique point to all unique points to be matched Hamming distance from, retain described 2 nearest points, with this unique point form mate right.
Further, screening the right concrete grammar of Feature Points Matching in described step S7 is: by the coupling of the coupling centering arest neighbors matching distance/time neighborhood matching distance > 0.6 in step S6 to rejecting, get the final match point that arest neighbors match point is this unique point.
Preferably, a coupling forms by the coordinate of two unique points and respective descriptor thereof, matching distance be Hamming distance between two descriptors from.
The invention has the advantages that and overcome the shortcoming that ORB does not have scale invariability, in conjunction with SURF, ORB is improved, and introduce
figurepicture pyramid, makes the algorithm after improvement have scale invariability, retains the feature of the good illumination robustness of ORB and fast operation simultaneously.
Accompanying drawing explanation
figure1 is the overall flow of the embodiment of the present invention
figure.
Embodiment
Below in conjunction with attached
figureand embodiment the present invention will be further described.
The present embodiment provides a kind of ORB characteristic point matching method with scale invariability, as
figureshown in 1, comprise the following steps:
Step S1: input to be detected
figurepicture, right
figurepicture carries out the SURF feature point detection improved, and determines unique point coordinate;
Step S2: in described step S1
figurepicture is set up
figurepicture pyramid;
Step S3: remove close
figurethe unique point at picture edge;
Step S4: the barycenter direction calculating residue character point;
Step S5: calculate ORB unique point descriptor;
Step S6: adopt k nearest neighbor algorithm to carry out Feature Points Matching;
Step S7: screening Feature Points Matching to and output detections after
figurepicture.
In the present embodiment, the SURF feature point detection improved in described step S1 adopts integration
figurepicture and box filter carry out convolution, build metric space solve quick Hessian response extraction unique point by gaussian kernel function, and at the 3*3*3 neighbor interpolation of described unique point, determine described unique point coordinate.
In the present embodiment, preferably, described metric space is 16 layers of metric space structure of not dividing into groups.
In the present embodiment, set up in described step S2
figureas pyramidal concrete grammar be: according to the size of Filtering Template in the SURF feature point detection improved, to be detected to what input
figureit is down-sampled that picture carries out Gauss, and the down-sampled mesoscale value of described Gauss is
wherein N is the size of Filtering Template.
In the present embodiment, the square by calculating unique point in described step S4 draws the barycenter direction of unique point, and concrete grammar is: definition
figurein picture, (p+q) rank square of any unique point neighborhood is:
then the center-of-mass coordinate C of its neighborhood is:
Can computing formula
Draw the barycenter direction θ of this unique point; Described formula is used to draw the barycenter direction of all residue characters point.
In the present embodiment, the concrete grammar calculating ORB unique point descriptor in described step S5 is:
Step S51: adopt exhaust algorithm calculate n related coefficient close to 0.5 random point pair;
Step S52: the random point in step S51 is rotated the barycenter direction according to unique point described in step S4, at Feature point correspondence
figurein picture pyramidal layer, adopt formula
Generate scale-of-two descriptor, in formula, (x, y) is postrotational random point pair, and p (x) is
figurethe gray-scale value of picture block p at pixel x=(u, v) place; Show that n position binary bits string is unique point descriptor according to described formula.
In the present embodiment, in described step S6, k nearest neighbor algorithm is specially: set K as 2, calculate each unique point to all unique points to be matched Hamming distance from, retain described 2 nearest points, with this unique point form mate right.
In the present embodiment, screening the right concrete grammar of Feature Points Matching in described step S7 is: by the coupling of the coupling centering arest neighbors matching distance/time neighborhood matching distance > 0.6 in step S6 to rejecting, get the final match point that arest neighbors match point is this unique point.
In the present embodiment, especially, if the unique point that existence two groups is to be matched, then first can calculate the k nearest neighbor coupling of 2 stack features points respectively, reject asymmetric coupling right, then the coupling rejecting arest neighbors matching distance/time neighborhood matching distance > 0.6 is right.The coupling stayed after screening is right, gets the final match point that its arest neighbors match point is this feature.
In the present embodiment, a coupling forms by the coordinate of two unique points and respective descriptor thereof, matching distance be Hamming distance between two descriptors from.
The foregoing is only preferred embodiment of the present invention, all equalizations done according to the present patent application the scope of the claims change and modify, and all should belong to covering scope of the present invention.
Claims (8)
1. there is an ORB characteristic point matching method for scale invariability, it is characterized in that comprising the following steps:
Step S1: input image to be detected, to the SURF feature point detection that image improves, and determines unique point coordinate;
Step S2: image pyramid is set up to the image in described step S1;
Step S3: remove the unique point near image border;
Step S4: the barycenter direction calculating residue character point;
Step S5: calculate ORB unique point descriptor;
Step S6: adopt k nearest neighbor algorithm to carry out Feature Points Matching;
Step S7: screening Feature Points Matching to and export image to be detected.
2. a kind of ORB characteristic point matching method with scale invariability according to claim 1, it is characterized in that: the SURF feature point detection improved in described step S1 adopts integral image and box filter to carry out convolution, build metric space by gaussian kernel function and solve quick Hessian response extraction unique point, and at the 3*3*3 neighbor interpolation of described unique point, determine described unique point coordinate.
3. a kind of ORB characteristic point matching method with scale invariability according to claim 2, is characterized in that: described metric space is 16 layers of metric space structure of not dividing into groups.
