CN102322820A - Automatic separation method for front and rear surface reflected light spots in surface shape detection system - Google Patents
Automatic separation method for front and rear surface reflected light spots in surface shape detection system Download PDFInfo
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Abstract
The invention discloses an automatic separation method for front and rear surface reflected light spots in a surface shape detection system. The method comprises the following steps of: A1, acquiring a current front and rear surface reflected light spot image F1 by a charge coupled device (CCD); A2, filtering noise by adopting a method combining background subtraction and multiplication filtration, and performing iterative threshold segmentation to obtain a binary image F2; A3, detecting all light spots in the image F2, and extracting the centroid of all the light spots; A4, tracking with a Kalman filter; A5, searching a nearest light spot centroid around the last predicted centroid, outputting the nearest light spot centroid, transmitting all found front surface reflected light spot centroid data to the Kalman filter, and predicting the position of the light spot centroid of the next frame by using the last predicted data and the actual extracted data through the Kalman filter; and A6, if the position is the data of the last frame, ending. The Kalman filter-based tracking algorithm accurately distinguishes the front surface reflected light spots, and the experimental result statistics of multiple times shows that the tracking accuracy is more than 95 percent.
Description
Technical field
The present invention relates to front and rear surfaces flare automatic separation method in a kind of the shape detection system, belong to optical module face shape detection technique and image processing field.
Background technology
In the face shape testing process that adopts based on the angular difference method, detection laser reflects after on CCD, form the front and rear surfaces hot spot after the optical system through the optical module front and rear surfaces.When the optical element front and rear surfaces depth of parallelism was very high, it was very little to make the corresponding front and rear surfaces of same measurement point measure the reflected light angle, on CCD, produces overlapping hot spot.Carry out centroid calculation if regard overlapping hot spot as integral body,, cause surface shape measurement result's error introducing the bigger error of calculation.Simultaneously in the reconstruct of face shape, can only use the front surface hot spot, therefore must the front and back flare be separated automatically, to reject the surperficial hot spot of the back of the body.Therefore, the front and back hot spot automatic separation method that needs the practical application of a kind of ability of research.
When carrying out optical module face shape and detect, behind the L bundle laser incident optics assembly to be detected, after the reflection of optical module front and rear surfaces, can on CCD, form K (the individual front and rear surfaces laser facula of L≤K≤2L), as shown in Figure 1.Can hot spot be divided into 3 types of hot spots such as A, B, C,, realize the extraction and the classification of front surface hot spot in conjunction with the gray feature of front and rear surfaces flare according to criterion.Concrete grammar is following:
(1) manually reject back of the body surface hot spot in first sampled point light spot image, detect the center-of-mass coordinate and the classification of all front surface hot spots in this image, with serve as to classify with reference to hot spot;
(2) since the 2nd sampled point, cut apart containing the image of carrying on the back surperficial hot spot, barycenter and the gray scale that extracts all hot spots with, form the hot spot S set;
(3) find out the light spot group intersection that meets formula C class from the S set, one group that from these combinations, chooses the gray-scale value maximum again as 4 C class front surface hot spots; From the S set, remove these combinations;
(4) if current scan point with reference to the hot spot different rows, utilize formula category-B hot spot the 2nd decision rule, upgrading with reference to facula information is current scan point; Like the colleague, utilize category-B hot spot the 2nd decision rule to differentiate.The light spot group cooperation that will meet this criterion is the candidate, and one group that finds out the gray-scale value maximum as 2 category-B front surface hot spots.From the S set, remove the hot spot combination of finding;
(5) last, utilize and differentiate category-A candidate hot spot, find out the maximum real category-A hot spot of conduct of gray scale;
There is following shortcoming in this method:
(1) needs back surface reflection hot spot and type in first two field picture are manually marked.
(2) separation of hot spot depends on the threshold value in the decision rule before and after;
(3) have overlapping the time just powerless when the front and back hot spot.
The rejecting of general spot backlight all is that the method through physically solves, the most frequently used with effectively carry on the back surfaces coated last layer vaseline at optical module.But optical module debug put on the shelf after, this mode is difficult to carry out because when optical module debug put on the shelf and move after, be not allow vaseline is smeared on the back surface of optical module.
Summary of the invention
Technical matters to be solved by this invention is that the deficiency that is directed against prior art provides front and rear surfaces flare automatic separation method in a kind of the shape detection system.
