CN102637299A - Method of using local extremum clustering to count light emitting diodes - Google Patents
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Abstract
The invention belongs to the technical field of machine vision, and particularly relates to a method of using local extremum clustering to count light emitting diodes. The method includes the steps: subjecting LED chip images shot by an industrial digital camera to mean value filtering and gauss filtering to remove image noise; solving a local minimum value point to determine an approximate area of light emitting diodes according to grayscale difference between a light emitting diode area and background color; setting up a data sector for each local extreme points and surrounding gray values thereof, and performing k-means mean value clustering according to Euler distance of the vectors to extract a local extreme cluster of the light emitting diodes; and finally, as for monopole light emitting diode chips, defining the number of light emitting diode local extreme points as the number of the light emitting diodes, and as for rectangular or parallelogram double-pole light emitting diode chips, determining a light emitting diode rule according to two pole matching to determine the number of the light emitting diodes. According to tests, the method is accurate in counting, low in time consumption and high in robustness.
Description
Technical field
The invention belongs to the machine vision technique field, be specifically related to a kind of method that light emitting diode is counted.
Background technology
Light emitting diode (LED) is because the life-span is long, low power consumption and other advantages is widely used in fields such as indication, demonstration.The improving constantly of technical breakthrough of white light LEDs and single led luminous intensity makes LED be applied to lighting field becomes possible [1].In the finished product test flow process of led chip (side's of being referred to as sheet in the market) manufacture craft, relate to the problem that LED quantity is measured.But because the artificial counting counting is slow, precision is low, labour intensity is big; And sensor counting equipment cost is high; Safeguard and be difficult for; Therefore, how to guarantee under the prerequisite that cost is low, response speed fast, counting is accurate, robustness is high that designing a kind of LED method of counting is the difficult problem that the industry urgent need solves.
The LED method of counting of Flame Image Process is that the utilization image technique is handled the figure that takes a crane shot of LED side's sheet, utilizes contactless method to obtain the quantity of LED, and the general static measurement result is more stable, is present industry method of counting relatively more commonly used.Existing image LED method of counting has Blob to analyze and sub-pixel positioning algorithm [2] graph outline recognizer [3], sub-pixel precision partitioning algorithm [4] etc.Though existing image LED method of counting has been obtained certain achievement, it is unstable to take LED side's sheet vertical view recognition effect through the industrial digital camera, has large stretch of connected region or deformity zone in the LED image of intensive stacking; For identification and the counting of LED brings difficulty; Cause present image LED method of counting efficiency of algorithm low, the processing time is long, and is not ideal to second-rate treatment of picture; And in practical application; Depend on very much light source, algorithm receives illumination effect bigger, therefore press for that a kind of response speed is fast, counting accurately, the higher LED number system of robustness.Use a computer LED side's picture is handled and counted, this method can be increased work efficiency greatly, improves accuracy, has certain practical value and application prospect.
Summary of the invention
The objective of the invention is to propose the method for counting of the light emitting diode that a kind of cost is low, counting is accurate, consuming time less, robustness is high.
