CN109816674A - Registration figure edge extracting method based on Canny operator - Google Patents
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Abstract
The present invention relates to a kind of registration figure edge extracting method based on Canny operator, comprising: step A: the registration figure that completion is registrated in scene is smoothed；Step B: determine the light and shade of marginal point, and the gradient intensity and gradient direction of registration figure are determined；Step C: non-maxima suppression is carried out to each partial gradient region in gradient map global in the step B, determines the edge in registration figure；Step D: by further extracting the true edge point in registration figure using threshold method；Step E: the being attached property of marginal point after threshold process is analyzed, to form final edge image.By the present invention in that carrying out edge extracting to the target on registration figure with Canny operator, noise is smoothed and reduced to image, while improving detection accuracy, can prevent the error detection and omission during Edge extraction.
Description
Technical field
The present invention relates to image identification technical field more particularly to a kind of registration figure edge extractings based on Canny operator
Method.
Background technique
With the fast development of computer technology, target identification technology has rapidly developed into a kind of very important
Tool and means, application range are also increasingly wider.However since the related components such as nut handwheel target is by environment, shooting
Angle and own situation influence, and are generally difficult to be expressed with analytic expression, so that being identified to it as a unusual difficult task.It arrives
So far, researcher is directed to nut handwheel related components target identification, primarily directed to ideally, front shooting
Nut handwheel related components are identified that main method to be applied is that round Hough transform detects nut handwheel correlation zero
Part inner circle, color segmentation go out components position.For the nut on the big machinery of the real air to surface shooting of unmanned plane inspection
The identification of handwheel related components, it is fewer and fewer, and also the relevant judgement of components missing is also seldom.
China Patent Publication No.: CN103635169A discloses a kind of defect detecting system, comprising: image processing unit,
Defect detection unit and image-display units, wherein the image processing unit is configured to obtain the form of absorbent commodity
Image, the morphological image of the absorbent commodity show the shape after the absorbent commodity processing in each step of multiple steps
State, the defect detection unit are configured to detect the absorbability after processing based on the morphological image obtained by image processing unit
Article whether there is rejected region, which, which is configured to work as, detects absorbent commodity by defect detection unit
The image of absorbent commodity after showing processing when rejected region.It can be seen that the detection system has the following problems:
First, the detection system is only applied to assembly line, and by the position of fixed product, whether detection components quality
Meet regulation, can not accomplish accurate detection outdoors；
Second, the detection system is used only camera and carries out Image Acquisition to the fixed part in placement position, when product is put
When putting uneven or product space and changing, the image of actual acquisition can not be registrated with benchmark image, detection effect is not
It is ideal；
Third, the detection method can not be handled the edge of image, cause it that can not carry out to the shape of part
Precisely judgement；
4th, the detection system only determines part by the comparison of morphological image when being detected for defect
Whether defect or loss are occurred, and testing result is not accurate.
Summary of the invention
For this purpose, the present invention provides a kind of registration figure edge extracting method based on Canny operator, to overcome in the prior art
The problem of image border can not accurately being handled.
To achieve the above object, the present invention provides a kind of registration figure edge extracting method based on Canny operator, comprising:
Step A: being smoothed the registration figure that completion is registrated in scene, to prevent amplification noise, makes an uproar in registration figure
Point increases, and causes error detection, generates false edge and impacts to edge extracting；
Step B: being detected using edge of the differential operator to registration figure, is led using the first derivative or second order of registration figure
Number determines the light and shade of marginal point, and is determined using gradient intensity and gradient direction of the finite difference to registration figure；
Step C: non-maxima suppression is carried out to each partial gradient region in gradient map global in the step B, passes through guarantor
The pixel value of maximum point in partial gradient is stayed, while the gray value of non-maximum point corresponding points is set 0, to determine in registration figure
Edge；
Step D: by further extracting the true edge point in registration figure using threshold method, and pseudo-edge point is reduced；
Step E: the being attached property of marginal point after threshold process is analyzed, the edge picture not accessed is found in registration figure
Weak pixels all in registration figure are connected, to form final edge graph by vegetarian refreshments using eight connectivity with the edge pixel point
Picture.
Further, select Gaussian filter fixed with balances noise removal efficiency and edge detection in the step A
Position precision.
