CN104657988A - Image segmentation method for micro-fine cohesive core particles based on angular point and curvature detection - Google Patents
Image segmentation method for micro-fine cohesive core particles based on angular point and curvature detection Download PDFInfo
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- CN104657988A CN104657988A CN201510060055.6A CN201510060055A CN104657988A CN 104657988 A CN104657988 A CN 104657988A CN 201510060055 A CN201510060055 A CN 201510060055A CN 104657988 A CN104657988 A CN 104657988A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
- G06T2207/10061—Microscopic image from scanning electron microscope
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20164—Salient point detection; Corner detection
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Abstract
The invention discloses an image segmentation method for micro-fine cohesive core particles based on angular point and curvature detection and is suitable for use in research on crushed mineral particle images. The method comprises the following steps: firstly, performing preprocessing on a mineral image; secondly, performing Harris angular point detection on an obtained binary image; thirdly, identifying concave points, that is to say, cohesive particle connecting points by utilizing curvature information of each angular point, adopting a certain rule according to characteristics of the concave points, determining an optimal segmentation path, and finishing segmentation of the cohesive ore particles. According to the method, the concave points are identified in combination with the angular point and curvature information by searching the angular points existent in a target region, the target region of the image is segmented through the directivity characteristics of the concave points and the nearest neighbor rule, and segmentation of the cohesive particles in the whole ore particle image is finally finished; the method is simple and can effectively segment a region of a large amount of cohesive particles in the image and restore distribution situation of the micro-fine ore particles in the image to the maximum extent.
Description
Technical field
The present invention relates to a kind of ore particles image partition method, be particularly useful for a kind of microfine adhesion ore particles image partition method based on angle point and curvature measuring used when studying for break mined material particle image.
Background technology
The fundamental purpose of mineral processing is separated by the valuable mineral in raw ore, one of them committed step is ground by tcrude ore, reach the object that valuable mineral dissociates, having document to prove there is certain relation in the grain graininess of ore grinding and degree of dissociation, therefore is an important technology to the accurate detection of ore particles granularity.General mineral processes adopts sieve method to detect grain graininess, and the method measures ore particles size by adopting the sieve of limited quantity, and error is larger.Utilize image procossing to ore particles Image Segmentation Using at present, identify it is a kind of accurate method.Digital camera can be adopted to carry out getting figure for millimetre-sized ore particles, but must adopt for micron particles the scanning electron microscope that enlargement ratio is higher, equally because the granularity of ore is less, intergranular adhesion is stronger, make adhesion phenomenon between particle very serious, and grain graininess is as the key character index of mineral grain material, its Measurement accuracy each technique to particle following process has important directive significance.In order to accurate analysis mineral grain particle size Lambda characterization, before analysis, particle image must be split and is separated.
Summary of the invention
For the weak point of above-mentioned technology, provide a kind of step simple, simple and fast the microfine adhesion ore particles image partition method based on angle point and curvature measuring.