The method of counting of the light emitting diode that the present invention proposes is a kind of new method of utilizing the local extremum cluster, can automatic gauge LED side sheet on the quantity of light emitting diode.These method concrete steps are following:
(1) picture for the led chip of taking with industrial digital camera carries out the image pre-service, specifically is to handle through mean filter, gaussian filtering, removes the image noise, makes that image blurring part is more clear;
(2) to through above-mentioned pretreated image, ask local minizing point's (being edge gray scale sudden change black region point), the label of establishing this local minizing point does<i >i</i>, and this extreme value volume size is labeled as<img file="47596DEST_PATH_IMAGE001.GIF" he="24" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="78" />, differ if exist with its gray-scale value around this minimum point<the point of C, C is a specific threshold here, then<img file="2914DEST_PATH_IMAGE002.GIF" he="24" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="88" />After calculating fully, ask all extreme value volumes<img file="90956DEST_PATH_IMAGE003.GIF" he="24" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="56" />Average extreme value area size<img file="357989DEST_PATH_IMAGE004.GIF" he="21" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="52" />Removal All Ranges size<img file="963414DEST_PATH_IMAGE005.GIF" he="24" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="138" />The local minizing point of (α is certain factor of 0～1); Generalized case, C gets 0.05～0.15 times of ALMC, and α gets 0.85～0.95, and occurrence can be confirmed according to repeatedly testing the best parameter of effect;
(3) to each local minizing point, set up a data vector with the gray value information around it, do the k-means mean cluster according to Euler's distance of vector, with the divide into several classes of these local minizing points, extract light emitting diode bright spot local extremum class;
(4) divide two types of situation to handle then: for single-stage light-emitting diodes tube side sheet, counting out of light emitting diode bright spot local minimum class is the number of light emitting diode; For rectangle or parallelogram double pole light emitting diode side sheet,, confirm the number of light emitting diode according to the principle of the definite light emitting diode of two-stage coupling.
further describe in the face of the particular content of each step down.
In the step (1): mean filter and gaussian filtering
Reasons such as under-exposure or time exposure can produce the ripple of making an uproar in a large number during owing to photograph, cause to occur some isolated bright spots in the photograph image or cause profile unintelligible.These phenomenons will be disturbed the correct counting of light emitting diode, therefore before the light emitting diode counting, need carry out denoising to picture, can adopt mean filter and gaussian filtering usually.
In the step (2): the sizeable local minizing point of computed volume
The form of expression of light emitting diode in image is exactly the black region of block edge gray scale sudden change.Because black region and background colour have gray scale difference once, so the minimum point of only requiring locally, just can obtain the general center of respective leds black region.According to these characteristics, the present invention proposes through asking the local extremum zone to come the preliminary method of confirming led area.
Each point
in the scanning LED side picture; If
is to be the minimum value in the subimage zone of size for
at center with
, ask the volume size algorithm of this point following:
(1) establishes the initialization S set for empty.
joins in the S set with point, gives
mark
.Wherein
representes that point
is untreated, and
expression point
was handled.
(2) get any untreated point in the S set
, if
8-field point in exist
(
p=i-1, i, i+1;
q=j-1, j j+1), satisfies
And
(C is some specific thresholds) is then with point
Add S set.
is labeled as 0.
(3) repeating step (2), each element all passes through processing in S set.Number of elements in the set S is the point
volume size parameters:?
.
(4) obtain all local minizing point and volume size thereof after, ask the average volume size parameter of all local minizing points:
.Scan all local minizing points, remove the local minizing point of volume size
(α is certain factor of 0～1).
After obtaining the area size of all local minizing points and this point, ask the average area size
of being had a few.Scan all local minizing points, remove the local minizing point of area size
(α is certain factor of 0～1).
In the step (3): the K-Means mean cluster
The black region of each edge gray scale sudden change is all corresponding to a Local Extremum.When average local extremum area size hour, Local Extremum increases, even carried out the volume denoising, still has the undesired signal point.Simultaneously, when having the bigger interference region of some and background colour aberration in the picture, also can introduce noise, disturb the definite of correct localized target extreme point.Disturb noise and light emitting diode cluster point in order more effectively to distinguish, the present invention adopts the K-means means clustering algorithm that extreme point is carried out clustering processing.
In the present invention; To each local minizing point
of trying to achieve, generate an eigenvector te according to the Pixel Information around it.Eigenvector te is made up of 5 components, representes as follows:
: establishing with
is the average gray value
of the size at center for the subimage of
(wherein
);
; Wherein, β is certain normalized factor of 0～1; Generalized case, β gets 0.8;
The distance of setting between two local minizing points is the Euler's distance between each eigenvector.Therefore K-Means means clustering algorithm [5] is as follows:
(1) eigenvector to each local minizing point
renumbers; If n local minizing point arranged; Then its characteristic of correspondence vector is
, and wherein
is the vector of one 5 component arbitrarily;
(2) select k Initialization Center point, for example
;
(3) for
; Respectively relatively with
; Suppose that Euler with
apart from minimum, just is labeled as i;
(4) be labeled as the point
of i for all; Calculate these points with
; And statistics is labeled as the number
of the point of i, recomputates
;
(5) repeating step (3), step (4), up to all
value variation less than given threshold values.