Further, include: using the process that Gaussian filter is smoothed registration figure in the step A
Convolution is carried out with figure is registrated using Gaussian filter, registration figure is smoothed, to reduce edge detector
Upper apparent influence of noise, wherein size for the Gaussian filter core of (2k+1) x (2k+1) growth equation formula such as formula (1) institute
Show:
The window of a 3x3 is A in image, and the pixel to be filtered is e, then passes through after gaussian filtering, pixel e's
Shown in brightness value such as formula (2):
Wherein * is convolution symbol, and all elements are added summation in sum representing matrix.
Further, the gaussian filtering core select 5 × 5 size with the runnability of balanced detector and its to noise
Susceptibility.
Further, the method being determined in the step B to registration figure gradient intensity and gradient direction includes:
Horizontal, the vertical and diagonal edge in registration figure are detected using four operators, by the calculation for calculating edge detection
Son returns to horizontal GxWith vertical GyThe first derivative values in direction, to determine the gradient intensity G and gradient direction θ of pixel:
θ=arctan (Gy/Gx) (4)
Wherein G is gradient intensity, and θ indicates gradient direction, and arctan is arctan function.
Further, the method for non-maxima suppression includes: in the step C
Step C1: the gradient intensity of current pixel is compared with two pixels on positive and negative gradient direction；
Step C2: relatively after, if the gradient intensity of current pixel be greater than other two comparison pixel, using the pixel as
Marginal point retains, if the gradient intensity of current pixel is less than other two comparison pixel, which is inhibited.
Further, in the step D using two threshold values of height to prevent threshold value is too low from pseudo-edge occur and accidentally deleted
High actual marginal point.
Further, include: using the method that two threshold values of height extract true edge point in registration figure in the step D
High threshold and Low threshold are selected, when the gradient value of edge pixel is higher than high threshold, then strong edge pixel is marked as, works as edge
The gradient value of pixel is less than high threshold and is greater than Low threshold, then weak edge pixel is marked as, when the gradient of edge pixel
Value is less than Low threshold, then can inhibit the edge pixel.
Compared with prior art, the beneficial effects of the present invention are by the present invention in that with Canny operator on registration figure
Target carry out edge extracting, noise, while improving detection accuracy, Neng Goufang are smoothed and reduced to image
The only error detection and omission during Edge extraction.
In particular, the present invention selects Gaussian filter when being smoothed to registration figure, in this way, scheming to registration
When being smoothed, the Gaussian filter can be by two kinds of parameters of noise remove efficiency and edge detection positioning accuracy
Balance further improves the detection accuracy of the method for the invention in a stationary value.
In particular, the present invention using Gaussian filter be registrated figure by way of convolution smoothly to be located to registration figure
Reason, by calculating the equation of gaussian filtering karyogenesis, can accurately obtain the brightness of each pixel in registration figure simultaneously
By the noise remove in pixel, reduces this method and the probability of error detection occur.
In particular, heretofore described filtering core selects 5 × 5 size, since the increase of size can be improved filtering core
Runnability, while also will increase its position error to edge detection, after selecting 5 × 5 sizes, the Gaussian smoothing filter
Device can reach an equalization point in runnability and detection error, further improve the detection essence of the method for the invention
Degree.
In particular, the present invention detects horizontal, vertical and diagonal edge in registration figure using four operators, and pass through calculating
The operator of edge detection returns to horizontal GxWith vertical GyThe first derivative values in direction, to determine the gradient intensity G and gradient of pixel
Direction θ, test object is few, it is easy to calculate step, and detection accuracy is high, improves the detection efficiency of the method for the invention.
In particular, the invention also includes non-maxima suppression methods, in this way, mentioning carrying out edge to registration figure using the method
When taking, quickly accurately edge point can be extracted, and inhibit other pixels.
In particular, the present invention detects registration figure using dual threshold when using threshold method, will not thus detect
In occur that threshold value is too low pseudo-edge is occurred and accidentally delete excessively high actual marginal point, further improve the detection of the extracting method
Efficiency.
Detailed description of the invention
Fig. 1 is the flow diagram of the registration figure edge extracting method of the present invention based on Canny operator；
Fig. 2 is the registration figure after the embodiment of the present invention is registrated live inspection figure and reference map；
Fig. 3 is that the embodiment of the present invention schemes the edge graph after progress edge extracting to registration；
Fig. 4 is the Objective extraction figure that the embodiment of the present invention extracts target part in specified region.
