CN106407902A - Geometric difference-based airplane object identification method - Google Patents

Geometric difference-based airplane object identification method Download PDF

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CN106407902A
CN106407902A CN201610789468.2A CN201610789468A CN106407902A CN 106407902 A CN106407902 A CN 106407902A CN 201610789468 A CN201610789468 A CN 201610789468A CN 106407902 A CN106407902 A CN 106407902A
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polygon
aircraft
profile
identified
point
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冯琦
王彦芳
赵艺阳
骆嫱
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Northwestern Polytechnical University
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Abstract

The invention provides a geometric difference-based airplane object identification method. A contour is extracted after an image of an object is subjected to image processing operation, an approximation polygon of the contour of the object is constructed via a minimum perimeter polygon algorithm, the area of the approximation polygon is enabled to be equal to template area after the approximation polygon is enlarged or shrunk according to a template database standard object, the approximation polygon is enabled to be partially superposed with template database figures via an arrangement algorithm, and identification operation can be conducted. That the method is simple and easy to implement is verified via a simulation experiment, the object can be identified quickly, high accuracy can be realized, and the geometric difference-based airplane object identification method has anti-geometric deformation and anti-noise performance.

Description

A kind of Aircraft Target Recognition based on disparity
Technical field
The invention belongs to field of target recognition, relate generally to the extraction of aircraft geometric properties and the identification of Aircraft Targets.
Background technology
Aircraft Target Identification is by being analyzed in real time to the photo captured by imaging radar or reconnaissance satellite, to determine Aircraft Targets.Existing most of recognition methodss need to carry out feature extraction to target, further according to the feature extracted using each Plant algorithm to be mated with aircraft signature in template base, be primarily present problems with：
(1) target characteristic extracting requires in principle with invariance, but sends out when shooting azimuth, gray scale, contrast etc. Changing can change often, that is, it is non-existent for having absolute Invariance feature vector, needs extraction to have relatively stable The characteristic vector of property；
(2) the extraction room and time complexity of complex characteristic is high, calculates complicated, reduces recognition efficiency, therefore will extract Feature preferably simply easily extracts feature；
(3) with the appearance of type aircraft, the three dimensional structure of aircraft becomes more complicated, and aircraft shoots under different attitudes Image difference very big, to feature extraction and aircraft identification bring bigger challenge.
Therefore, set up an accuracy rate height, to have anti-geometric deformation and the Aircraft Recognition of noiseproof feature be to have emphatically Big meaning.
Content of the invention
In order to overcome the deficiencies in the prior art, the present invention provides a kind of Aircraft Target Recognition based on disparity, Target can fast and effeciently be identified.
The technical solution adopted for the present invention to solve the technical problems comprises the following steps：
(1) utilize certain direction view outline polygon of aircraft, obtain the profile of other direction views by affine transformation Polygon；Travel through each model aircraft, set up aircraft multi-pose model library；
(2) normalizing is carried out to the aircraft profile in the aircraft profile in aircraft brake disc to be identified and aircraft multi-pose model library Change and process so that the aircraft profile in aircraft brake disc to be identified has identical with the aircraft profile in aircraft multi-pose model library Area；
(3) aircraft outline polygon overlap processing, comprises the following steps：
(3.1) the aircraft profile in aircraft brake disc to be identified is designated as polygon A, the aircraft in aircraft multi-pose model library Profile is designated as polygon B；Calculate center of gravity O of polygon A respectivelyABarycentric coodinates O with polygon BB；
(3.2) polygon B is rotated and translated certain a line each bar with polygon A successively so that polygon B Side is overlapping and midpoint overlaps, each bar side of traversal polygon B, calculates weighing for i-th of polygon A in j-th with polygon B O when foldedA、OBApart from dI, j；