Different characteristic according to extreme point obtains cluster result, keeps the Local Extremum monoid that the light emitting diode bright spot produces, and is the target data point group.
In the step (4): confirm the light emitting diode number
To the local minizing point of having tried to achieve light emitting diode cluster point in the step (3), for single-stage light-emitting diodes tube side sheet, the number of this Local Extremum is exactly the number of light emitting diode; For rectangle or parallelogram double pole light emitting diode side sheet, because the direction of two-stage in picture of each light emitting diode is consistent, extreme point is mated according to this characteristic, try to achieve the light emitting diode number.
The present invention is directed to the LED counting; By mean filter, gaussian filtering figures disposal route; Remove some image noises, make that image blurring part is more clear, extract the Local Extremum (LED bright spot and noise spot) of image then; Carry out mean cluster again and extract the LED bright spot, thereby reach the purpose that LED counts.Experimental result shows that method cost of the present invention is low, counting is accurate, consuming time less, robustness is high, can effectively be used for the quantity of LED on the automatic gauge LED side sheet.
Description of drawings
Fig. 1 is three kinds of typical light-emitting diodes tube side sheet samples.Wherein, a is a single-stage light-emitting diodes tube side sheet, and b is that luminous two c of parallelogram are rectangle light-emitting diodes tube side sheets.
Fig. 2 is an experimental result.Wherein, a is a single-stage light-emitting diodes tube side sheet, and b is a parallelogram light-emitting diodes tube side sheet, and c is a rectangle light-emitting diodes tube side sheet.Red point is represented the bright spot of light emitting diode behind the Local Extremum clustering algorithm among a, and yellow short line segment is represented the matching result of bipolar diode among b, the c.
Fig. 3 is local clustering method process flow diagram.
Embodiment
Specific algorithm of the present invention is following:
Local extremum clustering algorithm (LMC)
Input:Light emitting diode image img.
1. read the data among the img and be kept among the two-dimensional array I: QImage image (img); I [i] [j]=(int) qGray (image.pixel (j, i));
2. mean filter and gaussian filtering denoising: J=midfilt (I, 3); K=gaussfilt (1.6,9, J); Wherein, midfilt, gaussfilt represent mean filter and gaussian filtering function respectively;
3. calculate local minizing point: preserve two-dimensional array K for view data, the local minimum point calculating method is following:
For the data K in each image (i, j),
If (i j) was not visited, then K
Relatively K (i, j) be that the center size is the size of the gray-scale value of each pixel in the zone of N*N (N be certain fixed value) with it, (i j) is minimum value, and (i j) is local minizing point to then definite K as if K.
Up to all data K (i, j) processing finishes;
4. to each local minizing point, set up data vector:
To the K of local minizing point (i j), sets up data vector te, and method is following:
Int k=N/2; Wherein, N is described certain fixed value of step 3, generally gets the distance size between two adjacent local minizing points.
Double avg0=mean (K, i-k, i+k, j-k, j+k); Wherein, and mean (I, is, ie, js, je) function is used to calculate array I horizontal ordinate and begins the ie end from is, and ordinate begins the je end region from js, that is the mean pixel size in I [is..ie] [js..je] zone.