For achieving the above object, the present invention takes following technical scheme: based on the microfine adhesion ore particles image partition method of angle point and curvature measuring, its step is as follows:
A. scanning electron microscope is used to get figure to ground ore particles, utilize smooth function to the step of the smoothing process of ore particles image sequence, image threshold, shape filtering, removal edge particle, thus complete the binaryzation to ore particles image;
B. the image that there is microfine adhesion situation in the ore particles image after binaryzation is selected and is used as target area, Harris Corner Detection is carried out to the ore particles image of target area:
1) utilize level, vertically difference operator to carry out filtering to each pixel in the ore particles image of target area, obtain obtaining the first order derivative I of pixel in horizontal and vertical direction
x, I
y, utilize formula:
Obtain the second order Hessian matrix m of pixel function, in formula
i
xi
ybe respectively the second-order partial differential coefficient of this pixel function, and whether each pixel is extreme point to utilize Hessian matrix m to judge;
2) discrete two-dimensional zero-mean gaussian function formula is utilized:
to Hessian matrix
In four element values carry out Gaussian smoothing filter, obtain filtered Hessian matrix m';
3) formula is utilized:
filtered Hessian matrix m' value is substituted into the ore particles image slices vegetarian refreshments of target area, calculate the angle point amount cim of each pixel;
4) the angle point amount cim of each pixel and pre-set threshold value thresh is compared, mark the pixel that each is greater than pre-set threshold value thresh, when angle point amount cim value is greater than pre-set threshold value, then judge that this pixel is Harris angle point;
C. using each Harris angle point as center of circle curvature, radius is 5 pixels, passes through formula:
calculate the circular mask that each Harris angle point is corresponding, in formula: j represents a jth angle point, | L| is the girth of circular mask, | A
j| for a jth angle point be the circular mask in the center of circle and the ore particles image of target area coincide part mask camber line;
D. utilize formula: curvature (j) > 0.5, compare judgement to each angle point, the angle point of coincidence formula is concave point P
j, and get rid of non-concave point;
E. each concave point P is defined
jcircular mask and the mask camber line B of ore particles image not intersection of target area
j, according to mask camber line B
jlength definition mask camber line B
jmid point C
j, with concave point P
jfor launching site is to connection mid point C
jthe ray P formed
jc
jbe this concave point P
jdirection;
F. concave point list of coordinates is set up, using the top pixel of concave point coordinate to be matched as linear partition, other concave points in search listing mate with it, find and meet and concave point that direction contrary nearest with concave point to be matched as terminal pixel, Bresenham algorithm is adopted to draw defiber, complete the segmentation of the ore particles image of target area, the process of circulation coupling concave point, until concave points all in this connected domain is all mated.
Whether each pixel be the method for extreme point is that if m is positive definite matrix, then this point is minimal value to utilize Hessian matrix m to judge in step b, if m is negative definite matrix, then this point is maximum value, if m is indefinite matrix, then this point is not extreme value; The angle point response function R of angle point amount cim is R=λ
1λ
2-k* (λ
1+ λ
2)
2, in formula: λ
1, λ
2for two quadrature components that Hessian matrix m' obtains after real symmetric matrix diagonalization process, k is coefficient, and span is [0.04,0.06]; The radius of the level and smooth contour curve that pre-set threshold value thresh is produced by four element values in Gaussian function filtered Hessian matrix m, the variance measure parameter of Gaussian function and supporting domain determines, here Gaussian function scale parameter σ=2.5, the radius in skeletal support territory is 1, then the interval of threshold value is [0.004,0.008]; If the quantity of concave point is odd number in concave point list of coordinates, then after overmatching, ignore a remaining concave point.
Beneficial effect: because most of ore particles presents sharp-featured state, therefore this method is by finding the angle point existed in target area, in conjunction with angle point and curvature information, thus the concave point identified wherein, by directivity feature and the Nearest neighbor rule of concave point, thus image target area is split, finally complete the segmentation of adhesion particle in whole ore particles image, its method is simple, effectively can split the region of a large amount of adhesion particle in image, at utmost go back the distribution situation of fine-disseminated ore stone granulate in original image.
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention;
Fig. 2 is that the concave point of circular mask in a target area of the present invention detects and concave point direction expression figure.