Front and rear surfaces flare automatic separation method in a kind of the shape detection system may further comprise the steps:
A1:CCD gathers the light spot image F1 of current front and rear surfaces reflection; If first two field picture, then back of the body surface reflection hot spot is wiped in manual work, follows the tracks of for the back and selects correct target;
A2: the method that adopts background subtraction to combine with multiplication filtering is come the filtering noise row iteration Threshold Segmentation of going forward side by side, to isolate laser facula; Obtain bianry image F2;
A3: all hot spots among the detected image F2, if the hot spot adhesion is arranged, then carry out the separation of adhesion hot spot, extract the barycenter of all hot spots;
A4: utilize the Kalman wave filter to follow the tracks of.If first two field picture with facula mass center data initialization Kalman wave filter, and is done prediction for the first time, if first frame changes steps A 1, otherwise, then adopt the Kalman wave filter to follow the tracks of, promptly change steps A 5;
A5: seek nearest facula mass center and output on every side at the last barycenter of once predicting; All front surface reflection facula mass center data that find are passed to the Kalman wave filter, and the last predicted data of Kalman wave filter utilization and this actual amount paid it is predicted the position of next frame facula mass center;
A6: if the last frame data then finish.
Front and rear surfaces flare automatic separation method in described the shape detection system, in the said steps A 3, for the hot spot that adhesion is arranged, the method that its hot spot separates is following:
A31: bianry image F2 is carried out range conversion, obtain image F3, make overlapping hot spot concave point more obvious;
A32: the changing image F3 that adjusts the distance adopts the structural element that increases gradually to carry out the morphology ON operation, calculates image and the difference of original image behind each ON operation, obtains spot radius R;
A33: utilize overlapping differentiation, from F3, extract all overlapping regions and constitute overlapping region bianry image F4;
A34: to each overlapping region among the F4, follow the tracks of the overlapping region profile, extract chain code; According to chain code and radius R, ask for the corresponding centre point of each point, be gathered into the central area of each hot spot;
A35: with the slightly little circular configuration element of radius ratio R F4 is corroded and to obtain F5, and central area image and F5 are carried out intersection operation, depart from spot center centre point far away, obtain center of circle area image F6 thereby delete those;
A36: to zone, each hot spot center of circle, use radius to carry out an expansive working for the circular configuration element bigger slightly than R, make intersection operation with F2 then, its result is just as the shape estimation to this corresponding hot spot of zone, center of circle institute.
The present invention utilizes the kalman track algorithm, in first two field picture of each experiment, will carry on the back surface reflection hot spot people for wiping, and follows the tracks of the hot spot of all front surface reflections, thereby reaches the purpose of classification front and rear surfaces hot spot; For the front and rear surfaces hot spot situation of coincidence is arranged, adopt, realize the separation of overlapping hot spot well based on the overlapping cutting techniques of the hot spot of range conversion; Employing can right area have been told the hot spot of front surface reflection based on the track algorithm of Kalman wave filter, and repeatedly the experimental result statistics shows and follows the tracks of accuracy more than 95%.
Description of drawings
Fig. 1 is a surperficial hot spot separation method synoptic diagram of the prior art;
Fig. 2 is the hot spot separation process that the present invention is based on the Kalman wave filter;
Fig. 3 is the experiment effect figure that the present invention is based on the hot spot separation of Kalman wave filter; 3-1 is first frame, and 3-2 is second frame, and 3-3 is the 4th frame, and 3-4 is the 6th frame, and 3-5 is the 8th frame, and 3-6 is the 12 frame;
The experimental result picture that Fig. 4 carries out overlapping separation for the present invention to three types of laser faculas.
Embodiment
Below in conjunction with specific embodiment, the present invention is elaborated.
Embodiment 1
Front and back flare separation method based on kalman filtering
When carrying out the test of face shape, will carry on the back surface reflection hot spot people for wiping to first two field picture that obtains, follow the tracks of the hot spot of all front surface reflections, thereby reach the purpose of classification front and rear surfaces hot spot.With reference to figure 2, its key step is:
A1:CCD gathers the light spot image F1 of current front and rear surfaces reflection; If first two field picture, then back of the body surface reflection hot spot is wiped in manual work, follows the tracks of for the back and selects correct target;
A2: the method that adopts background subtraction to combine with multiplication filtering is come the filtering noise row iteration Threshold Segmentation of going forward side by side, to isolate laser facula; Obtain bianry image F2;
A3: all hot spots among the detected image F2, if the hot spot adhesion is arranged, then carry out the separation of adhesion hot spot, extract the barycenter of all hot spots.