Specific embodiment
In order to which objects and advantages of the present invention are more clearly understood, the present invention is further retouched below with reference to embodiment
It states；It should be appreciated that specific embodiment described herein is used only for explaining the present invention, it is not intended to limit the present invention.
Below in conjunction with attached drawing, the forgoing and additional technical features and advantages are described in more detail.
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this
A little embodiments are used only for explaining technical principle of the invention, are not limiting the scope of the invention.
Refering to Figure 1, its flow chart element for the registration figure edge extracting method of the present invention based on Canny operator
Figure, when image of the unmanned plane to scene is acquired and when in the system that is transported on ground, the system can be to inspection
Figure and reference map are registrated, it can extract the edge in registration figure after registration, and specific step of registration includes:
Step A: being smoothed the registration figure that completion is registrated in scene, to prevent amplification noise, makes an uproar in registration figure
Point increases, and causes error detection, generates false edge and impacts to edge extracting；
Step B: being detected using edge of the differential operator to registration figure, is led using the first derivative or second order of registration figure
Number determines the light and shade of marginal point, and is determined using gradient intensity and gradient direction of the finite difference to registration figure；
Step C: non-maxima suppression is carried out to each partial gradient region in gradient map global in the step B, passes through guarantor
The pixel value of maximum point in partial gradient is stayed, while the gray value of non-maximum point corresponding points is set 0, to determine in registration figure
Edge；
Step D: by further extracting the true edge point in registration figure using threshold method, and pseudo-edge point is reduced；
Step E: the being attached property of marginal point after threshold process is analyzed, the edge picture not accessed is found in registration figure
Weak pixels all in registration figure are connected, to form final edge graph by vegetarian refreshments using eight connectivity with the edge pixel point
Picture.
It will be appreciated by those skilled in the art that the method for the invention cannot be only used for acquiring in field device
Registration figure carry out edge extracting, it can also be used to for the extraction of image border in other places, the present embodiment is not limited specifically
System, as long as image can be carried out quickly and accurate edge extracting by meeting the method for the invention.
Specifically, selecting Gaussian filter fixed with balances noise removal efficiency and edge detection in the step A
Position precision；Include: using the process that Gaussian filter is smoothed registration figure
Convolution is carried out with figure is registrated using Gaussian filter, registration figure is smoothed, to reduce edge detector
Upper apparent influence of noise, wherein size for the Gaussian filter core of (2k+1) x (2k+1) growth equation formula such as formula (1) institute
Show:
The window of a 3x3 is A in image, and the pixel to be filtered is e, then passes through after gaussian filtering, pixel e's
Shown in brightness value such as formula (2):
Wherein * is convolution symbol, and all elements are added summation in sum representing matrix.It is understood that the Gauss filter
The size of wave device can be 3 × 3,4 × 4,5 × 5 or other sizes, as long as meet the Gaussian filter can to registration figure into
Row smoothing processing simultaneously reduces noise in image.
Specifically, the method being determined in the step B to registration figure gradient intensity and gradient direction includes:
Horizontal, the vertical and diagonal edge in registration figure are detected using four operators, by the calculation for calculating edge detection
Son returns to horizontal GxWith vertical GyThe first derivative values in direction, to determine the gradient intensity G and gradient direction θ of pixel:
θ=arctan (Gy/Gx) (4)
Wherein G is gradient intensity, and θ indicates gradient direction, and arctan is arctan function.
Specifically, the method for non-maxima suppression includes: in the step C
Step C1: the gradient intensity of current pixel is compared with two pixels on positive and negative gradient direction；
Step C2: relatively after, if the gradient intensity of current pixel be greater than other two comparison pixel, using the pixel as
Marginal point retains, if the gradient intensity of current pixel is less than other two comparison pixel, which is inhibited.
Specifically, using two threshold values of height to prevent threshold value is too low from pseudo-edge occur and accidentally deleted in the step D
High actual marginal point, comprising: selection high threshold and Low threshold are then marked when the gradient value of edge pixel is higher than high threshold
Be denoted as strong edge pixel, when edge pixel gradient value be less than high threshold and be greater than Low threshold, then be marked as weak edge
Pixel, when edge pixel gradient value be less than Low threshold, then can inhibit the edge pixel.