Embodiment
Below in conjunction with accompanying drawing, embodiments of the present invention are further described:
As shown in Figure 1, the microfine adhesion ore particles image partition method based on angle point and curvature measuring of the present invention, its step is as follows:
A. scanning electron microscope is used to get figure to ground ore particles, utilize smooth function to the step of the smoothing process of ore particles image sequence, image threshold, shape filtering, removal edge particle, thus complete the binaryzation to ore particles image;
B. the image that there is microfine adhesion situation in the ore particles image after binaryzation is selected and is used as target area, Harris Corner Detection is carried out to the ore particles image of target area:
1) utilize level, vertically difference operator to carry out filtering to each pixel in the ore particles image of target area, obtain obtaining the first order derivative I of pixel in horizontal and vertical direction
x, I
y, utilize formula:
Obtain the second order Hessian matrix m of pixel function, in formula
i
xix is respectively the second-order partial differential coefficient of this pixel function, and whether each pixel is extreme point to utilize Hessian matrix m to judge; Whether each pixel be the method for extreme point is that if m is positive definite matrix, then this point is minimal value to utilize Hessian matrix m to judge, if m is negative definite matrix, then this point is maximum value, if m is indefinite matrix, then this point is not extreme value;
2) discrete two-dimensional zero-mean gaussian function formula is utilized:
to Hessian matrix
In four element values carry out Gaussian smoothing filter, obtain filtered Hessian matrix m';
3) formula is utilized:
filtered Hessian matrix m' value is substituted into the ore particles image slices vegetarian refreshments of target area, calculate the angle point amount cim of each pixel; Wherein the angle point response function R of angle point amount cim is R=λ
1λ
2-k* (λ
1+ λ
2)
2, in formula: λ
1, λ
2for two quadrature components that Hessian matrix m' obtains after real symmetric matrix diagonalization process, k is coefficient, and span is [0.04,0.06];
4) the angle point amount cim of each pixel and pre-set threshold value thresh is compared, mark the pixel that each is greater than pre-set threshold value thresh, when angle point amount cim value is greater than pre-set threshold value, then judge that this pixel is Harris angle point; The radius of the level and smooth contour curve that pre-set threshold value thresh is produced by four element values in Gaussian function filtered Hessian matrix m, the variance measure parameter of Gaussian function and supporting domain determines, here Gaussian function scale parameter σ=2.5, the radius in skeletal support territory is 1, then the interval of threshold value is [0.004,0.008];
C. using each Harris angle point as center of circle curvature, radius is 5 pixels, passes through formula:
calculate the circular mask that each Harris angle point is corresponding, in formula: j represents a jth angle point, | L| is the girth of circular mask, | A
j| for a jth angle point be the circular mask in the center of circle and the ore particles image of target area coincide part mask camber line;
D. utilize formula: curvature (j) > 0.5, compare judgement to each angle point, the angle point of coincidence formula is concave point P
j, and get rid of non-concave point;
E. each concave point P is defined
jcircular mask and the mask camber line B of ore particles image not intersection of target area
j, according to mask camber line B
jlength definition mask camber line B
jmid point C
j, with concave point P
jfor launching site is to connection mid point C
jthe ray P formed
jc
jbe this concave point P
jdirection; The concave point being circular mask in target area as Fig. 2 detects and concave point direction expression figure, P in figure
1, P
2, P
3for angle point, A in figure
1, A
2, A
3be respectively angle point P
1, P
2, P
3for three circular masks in the center of circle and the ore particles image of target area coincide part mask camber line, B
1, B
2, B
3be respectively angle point P
1, P
2, P
3for the mask camber line of the ore particles image not intersection of three circular masks in the center of circle and target area, C
1, C
2, C
3be respectively mask camber line B
1, B
2, B
3mid point;
F. concave point list of coordinates is set up, using the top pixel of concave point coordinate to be matched as linear partition, other concave points in search listing mate with it, find and meet and concave point that direction contrary nearest with concave point to be matched as terminal pixel, Bresenham algorithm is adopted to draw defiber, complete the segmentation of the ore particles image of target area, the process of circulation coupling concave point, until concave points all in this connected domain is all mated, if the quantity of concave point is odd number in concave point list of coordinates, then after overmatching, ignore a remaining concave point.