(3.3) will be apart from dI, jWith default threshold value d*Compare, record meets dI, j<d*When corresponding polygon B attitude；
(4) identify target aircraft, comprise the following steps：
(4.1) ask for the common factor of polygon A and polygon B, obtain common factor polygon；
(4.2) ask for the polygonal area S that occurs simultaneouslyIntersectionAnd the area S of polygon A；
(4.3) seek template similarity coefficient ξ=SIntersection/S；
(4.4) the aircraft profile in the maximum aircraft multi-pose model library of template similarity coefficient, corresponding aircraft class are chosen Type is target to be identified and corresponds to target.
Before described step (2), Image semantic classification is carried out to aircraft brake disc to be identified, including image gray processing, figure As smooth, image filtering, image enhaucament, Image Edge-Detection and polygonal segments.
Described step (2) comprises the following steps：
(2.1) the aircraft profile in aircraft brake disc to be identified is designated as polygon A, the aircraft in aircraft multi-pose model library Profile is designated as polygon B；Calculate the area S of polygon A and polygon B respectivelyA、SB；Likelihood ratio k=B/A of polygon A, B；
(2.2) take up an official post from polygon A place plane and take each summit of 1 point of O, junction point O and polygon A, calculate every company Line length li, i=1,2,3,4 ..., n, n represent polygon vertex number, take point B on every line or its extended line respectively1、 B2、B3、…、BnSo that each point and point O are apart from Li=k*li；
(2.3) junction point B1、B2、B3、…、Bn, obtain the polygon after normalization.
The invention has the beneficial effects as follows：The Target Photo getting is extracted profile after image procossing, and with Xiao Zhou Long polygon algorithm constructs the approximate polygon of objective contour；Then zoomed in or out after process according to template base standard target Make its area equal with template area；Reuse and put algorithm and so that it is overlapped with template base visuals；And a kind of improvement is proposed Type doubly linked list algorithm seeks polygon intersection, reaches the purpose of identification image by calculating intersection size.This It is simple that invention demonstrates the method through emulation experiment, can quickly identify target, and accuracy rate is high, has anti-geometric deformation And noiseproof feature.
Brief description
Fig. 1 is method of the present invention flow chart；
Fig. 2 is intersection point schematic diagram in embodiment；
Fig. 3 is aircraft various putting position schematic diagram in embodiment；
Fig. 4 is the method flow diagram traveling through each intersection point in embodiment.
Specific embodiment
The present invention is further described with reference to the accompanying drawings and examples, and the present invention includes but are not limited to following enforcements Example.
The present invention provides a kind of Aircraft Target Identification algorithm based on disparity, and its step is：
(1) aircraft single-view multi-pose model library is set up：Using certain direction view outline polygon of aircraft, with affine Conversion obtains other multi-angled view it is established that multiple view model storehouse；
(2) target aircraft Image semantic classification：To aircraft brake disc to be identified, carry out pretreatment first, to acquired original figure As being processed, reduce the impacts to subsequent treatment for the invalid information such as noise, strengthen useful information, improve picture contrast；
(3) aircraft profile standardization：With template base multi-pose aircraft, similar polygon principle is utilized to target aircraft profile Do normalized so as to have identical area；
(4) aircraft outline polygon overlap processing：Make the aircraft polygon in aircraft to be identified and template base by a set pattern Then overlapping, and by center of gravity restriction method, obtained overlap mode data base is simplified；
(5) identify target aircraft：Travel through storage intersection point using doubly linked list and ask for aircraft profile intersection of polygon, according to friendship Collection area identifies aircraft with the area ratio of former outline polygon.
As a further improvement on the present invention, the idiographic flow of described step (1) is：
(1.1) collect and obtain all kinds of aircraft pictures, obtain the outline polygon of all kinds of aircrafts after processing；
(1.2) model aircraft image outline is done the attitude figure that affine transformation can be obtained by its each attitude, thus setting up Play aircraft multi-pose model library.
As a further improvement on the present invention, the idiographic flow of described step (1.2) is：
(1.2.1) set up affine Transform Model
Wherein,Det (A) ≠ 0,