Embodiment 1
The present embodiment can be detected to whether the handwheel components in field device lack, including three parts: be based on
SURF feature is become to being registrated of inspection figure and reference map, the registration figure edge extracting based on Canny operator and based on broad sense Hough
The components target identification changed；When being detected to field device, first with SURF algorithm to collected inspection figure and base
Quasi- figure is registrated, and is extracted after the completion of registration using edge of the Canny operator to registration figure, to export edge image；It is defeated
Using Generalized Hough Transform to edge image and template image progress mutual information calculating after the completion of out, and according to calculating
The size of association relationship can determine whether to lack.It is understood that detection method described in the present embodiment cannot be only used for pair
Whether handwheel lacks and is judged in equipment, can also in image nut or other parts whether lack and judge, as long as
Its specified working condition can be reached by meeting the present embodiment each section.
Specifically, first part is registrated inspection figure with reference map by SURF feature in the present embodiment, including
Euclidean distance sequence between SURF feature extraction, characteristic point is calculated with the screening of directrix Euclidean distance and affine matrix, comprising:
Step 1.1: to image carry out SURF feature extraction, comprising establish integral image, construction approximation Hessian matrix,
Tectonic scale space and precise positioning feature point；
Step 1.2: the feature extracted using SURF algorithm, SURF operator have scale, rotational invariance, and right
There is very strong robustness in picture noise, light variation, affine deformation etc., so for identical object in inspection figure and reference map
Body, the Feature Descriptor extracted be very close to, with distance metric.SURF feature is carried out to inspection figure and reference map
After extraction, use Euclidean distance method as similarity measurement；
Step 1.3: making inspection figure and reference map parallel arranged, demarcate matching double points in inspection figure and reference map respectively
Position and its corresponding connection straight line carry out the degree of registration linear distance according to the matching double points of characteristic point Euclidean distance obtained
Amount because inspection figure and reference map are shot using same camera, the format and size for the photo shot be it is the same,
Measure still selects Euclidean distance method, with the programmed screening for carrying out registration point pair near directrix average value；
Step 1.4: the matching double points selected to finishing screen calculate affine change according to the position of each in the picture
Matrix is changed, the registration by inspection figure to reference map is finally realized using the matrix；
By taking the screening for matching directrix Euclidean distance method to carry out registration pair, and the calculating for carrying out affine matrix is used to
Registration, although as shown in Fig. 2, result is not high without the registration accuracy in the case of missing, be also able to achieve generally without mistake
Really match alignment request.
Specifically, second part of the present invention is extracted by edge of the Canny operator to image, it include Gauss filter
The smooth input picture of wave device, gradient magnitude image and the calculating of angular image, the non-maximum restraining of gradient magnitude image, dual threshold
Processing and linking parsing, comprising:
Step 2.1: Gaussian filter smoothing processing inspection figure and Prototype drawing are utilized, due in Canny detection process, having pair
The derivative calculations process of image, and the calculated result of derivative does not have robustness for noise, it is very sensitive to noise, so wanting
It is smoothed, just not will cause the amplification of noise figure in this way, noise is more, and imaginary point can be such that false edge becomes with regard to more
More, adverse effect will cause to the extraction at edge, but smothing filtering and edge detection are conflicting both sides, because flat
Although sliding filtering can effectively inhibit noise jamming, also the edge of image can be made to thicken, this side after allowing for
There is uncertainty in edge positioning operation, according to many practical engineering experiences of forefathers as a result, Gaussian filter can be with
It accurately detects to position on noise remove and side and a preferable half-way house is provided between the two paradox；
Step 2.2: the edge of image is extracted, it is slower in the transformation along the pixel value in edge direction,
And perpendicular in the normal direction of the edge direction pixel value variation just more acutely because generally related to object it
Between, the color change between scene, between region etc., differential operator, which provides one kind to calculate the variation on this edge, to be had
The method of power in Practical Project, the detection at edge is carried out with the single order of image or second dervative, it is determined whether have marginal point can
Whether on the slope with the method for first derivative, that is, to judge this point, then, Second Derivative Methods can be used for light and shade judgement,
Namely judge an edge pixel point belong to it is bright on one side or it is dark while the single order local derviation ,ed using image, i.e., it is limited
The determination of difference progress image gradient amplitude and direction；
Step 2.3: the inhibition of non-maximum, above-mentioned processing are carried out to each partial gradient region in global gradient map
Obtained global gradient is not meant to real edge, at this time in order to determine edge it is necessary to each in global gradient map
A partial gradient region carries out the inhibition of non-maximum, thus retain the maximum point of partial gradient, the point in image, corresponding figure
As the value in gradient magnitude matrix is bigger, illustrate that its gradient value is bigger, this belongs to one of image enchancing method, can not table
Show that this point is exactly marginal point, non-maxima suppression is the pith in Canny edge detection method, is briefly exactly to find
Local maximum in gradient image retains the pixel value of the point in corresponding original image, and non-maximum point is corresponding
The gray value of point set 0；
Step 2.4: handled using threshold method to reduce pseudo-edge point, the proposition of threshold method is in order to further
Extract true marginal point, if only using a threshold value, lower than the value point value all can zero setting, threshold value is too low at this time
Pseudo-edge just will appear, and excessively high actual marginal point can be deleted accidentally, in order to improve this case, use two threshold values of height；
Step 2.5: after threshold process, forming longer edge line and being attached property is then needed to analyze, look in the picture
Weak pixels all in image are connected to the point, are formed most by the edge pixel point also not visited to one using 8 connectivity
Whole edge image.