Claims (5)
1., based on a microfine adhesion ore particles image partition method for angle point and curvature measuring, it is characterized in that step is as follows:
A. scanning electron microscope is used to get figure to ground ore particles, utilize smooth function to the step of the smoothing process of ore particles image sequence, image threshold, shape filtering, removal edge particle, thus complete the binaryzation to ore particles image;
B. the image that there is microfine adhesion situation in the ore particles image after binaryzation is selected and is used as target area, Harris Corner Detection is carried out to the ore particles image of target area:
1) utilize level, vertically difference operator to carry out filtering to each pixel in the ore particles image of target area, obtain obtaining the first order derivative I of pixel in horizontal and vertical direction
x, I
y, utilize formula:
Obtain the second order Hessian matrix m of pixel function, in formula
i
x 2, I
y 2, I
xi
ybe respectively the second-order partial differential coefficient of this pixel function, and whether each pixel is extreme point to utilize Hessian matrix m to judge;
2) discrete two-dimensional zero-mean gaussian function formula is utilized:
to Hessian matrix
In four element values carry out Gaussian smoothing filter, obtain filtered Hessian matrix m';
3) formula is utilized:
filtered Hessian matrix m' value is substituted into the ore particles image slices vegetarian refreshments of target area, calculate the angle point amount cim of each pixel;
4) the angle point amount cim of each pixel and pre-set threshold value thresh is compared, mark the pixel that each is greater than pre-set threshold value thresh, when angle point amount cim value is greater than pre-set threshold value, then judge that this pixel is Harris angle point;
C. using each Harris angle point as center of circle curvature, radius is 5 pixels, passes through formula:
calculate the circular mask that each Harris angle point is corresponding, in formula: j represents a jth angle point, | L| is the girth of circular mask, | A
j| for a jth angle point be the circular mask in the center of circle and the ore particles image of target area coincide part mask camber line;
D. utilize formula: curvature (j) > 0.5, compare judgement to each angle point, the angle point of coincidence formula is concave point P
j, and get rid of non-concave point;
E. each concave point P is defined
jcircular mask and the mask camber line B of ore particles image not intersection of target area
j, according to mask camber line B
jlength definition mask camber line B
jmid point C
j, with concave point P
jfor launching site is to connection mid point C
jthe ray P formed
jc
jbe this concave point P
jdirection;
F. concave point list of coordinates is set up, using the top pixel of concave point coordinate to be matched as linear partition, other concave points in search listing mate with it, find and meet and concave point that direction contrary nearest with concave point to be matched as terminal pixel, Bresenham algorithm is adopted to draw defiber, complete the segmentation of the ore particles image of target area, the process of circulation coupling concave point, until concave points all in this connected domain is all mated.
2. the microfine adhesion ore particles image partition method based on angle point and curvature measuring according to claim 1, its feature exists: whether each pixel is the method for extreme point and is to utilize Hessian matrix m to judge, if m is positive definite matrix, then this point is minimal value, if m is negative definite matrix, then this point is maximum value, if m is indefinite matrix, then this point is not extreme value.
3. the microfine adhesion ore particles image partition method based on angle point and curvature measuring according to claim 1, its feature exists: the angle point response function R of angle point amount cim is R=λ
1λ
2-k* (λ
1+ λ
2)
2, in formula: λ
1, λ
2for two quadrature components that Hessian matrix m' obtains after real symmetric matrix diagonalization process, k is coefficient, and span is [0.04,0.06].
4. the microfine adhesion ore particles image partition method based on angle point and curvature measuring according to claim 1, its feature exists: the radius of the level and smooth contour curve that pre-set threshold value thresh is produced by four element values in Gaussian function filtered Hessian matrix m, the variance measure parameter of Gaussian function and supporting domain determines, here Gaussian function scale parameter σ=2.5, the radius in skeletal support territory is 1, then the interval of threshold value is [0.004,0.008].
5. the microfine adhesion ore particles image partition method based on angle point and curvature measuring according to claim 1, its feature exists: if the quantity of concave point is odd number in concave point list of coordinates, then after overmatching, ignores a remaining concave point.