Double te [5]; Wherein, te representes K (i, data vector j).
te[0]?=?avg0;
te[1]?=mean(K,i-k,i-k,j-k,j-k)?–?avg0;
te[2]?=mean(K,i-k,i-k,j+k,j+k)?–?avg0;
te[3]?=mean(K,i+k,i+k,j-k,j-k)?–?avg0;
te[4]?=mean(K,i+k,i+k,j+k,j+k)?–?avg0;
5. Local Extremum is carried out the K-Means mean cluster: int * c=k_means (te, size, dim, feilei, 1e-4,0); Wherein, k_means is a mean cluster function, and te is the data vector of Local Extremum, and size is the number of Local Extremum, and dim representes cycle index, and feilei representes the cluster species number, and output c representes the classification designator of each Local Extremum.Root
The cluster kind of light emitting diode is confirmed according to the value of c in
.
Output:The cluster kind of light emitting diode behind the K-Means mean cluster.
The LMC algorithm has been tried to achieve the Local Extremum of light emitting diode cluster point, and for single-stage light-emitting diodes tube side sheet, the number of this Local Extremum is exactly the number of light emitting diode; For rectangle or parallelogram double pole light emitting diode side sheet, because the direction of two-stage in picture of each light emitting diode is consistent, extreme point is mated according to this characteristic, try to achieve the light emitting diode number.
List of references
[1]Steigerwald?D.A,?Bhat?J.C,?Collins?D,?et?a1.?Illumination?with?Solid?State?Lighting?Technology[J].?IEEE?Journal?of?Selected?TopiCS?in?Quantum?Electronics,?2002,8(2):310-320.
[2] Ma Yonghua, Geng Ruifang opens a handle of the Big Dipper. based on the research [J] of the machine vision image-recognizing method of Blob algorithm. and instrument and meter user, 2008,15 (4): 1-2.
[3] land. based on the graph outline automatic identification technology research [J] of Flame Image Process. Chang An University's journal (building and environmental science version) .2004, (3): 73-78.
[4] Zhang Haining, Zhang Biwei. the crystal grain image-recognizing method of cutting apart based on sub-pixel precision [J]. electronic design engineering .2011,19 (4): 113-116.
[5]?Fahim?A.M,?Salem?A.M,?Torkey?F.A,?Ramadan?M.A,?et?al.?An?efficient?enhanced?k-means?clustering?algorithm[J].?2006,7(10):1626-1633。
Claims (3)
1. method of utilizing the local extremum cluster that light emitting diode is counted is characterized in that concrete steps are following:
(1) picture for the led chip of taking with industrial digital camera carries out the image pre-service, and mean filter, gaussian filtering are adopted in the image pre-service, removes the image noise, makes that image blurring part is more clear;
(2) to through above-mentioned pretreated image, ask local minizing point, the label of establishing this local minizing point does<i >i</i>, and this extreme value volume size is labeled as<img file="2012100737447100001DEST_PATH_IMAGE001.GIF" he="24" id="ifm0001" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="78" />, differ if exist with its gray-scale value around this minimum point<the point of C, then<img file="84342DEST_PATH_IMAGE002.GIF" he="24" id="ifm0002" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="88" />After calculating fully, ask all extreme value volumes<img file="2012100737447100001DEST_PATH_IMAGE003.GIF" he="24" id="ifm0003" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="56" />Average extreme value area size<img file="566139DEST_PATH_IMAGE004.GIF" he="21" id="ifm0004" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="52" />Removal All Ranges size<img file="2012100737447100001DEST_PATH_IMAGE005.GIF" he="24" id="ifm0005" img-content="drawing" img-format="GIF" inline="no" orientation="portrait" wi="138" />Local minizing point; Here C is a specific threshold, and C gets 0.05～0.15 times of ALMC, and α is 0～1 a factor, gets 0.85～0.95;
(3) to each local minizing point, set up a data vector with the gray value information around it, do the k-means mean cluster according to Euler's distance of vector, with the divide into several classes of these local minizing points, extract light emitting diode bright spot local extremum class;
(4) divide two types of situation to handle then: for single-stage light-emitting diodes tube side sheet, counting out of light emitting diode bright spot local minimum class is the number of light emitting diode; For rectangle or parallelogram double pole light emitting diode side sheet,, confirm the number of light emitting diode according to the principle of the definite light emitting diode of two-stage coupling.