By Canny operator to the edge extracting of picture after registration as shown in figure 3, can be obtained according to Fig. 3, by using
Image edge after Canny operator extraction is also apparent.
Part III of the present invention be the components target identification based on Generalized Hough Transform, including reference point locations selection,
R-table is established, the space Hough is established, peak value positioning and mutual information calculate, comprising:
Step 3.1: the selection of reference point, reference point can be any point in template edge image, including non-edge
The position of point, is typically chosen the central point of template edge image, since the edge of handwheel part is under the influence of environment, detected
Shape it is usually and irregular, so the coordinate for finally choosing template picture top left corner pixel is reference point；
Step 3.2: with template edge image total edge points and distance reference amount, establishing R-table matrix；
Step 3.3: for each marginal point in inspection edge picture, it is corresponding discrete to calculate gradient by above-mentioned rule
Value, for each in template edge picture fall into marginal point, will according to R-table calculate inspection edge picture in accord with
The corresponding reference coordinate of the marginal point of conjunction, establishes the space Hough, and space size is identical as inspection picture space size；
Step 3.4: finding the maximal peak point in the space Hough, that is, search out optimal reference point.
Step 3.5: utilizing Generalized Hough Transform method, reference coordinate is found in inspection image, correspondence mappings are template
The coordinate of upper left angle point the picture of template size, and and template image are extracted in inspection image with the reference coordinate position
Mutual information calculating is done, judges whether part lacks according to the size of the association relationship calculated.
In order to verify the reliability of this part method, the present embodiment to reference map carry out template extraction and first to reference map into
Row identification is identified very accurate handwheel recognition result after registration using Generalized Hough Transform method using obtained template
As shown in figure 4, can be obtained according to Fig. 4, this part method is also very accurate to the identification of target in the picture.When handwheel missing, benefit
The extracted region of template size is carried out with the location information that Generalized Hough Transform obtains, and does mutual information calculating with template picture,
It may determine that whether handwheel lacks.
So far, it has been combined preferred embodiment shown in the drawings and describes technical solution of the present invention, still, this field
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific embodiments.Without departing from this
Under the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to the relevant technologies feature, these
Technical solution after change or replacement will fall within the scope of protection of the present invention.
The above description is only a preferred embodiment of the present invention, is not intended to restrict the invention；For those skilled in the art
For member, the invention may be variously modified and varied.All within the spirits and principles of the present invention, it is made it is any modification,
Equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of registration figure edge extracting method based on Canny operator characterized by comprising
Step A: being smoothed the registration figure that completion is registrated in scene, to prevent amplification noise, is registrated noise in figure and increases
Add, cause error detection, generate false edge and edge extracting is impacted；
Step B: being detected using edge of the differential operator to registration figure, is sentenced using the first derivative or second dervative of registration figure
Determine the light and shade of marginal point, and is determined using gradient intensity and gradient direction of the finite difference to registration figure；
Step C: non-maxima suppression is carried out to each partial gradient region in gradient map global in the step B, passes through reservation office
The pixel value of maximum point in portion's gradient, while the gray value of non-maximum point corresponding points is set 0, to determine the side in registration figure
Edge；
Step D: by further extracting the true edge point in registration figure using threshold method, and pseudo-edge point is reduced；
Step E: the being attached property of marginal point after threshold process is analyzed, the edge pixel not accessed is found in registration figure
Weak pixels all in registration figure are connected, to form final edge image by point using eight connectivity with the edge pixel point.