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Cited By (11)
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|---|---|---|---|---|
| CN106447669A (en) * | 2016-04-08 | 2017-02-22 | 潍坊学院 | Circular masking-out area rate determination-based adhesive particle image concave point segmentation method |
| CN106530272A (en) * | 2016-10-09 | 2017-03-22 | 山东师范大学 | Overlapped protein point separation method and device based on concave point matching |
| CN108090434A (en) * | 2017-12-13 | 2018-05-29 | 赣州好朋友科技有限公司 | A kind of ore method for quickly identifying |
| CN108108700A (en) * | 2017-12-27 | 2018-06-01 | 江苏大学 | A kind of characteristic area recognition methods of the pig based on peg conversion |
| CN109047026A (en) * | 2018-08-02 | 2018-12-21 | 重庆科技学院 | A kind of ore screening system and method |
| CN110160528A (en) * | 2019-05-30 | 2019-08-23 | 华中科技大学 | A kind of mobile device pose localization method based on angle character identification |
| CN112598680A (en) * | 2020-12-16 | 2021-04-02 | 北京理工大学 | Image segmentation method and system for cohesive ore based on artificial intelligence network |
| CN113345015A (en) * | 2021-08-05 | 2021-09-03 | 浙江华睿科技股份有限公司 | Package position detection method, device and equipment and readable storage medium |
| CN114152211A (en) * | 2021-01-12 | 2022-03-08 | 中国石油天然气股份有限公司 | Fracturing propping agent roundness measuring method based on microscopic image processing |
| CN116823827A (en) * | 2023-08-29 | 2023-09-29 | 山东德信微粉有限公司 | Ore crushing effect evaluation method based on image processing |
| CN117576135A (en) * | 2023-11-27 | 2024-02-20 | 北京霍里思特科技有限公司 | Method, equipment and storage medium for segmenting ore based on ore image |
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Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447669A (en) * | 2016-04-08 | 2017-02-22 | 潍坊学院 | Circular masking-out area rate determination-based adhesive particle image concave point segmentation method |
| CN106447669B (en) * | 2016-04-08 | 2019-01-25 | 潍坊学院 | The adhesion particle image concave point dividing method differentiated based on round masking-out area ratio |
| CN106530272A (en) * | 2016-10-09 | 2017-03-22 | 山东师范大学 | Overlapped protein point separation method and device based on concave point matching |
| CN106530272B (en) * | 2016-10-09 | 2019-06-28 | 山东师范大学 | Based on the matched overlapping protein spots separation method of concave point and device |
| CN108090434A (en) * | 2017-12-13 | 2018-05-29 | 赣州好朋友科技有限公司 | A kind of ore method for quickly identifying |
| CN108090434B (en) * | 2017-12-13 | 2020-06-23 | 赣州好朋友科技有限公司 | Rapid ore identification method |
| CN108108700B (en) * | 2017-12-27 | 2020-02-21 | 江苏大学 | Pig feature region identification method based on chord axis transformation |
| CN108108700A (en) * | 2017-12-27 | 2018-06-01 | 江苏大学 | A kind of characteristic area recognition methods of the pig based on peg conversion |
| CN109047026A (en) * | 2018-08-02 | 2018-12-21 | 重庆科技学院 | A kind of ore screening system and method |
| CN109047026B (en) * | 2018-08-02 | 2021-08-27 | 重庆科技学院 | Ore screening system and method |
| CN110160528A (en) * | 2019-05-30 | 2019-08-23 | 华中科技大学 | A kind of mobile device pose localization method based on angle character identification |
| CN112598680A (en) * | 2020-12-16 | 2021-04-02 | 北京理工大学 | Image segmentation method and system for cohesive ore based on artificial intelligence network |
| CN112598680B (en) * | 2020-12-16 | 2023-01-24 | 北京理工大学 | Image segmentation method and system of sticky ore based on artificial intelligence network |
| CN114152211A (en) * | 2021-01-12 | 2022-03-08 | 中国石油天然气股份有限公司 | Fracturing propping agent roundness measuring method based on microscopic image processing |
| CN114152211B (en) * | 2021-01-12 | 2024-04-30 | 中国石油天然气股份有限公司 | Microscopic image processing-based roundness measurement method for fracturing propping agent |
| CN113345015A (en) * | 2021-08-05 | 2021-09-03 | 浙江华睿科技股份有限公司 | Package position detection method, device and equipment and readable storage medium |
| CN116823827A (en) * | 2023-08-29 | 2023-09-29 | 山东德信微粉有限公司 | Ore crushing effect evaluation method based on image processing |
| CN116823827B (en) * | 2023-08-29 | 2023-11-10 | 山东德信微粉有限公司 | Ore crushing effect evaluation method based on image processing |
| CN117576135A (en) * | 2023-11-27 | 2024-02-20 | 北京霍里思特科技有限公司 | Method, equipment and storage medium for segmenting ore based on ore image |
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Application publication date: 20150527 |