For the hot spot that adhesion is arranged, the main flow process of its hot spot separation method is following:
A31: bianry image F2 is carried out range conversion (seeing formula 1), obtain image F3, make overlapping hot spot concave point more obvious.
d＝|m-k|+|n-l| (1)
Wherein, (k l) is boundary pixel, and (m n) is area pixel.
A32: the changing image F3 that adjusts the distance adopts the structural element that increases gradually to carry out the morphology ON operation, calculates image and the difference of original image behind each ON operation, obtains spot radius R.
A33: utilize overlapping differentiation (seeing formula 2), from F3, extract all overlapping regions and constitute overlapping region bianry image F4.
Decision rule defines as follows:
P wherein
0Be the overlapping discrimination threshold of hot spot, PE=4 π A/C
2(0＜PE≤1), A is the area of hot spot, C is the girth of hot spot.
A34: to each overlapping region among the F4, follow the tracks of the overlapping region profile, extract chain code.According to chain code and radius R, ask for the corresponding centre point of each point, be gathered into the central area of each hot spot.
A35: with the slightly little circular configuration element of radius ratio R F4 is corroded and to obtain F5, and central area image and F5 are carried out intersection operation, depart from spot center centre point far away, obtain center of circle area image F6 thereby delete those.
A36: to zone, each hot spot center of circle, use radius to carry out an expansive working for the circular configuration element bigger slightly than R, make intersection operation with F2 then, its result is just as the shape estimation to this corresponding hot spot of zone, center of circle institute.
A4: utilize the Kalman wave filter to follow the tracks of.If first two field picture with facula mass center data initialization Kalman wave filter, and is done prediction for the first time, if first frame changes steps A 1, otherwise, then adopt the Kalman wave filter to follow the tracks of, promptly change steps A 5.
A5: seek nearest facula mass center and output (being the hot spot of front surface reflection) on every side at the last barycenter of once predicting; All front surface reflection facula mass center data that find are passed to the Kalman wave filter, and the last predicted data of Kalman wave filter utilization and this actual amount paid it is predicted the position of next frame facula mass center;
A6: if the last frame data then finish;
Embodiment 2
Employing is based on tracking effect such as Fig. 3 of Kalman wave filter; The hot spot of hot spot among Fig. 3 in the rectangle frame for detecting; The front surface reflection hot spot of hot spot in the circular frame for following the tracks of; Can find under the situation that the front and rear surfaces flare occurs, adopt the hot spot of having told front surface reflection based on the track algorithm ability right area of Kalman wave filter, repeatedly the experimental result statistics shows that the tracking accuracy is more than 95%.
Embodiment 3
Overlapping hot spot separating experiment result based on range conversion
In the experiment three types of laser faculas have been carried out overlapping separation: (1) big or small basically identical, overlapping not serious hot spot (Fig. 4-1); (2) the overlapping hot spot that causes not of uniform size (Fig. 4-2); (3) big or small basically identical, overlapping more serious hot spot (Fig. 4-3).Wherein, be followed successively by former figure, segmentation effect figure, center of circle areal map, separating resulting figure from left to right among Fig. 4-1, Fig. 4-2 and Fig. 4-3.Experimental result shows, through method of the present invention, can realize the separation of overlapping hot spot well.
Should be understood that, concerning those of ordinary skills, can improve or conversion, and all these improvement and conversion all should belong to the protection domain of accompanying claims of the present invention according to above-mentioned explanation.
Claims (2)
1. front and rear surfaces flare automatic separation method in the face shape detection system is characterized in that, may further comprise the steps:
A1:CCD gathers the light spot image F1 of current front and rear surfaces reflection; If first two field picture, then back of the body surface reflection hot spot is wiped in manual work, follows the tracks of for the back and selects correct target;
A2: the method that adopts background subtraction to combine with multiplication filtering is come the filtering noise row iteration Threshold Segmentation of going forward side by side, to isolate laser facula; Obtain bianry image F2;
A3: all hot spots among the detected image F2, if the hot spot adhesion is arranged, then carry out the separation of adhesion hot spot, extract the barycenter of all hot spots;
A4: utilize the Kalman wave filter to follow the tracks of; If first two field picture with facula mass center data initialization Kalman wave filter, and is done prediction for the first time, if first frame changes steps A 1, otherwise, then adopt the Kalman wave filter to follow the tracks of, promptly change steps A 5;
A5: seek nearest facula mass center and output on every side at the last barycenter of once predicting; All front surface reflection facula mass center data that find are passed to the Kalman wave filter, and the last predicted data of Kalman wave filter utilization and this actual amount paid it is predicted the position of next frame facula mass center;
A6: if the last frame data then finish.