(1.2.2) rotation and translation transformation set：a1=b2=cosj ,-a2=b1=sin j；
(1.2.3) homothetic conversion (change of scale in image) sets：a1=b2=T, a2=b1=0, a=Z=0；
(1.2.4) shear transformation sets：a1=b2=1, a2=0, b1=k, a=Z=0.
As a further improvement on the present invention, the idiographic flow of described step (3) is：
(3.1) calculate the area S of polygon A and polygon BA、SB.The likelihood ratio of polygon A, B is k (B/A)；
(3.2) take up an official post from plane and take 1 point of O, connect each summit of this point and polygon A, calculate every wire length li(i =1,2,3,4 ..., n) (n represents polygon vertex number), pressed the direction prolongation away from O and (worked as k<In O and summit line when 1 On take a little) make Li=k*li, obtain point B1、B2、B3、…、Bn；
(3.3) junction point B1、B2、B3、…、BnIt is amplified the polygon after (or reducing).
As a further improvement on the present invention, the idiographic flow of described step (4) is：
(4.1) calculate center of gravity O of polygon AAAnd barycentric coodinates O of polygon BB；
(4.2) make B through rotation, after translation from a line start successively with A first, second ... until last A line is overlapping and midpoint overlaps, and calculates and put rear O each timeA、OBApart from dI, j(i, j) (represents that i-th side of A falls On j-th side of B)；
(4.3) centroidal distance threshold value d is set*；
(4.4) d will be metI, j<d*Adjustment putting position polygon be sequentially output.
As a further improvement on the present invention, the idiographic flow of described step (5) is：
(5.1) may have access to forerunner using doubly linked list and the construction featuress of descendant node and easy insertion point ask for be measured flying Machine polygon and template base aircraft intersection of polygon；
(5.2) ask for common factor area of a polygon SIntersectionAnd aircraft area of a polygon S to be measured；
(5.3) seek template similarity coefficient ξ=SIntersection/S；
(5.4) compare similarity coefficient, what similarity coefficient was maximum is target correspondence target to be identified.
As a further improvement on the present invention, the idiographic flow of described step (5.1) is：
(5.1.1) defining polygon A is aircraft polygon to be measured, and polygon B is template base aircraft polygon；
(5.1.2) A vertex sequence is put into chain Table A according to sequence counter-clockwise, by B vertex sequence according to counter-clockwise Put into chained list B；
(5.1.3) A and B is made to be put according to described in step (4)；
(5.1.4) obtain all intersecting point coordinates of A, B, and intersection point is inserted into A, B chained list；
(5.1.5) travel through intersection point；
(5.1.6) judge whether that all intersection points are all traversed, without then going to step (5.1.5), otherwise output is handed over Collection polygon.
As shown in figure 4, the idiographic flow of described step (5.1.5) is：
(5.1.5.1) an access point intersection point entering into B from A is selected to begin stepping through chained list；
(5.1.5.2) when reaching next intersection point, judge whether intersection point is former chained list polygon vertex, go to another if not One chained list continues traversal, otherwise judges whether this intersection point is another chained list polygon vertex again, if otherwise along former polygonal chain Table continues traversal, otherwise judges whether with intersection point as starting point two polygonal sides overlap, goes to another polygon if not Chained list continues traversal, and otherwise continuing traversal to next intersection point along former polygon chained list is overlapping line segment end, then judges along the inverse time When pin moves towards, whether intersection point is access point with respect to another polygon, if it is continues traversal along former polygon chained list, otherwise turns To another polygon traversal；
(5.1.5.3) judging whether intersection point is intersection point selected by (5.1.5.1), if otherwise going to (5.1.5.2), otherwise tying Bundle traversal, obtains a polygonal common factor chained list.
Embodiments of the invention comprise the following steps：
Step 1, aircraft single-view multi-pose model library are set up.Using certain direction view outline polygon of aircraft, use Affine transformation obtains other multi-angled view it is established that multiple view model storehouse；
Concrete steps are carried out as follows：
1.1 collections obtain all kinds of aircraft pictures, obtain the outline polygon of all kinds of aircrafts after processing；
Model aircraft image outline is done the attitude figure that affine transformation can be obtained by its each attitude, thus setting up by 1.2 Aircraft multi-pose model library.