2. the registration figure edge extracting method according to claim 1 based on Canny operator, which is characterized in that the step
Select Gaussian filter with balances noise removal efficiency and edge detection positioning accuracy in rapid A.
3. the registration figure edge extracting method according to claim 2 based on Canny operator, which is characterized in that the step
Include: using the process that Gaussian filter is smoothed registration figure in rapid A
Convolution is carried out with figure is registrated using Gaussian filter, registration figure is smoothed, it is bright on edge detector to reduce
Aobvious influence of noise, wherein size is shown in the growth equation formula such as formula (1) of the Gaussian filter core of (2k+1) x (2k+1):
The window of a 3x3 is A in image, and the pixel to be filtered is e, then passes through after gaussian filtering, the brightness of pixel e
Value is as shown in formula (2):
Wherein * is convolution symbol, and all elements are added summation in sum representing matrix.
4. the registration figure edge extracting method according to claim 3 based on Canny operator, which is characterized in that the height
The size of this filtering core selection 5 × 5 is with the runnability of balanced detector and its susceptibility to noise.
5. the registration figure edge extracting method according to claim 1 based on Canny operator, which is characterized in that the step
The method being determined in rapid B to registration figure gradient intensity and gradient direction includes:
Horizontal, the vertical and diagonal edge in registration figure are detected using four operators, the operator by calculating edge detection returns
The flat G of return waterxWith vertical GyThe first derivative values in direction, to determine the gradient intensity G and gradient direction θ of pixel:
θ=arctan (Gy/Gx) (4)
Wherein G is gradient intensity, and θ indicates gradient direction, and arctan is arctan function.
6. the registration figure edge extracting method according to claim 1 based on Canny operator, which is characterized in that the step
The method of non-maxima suppression includes: in rapid C
Step C1: the gradient intensity of current pixel is compared with two pixels on positive and negative gradient direction；
Step C2: after relatively, if the gradient intensity of current pixel is greater than other two comparison pixel, using the pixel as edge
Point retains, if the gradient intensity of current pixel is less than other two comparison pixel, which is inhibited.
7. the registration figure edge extracting method according to claim 1 based on Canny operator, which is characterized in that the step
Using two threshold values of height to prevent threshold value is too low from pseudo-edge occur and accidentally delete excessively high actual marginal point in rapid D.
8. the registration figure edge extracting method according to claim 7 based on Canny operator, which is characterized in that the step
Suddenly the use of the method that two threshold values of height extract true edge point in registration figures includes: selection high threshold and Low threshold in D, work as side
The gradient value of edge pixel is higher than high threshold, then is marked as strong edge pixel, when the gradient value of edge pixel is less than high threshold
And be greater than Low threshold, then be marked as weak edge pixel, when edge pixel gradient value be less than Low threshold, then can inhibit institute
State edge pixel.
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| CN116363160A (en) * | 2023-05-30 | 2023-06-30 | 杭州脉流科技有限公司 | CT perfusion image brain tissue segmentation method and computer equipment based on level set |
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| CN117036222A (en) * | 2023-08-18 | 2023-11-10 | 广东省水利水电科学研究院 | Water body detection method, device and medium for fusing multi-scale polarized SAR images |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521836A (en) * | 2011-12-15 | 2012-06-27 | 江苏大学 | Edge detection method based on gray-scale image of specific class |
| CN108550165A (en) * | 2018-03-18 | 2018-09-18 | 哈尔滨工程大学 | A kind of image matching method based on local invariant feature |
-
2018
- 2018-12-27 CN CN201811612185.6A patent/CN109816674A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521836A (en) * | 2011-12-15 | 2012-06-27 | 江苏大学 | Edge detection method based on gray-scale image of specific class |
| CN108550165A (en) * | 2018-03-18 | 2018-09-18 | 哈尔滨工程大学 | A kind of image matching method based on local invariant feature |
Non-Patent Citations (2)
| Title |
|---|
| 靳艳红: "基于改进Canny算法的图像边缘检测的研究", pages 18 - 32 * |
| 颜孙溯: "基于机器视觉的PCB板有极性电子元件检查算法研究", 《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》, pages 9 - 70 * |
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