2. front and rear surfaces flare automatic separation method in according to claim 1 the shape detection system is characterized in that, in the said steps A 3, for the hot spot that adhesion is arranged, the method that its hot spot separates is following:
A31: bianry image F2 is carried out range conversion, obtain image F3, make overlapping hot spot concave point more obvious;
A32: the changing image F3 that adjusts the distance adopts the structural element that increases gradually to carry out the morphology ON operation, calculates image and the difference of original image behind each ON operation, obtains spot radius R;
A33: utilize overlapping differentiation, from F3, extract all overlapping regions and constitute overlapping region bianry image F4;
A34: to each overlapping region among the F4, follow the tracks of the overlapping region profile, extract chain code; According to chain code and radius R, ask for the corresponding centre point of each point, be gathered into the central area of each hot spot;
A35: with the slightly little circular configuration element of radius ratio R F4 is corroded and to obtain F5, and central area image and F5 are carried out intersection operation, depart from spot center centre point far away, obtain center of circle area image F6 thereby delete those;
A36: to zone, each hot spot center of circle, use radius to carry out an expansive working for the circular configuration element bigger slightly than R, make intersection operation with F2 then, its result is just as the shape estimation to this corresponding hot spot of zone, center of circle institute.
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| CN104019764A (en) * | 2014-06-20 | 2014-09-03 | 中国工程物理研究院激光聚变研究中心 | Calibration method for scanning type surface shape measurement optical system of 2*2 array light source |
| CN107452026A (en) * | 2017-08-08 | 2017-12-08 | 何佳芮 | A kind of processing system and its method for image spot barycenter |
| CN108981585A (en) * | 2017-06-01 | 2018-12-11 | 上海砺晟光电技术有限公司 | It can accurately measure the laser displacement sensor of curved surface displacement of targets |
| US10375645B2 (en) | 2010-12-27 | 2019-08-06 | Microsoft Technology Licensing, Llc | Power management via coordination and selective operation of timer-related tasks |
| CN110766700A (en) * | 2019-10-23 | 2020-02-07 | 吉林大学 | ICP-AES spectral image processing method based on digital micromirror |
| CN112150545A (en) * | 2020-09-28 | 2020-12-29 | 中国科学院空间应用工程与技术中心 | Light spot centroid obtaining method and device based on Tobit Kalman filter |
| CN112634304A (en) * | 2020-12-31 | 2021-04-09 | 上海易维视科技有限公司 | Method for removing reflection light spots in 3D format video or image |
| CN113155756A (en) * | 2021-03-31 | 2021-07-23 | 中国科学院长春光学精密机械与物理研究所 | Light spot online calibration method |
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| US10375645B2 (en) | 2010-12-27 | 2019-08-06 | Microsoft Technology Licensing, Llc | Power management via coordination and selective operation of timer-related tasks |
| CN104019764A (en) * | 2014-06-20 | 2014-09-03 | 中国工程物理研究院激光聚变研究中心 | Calibration method for scanning type surface shape measurement optical system of 2*2 array light source |
| CN104019764B (en) * | 2014-06-20 | 2016-08-24 | 中国工程物理研究院激光聚变研究中心 | 2 × 2 array light source scan-type surface shape measurement optical system scaling methods |
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| CN107452026A (en) * | 2017-08-08 | 2017-12-08 | 何佳芮 | A kind of processing system and its method for image spot barycenter |
| CN110766700A (en) * | 2019-10-23 | 2020-02-07 | 吉林大学 | ICP-AES spectral image processing method based on digital micromirror |
| CN110766700B (en) * | 2019-10-23 | 2022-01-28 | 吉林大学 | ICP-AES spectral image processing method based on digital micromirror |
| CN112150545A (en) * | 2020-09-28 | 2020-12-29 | 中国科学院空间应用工程与技术中心 | Light spot centroid obtaining method and device based on Tobit Kalman filter |
| CN112634304A (en) * | 2020-12-31 | 2021-04-09 | 上海易维视科技有限公司 | Method for removing reflection light spots in 3D format video or image |
| CN112634304B (en) * | 2020-12-31 | 2022-09-13 | 上海易维视科技有限公司 | Method for removing reflection light spots in 3D format video or image |
| CN113155756A (en) * | 2021-03-31 | 2021-07-23 | 中国科学院长春光学精密机械与物理研究所 | Light spot online calibration method |
| CN113155756B (en) * | 2021-03-31 | 2022-10-04 | 中国科学院长春光学精密机械与物理研究所 | Light spot online calibration method |
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