Step 2, target aircraft Image semantic classification.To aircraft brake disc to be identified, carry out pretreatment first, to acquired original Image is processed, and reduces the impacts to subsequent treatment for the invalid information such as noise, strengthens useful information, improves picture contrast.
Step 3, aircraft profile standardization：Utilize similar polygon former in target aircraft profile and template base multi-pose aircraft Reason does normalized so as to have identical area；
Concrete steps are carried out as follows：
The 3.1 area S calculating polygon A and polygon BA、SB.The likelihood ratio of polygon A, B is k (B/A)；
3.2 take up an official post from plane takes 1 point of O, connects each summit of this point and polygon A, calculates every wire length li(i= 1,2,3,4 ..., n) (n represents polygon vertex number), pressed the direction prolongation away from O and (worked as k<When 1 on O with summit line Take a little) make Li=k*li, obtain point B1、B2、B3、…、Bn；
3.3 junction point B1、B2、B3、…、BnIt is amplified the polygon after (or reducing).
Step 4, aircraft outline polygon overlap processing.Make the aircraft polygon in aircraft to be identified and template base by certain Rule is overlapping, and by center of gravity restriction method, obtained overlap mode data base is simplified；
Concrete steps are carried out as follows：
Center of gravity O of 4.1 calculating polygon AAAnd barycentric coodinates O of polygon BB；
4.2 make B through rotation, after translation from a line start successively with A first, second ... until last Bar side is overlapping and midpoint overlaps, and calculates and put rear O each timeA、OBApart from dI, j(i, j) (represents that i-th side of A falls in B J-th side on)；
4.3 setting centroidal distance threshold values d*=15；
4.4 will meet dI, j<d*Adjustment putting position polygon be sequentially output, as shown in figure 3, in figure is various putting The parameter of position is as shown in the table：
(a1) (a2) (a3) (a4)
Aircraft A overlap side 5 6 12 16
Aircraft B overlap side 4 2 17 34
Centroidal distance 3.579 6.107 3.961 12.590
Common factor area of a polygon 22231.6 17705.2 15653.0 17568.3
Aircraft area of a polygon 25052 25052 25052 25052
Common factor ratio 0.887 0.707 0.625 0.701
(b1) (b2) (b3) (b4)
Aircraft A overlap side 16 18 19 0
Aircraft B overlap side 33 4 33 0
Centroidal distance 7.911 8.734 8.734 0
Common factor area of a polygon 17751.4 17704.0 17704.0 27149.9995
Aircraft area of a polygon 27150 27150 27150 17150
Common factor ratio 0.654 0.652 0.652 1
Step 5, identification target aircraft.Travel through storage intersection point using doubly linked list and ask for aircraft profile intersection of polygon, root Area ratio identification aircraft according to common factor area and former outline polygon.Specifically carry out as follows：
5.1 ask for common factor polygon using doubly linked list.Specifically carry out as follows：
5.1.1 setting A is aircraft polygon to be measured, and B is template base polygon；
5.1.2 generate the corresponding chained list of polygon A, B：Chained list A={ a1, a2..., a38, chained list B={ b1, b2..., b38}；
5.1.3 according to the algorithm of putting limiting based on center of gravity, suitable disposing way, such as the 6th side of figure A and the 2nd of B are chosen Overlapping (while start counting up from 0)；
5.1.4 all intersection points of A are obtained：I={ I1, I2..., I17, intersection point is inserted in A, B chained list, obtains new A, B Chained list：
A '={ a1..., a6, I1, a7, I2, I3, a8..., a11, I5, a12, a13(I4), a14, a17(I6), a19, I7, a20..., a24(I8) ..., a26, I11, a27, I10, a28, I9..., a32(I12), I13, a33, I14, I15, I16, a34, I17... a38, a1}；
B '={ b1..., b3(I2), I3, b4, b5(I4), I5, I6, I7, b6, b7, I8, b8(I9), I10, b9, I11, b10..., b14, I12, b15(I13), b16..., b23, I14, b24, b30, b31(I15), b32, I16, b33, I17, b34, b35, I1, b36..., b38, b1}；
5.1.5 travel through intersection point, obtain common factor polygon chained list W：
W={ I1, a7, I2, I3, a8..., a11, I5, I6, I7, a20..., I8, I9, a29..., a31, I11, I12, b16, b23, I13, I14, b32, I15, a34, I16, b34, b35, I1}∪{I10, b9, I11, a27}
5.2 ask for common factor area of a polygon
SIntersection=17705.2
Aircraft area of a polygon to be measured
S=25052；
5.3 seek template similarity coefficient
ξ=SIntersection/ S=0.707；
5.4 compare similarity coefficient identification target, select the corresponding aircraft of b4 template to be that aircraft to be measured corresponds to type in figure legends Number.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited to above-described embodiment, all The technical scheme belonging under thinking of the present invention belongs to protection scope of the present invention.It should be pointed out that it is general for the art For logical technical staff, some improvements and modifications without departing from the principles of the present invention, should be regarded as the protection model of the present invention Enclose.