4. a kind of ORB characteristic point matching method with scale invariability according to claim 1, it is characterized in that: the concrete grammar setting up image pyramid in described step S2 is: according to the size of Filtering Template in the SURF feature point detection improved, carry out Gauss to the image to be detected of input down-sampled, the down-sampled mesoscale value of described Gauss is
wherein N is the size of Filtering Template.
5. a kind of ORB characteristic point matching method with scale invariability according to claim 1, it is characterized in that: the square by calculating unique point in described step S4 draws the barycenter direction of unique point, and concrete grammar is: in definition image, (p+q) rank square of any unique point neighborhood is:
then the center-of-mass coordinate C of its neighborhood is:
can computing formula
draw the barycenter direction θ of this unique point; Described formula is used to draw the barycenter direction of all residue characters point.
6. a kind of ORB characteristic point matching method with scale invariability according to claim 1, is characterized in that: the concrete grammar calculating ORB unique point descriptor in described step S5 is:
Step S51: adopt exhaust algorithm calculate n related coefficient close to 0.5 random point pair; Step S52: rotated the barycenter direction according to unique point described in step S4 by the random point in step S51, on the image pyramid layer of Feature point correspondence, adopts formula
generate scale-of-two descriptor, in formula, (x, y) is postrotational random point pair, and p (x) is for image block p is at the gray-scale value at pixel x=(u, v) place; Show that n position binary bits string is unique point descriptor according to described formula.
7. a kind of ORB characteristic point matching method with scale invariability according to claim 1, it is characterized in that: in described step S6, k nearest neighbor algorithm is specially: set K as 2, calculate each unique point to all unique points to be matched Hamming distance from, retain described 2 nearest points, with this unique point form mate right.
8. a kind of ORB characteristic point matching method with scale invariability according to claim 1, it is characterized in that: screening the right concrete grammar of Feature Points Matching in described step S7 is: by the coupling of the coupling centering arest neighbors matching distance/time neighborhood matching distance > 0.6 in step S6 to rejecting, get the final match point that arest neighbors match point is this unique point.
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Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106504229A (en) * | 2016-09-30 | 2017-03-15 | 上海联影医疗科技有限公司 | The detection method of characteristic point in image |
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US10580135B2 (en) | 2016-07-14 | 2020-03-03 | Shanghai United Imaging Healthcare Co., Ltd. | System and method for splicing images |
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102915372A (en) * | 2012-11-06 | 2013-02-06 | 成都理想境界科技有限公司 | Image retrieval method, device and system |
CN103745236A (en) * | 2013-12-20 | 2014-04-23 | 清华大学 | Texture image identification method and texture image identification device |
CN103927387A (en) * | 2014-04-30 | 2014-07-16 | 成都理想境界科技有限公司 | Image retrieval system, method and device |
CN104050475A (en) * | 2014-06-19 | 2014-09-17 | 樊晓东 | Reality augmenting system and method based on image feature matching |
-
2015
- 2015-04-22 CN CN201510193048.3A patent/CN104850851A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102915372A (en) * | 2012-11-06 | 2013-02-06 | 成都理想境界科技有限公司 | Image retrieval method, device and system |
CN103745236A (en) * | 2013-12-20 | 2014-04-23 | 清华大学 | Texture image identification method and texture image identification device |
CN103927387A (en) * | 2014-04-30 | 2014-07-16 | 成都理想境界科技有限公司 | Image retrieval system, method and device |
CN104050475A (en) * | 2014-06-19 | 2014-09-17 | 樊晓东 | Reality augmenting system and method based on image feature matching |
Non-Patent Citations (2)
Title |
---|
崔振兴等: "一种改进的SURF快速匹配算法"", 《江苏师范大学学报(自然科学版)》 * |
许宏科等: ""基于改进的ORB的图像特征点匹配"", 《科学技术与工程》 * |
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US10580135B2 (en) | 2016-07-14 | 2020-03-03 | Shanghai United Imaging Healthcare Co., Ltd. | System and method for splicing images |
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CN106504229A (en) * | 2016-09-30 | 2017-03-15 | 上海联影医疗科技有限公司 | The detection method of characteristic point in image |
CN106894481A (en) * | 2017-01-13 | 2017-06-27 | 杨徐子谦 | One kind is based on intelligent closestool automatic detection and cleans dirt method and system |
CN106894481B (en) * | 2017-01-13 | 2019-07-16 | 杨徐子谦 | One kind is detected automatically based on intelligent closestool and is cleaned dirt method and system |
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CN108496186A (en) * | 2017-08-31 | 2018-09-04 | 深圳市大疆创新科技有限公司 | Characteristic extracting circuit and image processing IC |
CN107944471A (en) * | 2017-11-03 | 2018-04-20 | 安徽工程大学 | A kind of ORB characteristic point matching methods based on Nonlinear Scale Space Theory |
CN108010045A (en) * | 2017-12-08 | 2018-05-08 | 福州大学 | Visual pattern characteristic point error hiding method of purification based on ORB |
CN108455228A (en) * | 2017-12-29 | 2018-08-28 | 长春师范大学 | The automatic Load System of tire |
CN108455228B (en) * | 2017-12-29 | 2023-07-28 | 长春师范大学 | Automatic tire loading system |
CN109859225A (en) * | 2018-12-24 | 2019-06-07 | 中国电子科技集团公司第二十研究所 | A kind of unmanned plane scene matching aided navigation localization method based on improvement ORB Feature Points Matching |
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