2. the method for utilizing the local extremum cluster that light emitting diode is counted according to claim 1 is characterized in that the step of calculating the suitable local minizing point of volume size in the step (2) is:
Each point
in the scanning LED side picture; If
is to be the minimum value in the subimage zone of size for
at center with
, ask the volume size algorithm of this point following:
(1) establishes the initialization S set for empty;
joins in the S set with point; Give
mark
; Wherein
representes that point
is untreated, and
expression point
was handled;
(2) get any untreated point in the S set
, if
8-field point in exist
(
p=i-1, i, i+1;
q=j-1, j j+1), satisfies
And
, then with point
Add S set; Will
Be labeled as 0;
(3) repeating step (2), each element all passes through processing in S set; The element number of S set is exactly the volume size parameter of point
: LMC;
(4) obtain all local minizing point and volume size thereof after; Ask the average volume size parameter of all local minizing points: ALMC; Scan all local minizing points, remove the local minizing point of volume size
;
After obtaining the area size of all local minizing points and this point; Ask the average area size
of being had a few; Scan all local minizing points, remove the local minizing point of area size
.
3. the method for utilizing the local extremum cluster that light emitting diode is counted according to claim 1 is characterized in that:
The step of K-Means mean cluster is in the step (3):
To each local minizing point
of trying to achieve; Generate an eigenvector te according to the Pixel Information around it; Eigenvector te is made up of 5 components, representes as follows:
: the average gray value
of establishing with
subimage of size that is the center for
;
; Wherein,
, β are certain normalized factor of 0～1;
: establish with
be the center size for the subimage of
average gray value is
,
;
: establish with
be the center size for the subimage of
average gray is
,
;
The distance of setting between two local minizing points is the Euler's distance between each eigenvector, and the K-Means means clustering algorithm is following:
(1) eigenvector to each local minizing point
renumbers; If n local minizing point arranged; Then its characteristic of correspondence vector is
, and wherein
is the vector of one 5 component arbitrarily;
(2) select k Initialization Center point,
;
(3) for
; Respectively relatively with
; Suppose that Euler with
apart from minimum, just is labeled as i;
(4) be labeled as the point
of i for all; Calculate these points with
; And statistics is labeled as the number
of the point of i, recomputates
;
(5) repeating step (3), step (4), up to all
value variation less than given threshold values;
Different characteristic according to extreme point obtains cluster result, keeps the Local Extremum monoid that the light emitting diode bright spot produces, and is the target data point group.
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| CN105575844A (en) * | 2016-01-29 | 2016-05-11 | 华灿光电(苏州）有限公司 | Counting method and counting device for LED chips |
| CN107316293A (en) * | 2017-06-22 | 2017-11-03 | 深圳市灵星雨科技开发有限公司 | A kind of LED blurred pictures identification determination methods and its system |
| CN112833773A (en) * | 2021-01-13 | 2021-05-25 | 无锡卡尔曼导航技术有限公司 | High-precision real-time mu counting method for operation |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105575844A (en) * | 2016-01-29 | 2016-05-11 | 华灿光电(苏州）有限公司 | Counting method and counting device for LED chips |
| CN105575844B (en) * | 2016-01-29 | 2018-06-26 | 华灿光电(苏州）有限公司 | The method of counting and device of a kind of light-emitting diode chip for backlight unit |
| CN107316293A (en) * | 2017-06-22 | 2017-11-03 | 深圳市灵星雨科技开发有限公司 | A kind of LED blurred pictures identification determination methods and its system |
| CN107316293B (en) * | 2017-06-22 | 2020-07-03 | 深圳市灵星雨科技开发有限公司 | LED fuzzy picture identification and judgment method and system |
| CN112833773A (en) * | 2021-01-13 | 2021-05-25 | 无锡卡尔曼导航技术有限公司 | High-precision real-time mu counting method for operation |
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