Claims (3)

1. a kind of Aircraft Target Recognition based on disparity is it is characterised in that comprise the steps：
(1) utilize certain direction view outline polygon of aircraft, the profile obtaining other direction views by affine transformation is polygon Shape；Travel through each model aircraft, set up aircraft multi-pose model library；
(2) place is normalized to the aircraft profile in the aircraft profile in aircraft brake disc to be identified and aircraft multi-pose model library Reason is so that the aircraft profile in aircraft brake disc to be identified has identical face with the aircraft profile in aircraft multi-pose model library Long-pending；
(3) aircraft outline polygon overlap processing, comprises the following steps：
(3.1) the aircraft profile in aircraft brake disc to be identified is designated as polygon A, the aircraft profile in aircraft multi-pose model library It is designated as polygon B；Calculate center of gravity O of polygon A respectivelyABarycentric coodinates O with polygon BB；
(3.2) polygon B is rotated and translated certain a line each bar side weight with polygon A successively so that polygon B Folded and midpoint overlaps, each bar side of traversal polygon B, calculate i-th of polygon A overlapping in j-th with polygon B when OA、OBApart from dI, j；
(3.3) will be apart from dI, jWith default threshold value d*Compare, record meets dI, j<d*When corresponding polygon B attitude；
(4) identify target aircraft, comprise the following steps：
(4.1) ask for the common factor of polygon A and polygon B, obtain common factor polygon；
(4.2) ask for the polygonal area S that occurs simultaneouslyIntersectionAnd the area S of polygon A；
(4.3) seek template similarity coefficient ξ=SIntersection/S；
(4.4) choose the aircraft profile in the maximum aircraft multi-pose model library of template similarity coefficient, corresponding type of airplane is Correspond to target for target to be identified.
2. the Aircraft Target Recognition based on disparity according to claim 1 it is characterised in that：Described step (2) before, Image semantic classification is carried out to aircraft brake disc to be identified, including image gray processing, image smoothing, image filtering, figure Image intensifying, Image Edge-Detection and polygonal segments.
3. the Aircraft Target Recognition based on disparity according to claim 1 is it is characterised in that described step (2) comprise the following steps：
(2.1) the aircraft profile in aircraft brake disc to be identified is designated as polygon A, the aircraft profile in aircraft multi-pose model library It is designated as polygon B；Calculate the area S of polygon A and polygon B respectivelyA、SB；Likelihood ratio k=B/A of polygon A, B；
(2.2) take up an official post from polygon A place plane and take each summit of 1 point of O, junction point O and polygon A, calculate every line long Degree li, i=1,2,3,4 ..., n, n represent polygon vertex number, take point B on every line or its extended line respectively1、B2、 B3、…、BnSo that each point and point O are apart from Li=k*li；
(2.3) junction point B1、B2、B3、…、Bn, obtain the polygon after normalization.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108052942A (en) * 2017-12-28 2018-05-18 南京理工大学 A kind of visual pattern recognition methods of aircraft flight attitude
RU2708346C1 (en) * 2017-03-22 2019-12-05 Александр Иванович Стучилин Method for identification of aircraft type by means of passive optical location and device for implementation thereof
WO2021164328A1 (en) * 2020-02-17 2021-08-26 腾讯科技（深圳）有限公司 Image generation method, device, and storage medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101877062A (en) * 2009-12-21 2010-11-03 汉王科技股份有限公司 Method for profile analysis in image layout area
CN102930300A (en) * 2012-11-21 2013-02-13 北京航空航天大学 Method and system for identifying airplane target
CN103914687A (en) * 2014-03-14 2014-07-09 常州大学 Rectangular-target identification method based on multiple channels and multiple threshold values
CN104105439A (en) * 2012-02-06 2014-10-15 皇家飞利浦有限公司 Invisible bifurcation detection within vessel tree images
CN105469094A (en) * 2015-11-28 2016-04-06 重庆交通大学 Edge vector line extraction algorithm of binary image of road surface
CN107516095A (en) * 2016-06-16 2017-12-26 阿里巴巴集团控股有限公司 A kind of image-recognizing method and device

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101877062A (en) * 2009-12-21 2010-11-03 汉王科技股份有限公司 Method for profile analysis in image layout area
CN104105439A (en) * 2012-02-06 2014-10-15 皇家飞利浦有限公司 Invisible bifurcation detection within vessel tree images
CN102930300A (en) * 2012-11-21 2013-02-13 北京航空航天大学 Method and system for identifying airplane target
CN103914687A (en) * 2014-03-14 2014-07-09 常州大学 Rectangular-target identification method based on multiple channels and multiple threshold values
CN105469094A (en) * 2015-11-28 2016-04-06 重庆交通大学 Edge vector line extraction algorithm of binary image of road surface
CN107516095A (en) * 2016-06-16 2017-12-26 阿里巴巴集团控股有限公司 A kind of image-recognizing method and device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王彦芳等: ""基于几何差异的目标识别算法"", 《计算机测量与控制》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2708346C1 (en) * 2017-03-22 2019-12-05 Александр Иванович Стучилин Method for identification of aircraft type by means of passive optical location and device for implementation thereof
CN108052942A (en) * 2017-12-28 2018-05-18 南京理工大学 A kind of visual pattern recognition methods of aircraft flight attitude
CN108052942B (en) * 2017-12-28 2021-07-06 南京理工大学 Visual image recognition method for aircraft flight attitude
WO2021164328A1 (en) * 2020-02-17 2021-08-26 腾讯科技（深圳）有限公司 Image generation method, device, and storage medium
US11847812B2 (en) 2020-02-17 2023-12-19 Tencent Technology (Shenzhen) Company Limited Image generation method and apparatus, device, and storage medium

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