CN104732900A - Pixel defect detection method and device - Google Patents
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- CN104732900A CN104732900A CN201310713365.4A CN201310713365A CN104732900A CN 104732900 A CN104732900 A CN 104732900A CN 201310713365 A CN201310713365 A CN 201310713365A CN 104732900 A CN104732900 A CN 104732900A
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Abstract
The invention provides a pixel defect detection method which includes the following steps that video scanning is conducted on a base plate to be detected; an video frame image meeting a preset condition is extracted; registration is conducted on the video frame image and a reference image to obtain a registration image; the registration image is compared with the reference image and analyzed to determine defect pixels. According to the pixel defect detection method, through video shooting and extracting of the frame image meeting the preset condition, the detection speed can be increased compared with ordinary camera photographing, registration is conducted on the video frame image and the reference image, and fast detection of the defect of the base plate to be detected can be achieved without high-precision physical alignment and positioning mobile scanning. The invention further provides a pixel defect detection device.
Description
Technical field
The present invention relates to defect detecting technique, particularly relate to a kind of picture element flaw detection method and device.
Background technology
Organic light emitting display (Organic Light-Emitting Diode, OLED) be active illuminating display device, compare Thin Film Transistor-LCD (the Thin Film Transistor Liquid CrystalDisplay of current main flow, TFT-LCD), it has high-contrast, wide viewing angle, low-power consumption, fast response time, the advantages such as volume is thinner, are expected to the flat panel display of future generation become after LCD.
In the manufacture process of OLED, can cause various bad because of the problem of technique, the homogeneity of its picture display brightness is difficult to be guaranteed, and how promoting yield just becomes the problem be concerned about very much each manufacture commercial city.First, must detect bad, the root finding out bad generation because of, then just can carry out technologic improvement targetedly and promote yield.On traditional streamline, the automatic optical detection device that adopts detects some in technique are bad more, and it needs high-precision physical alignment, because deviation of the alignment directly can affect the Detection results of pixel scale.And limit the threshold value setting of defects detection, if threshold value arranges too small, high pixel comparison precision may cause large-area flase drop, so select suitable threshold value more difficult, and easily occurs undetected.Its high-precision location motion scan and complicated image capturing system limit its application scenario simultaneously.
Summary of the invention
Based on this, be necessary that proposition is a kind of without the need to high precision physical alignment and method for quick and the device of locating motion scan.
A kind of picture element flaw detection method, comprises the steps:
Videoscanning is carried out to substrate to be measured;
Extract and meet pre-conditioned frame of video picture;
Described frame of video picture and reference picture are carried out registration and obtains registration picture;
Described registration picture and described reference picture are analyzed and determine defect pixel.
In an embodiment wherein, described described registration picture and described reference picture being analyzed determines that the step of defect pixel comprises:
The sample area in described registration picture is determined according to the sampled point in described reference picture;
Corresponding region in described sample area and described reference picture is contrasted, judges whether there is described defect pixel in described sample area;
Determine the described defect pixel of described sample area.
In an embodiment wherein, described substrate to be measured is carried out to the step of videoscanning before, also comprise the step inserting corresponding filter.
In an embodiment wherein, described to substrate to be measured carry out videoscanning be specially repeatedly use the filter of different wave length to repeat videoscanning is carried out to described substrate to be measured.
In an embodiment wherein, also comprise the curve of spectrum obtaining described sample area pixel, then judge the position of abnormity point according to the consistance of the described curve of spectrum.
In an embodiment wherein, the difference of the pixel grey scale of the corresponding region in the pixel grey scale of described sample area and described reference picture is greater than the pixel of described predetermined threshold value as defect pixel.
In an embodiment wherein, described extraction meets pre-conditioned frame of video picture and is specially the frame of video picture of the degree of correlation between extracted at equal intervals picture in preset range.
In an embodiment wherein, described pattern of carrying out videoscanning to substrate to be measured is substrate Move Mode or camera Move Mode.
In an embodiment wherein, at the uniform velocity move described substrate to be measured by S type path and realize carrying out videoscanning to described substrate to be measured.
In an embodiment wherein, describedly described frame of video picture and reference picture are carried out the step that registration obtains registration picture and comprise:
Calculate the degree of correlation between described frame of video picture and described reference picture;
Described frame of video picture corresponding for described degree of correlation maximal value and described reference picture appropriate section are mapped as registration picture.
In an embodiment wherein, the described registration that described frame of video picture and reference picture carried out obtains registration picture and comprises:
Obtain a pair unique point between described frame of video picture and described reference picture;
Obtain according to described unique point coordinate
In d
1and d
2, wherein
Represent the coordinate (x, y) of described frame of video picture and described reference picture coordinate (x
0, y
0) between affine transformation relationship, wherein a
1, b
1, a
2and b
2all equal 1;
According to formula
Described frame of video picture is carried out corresponding translation and aims at as registration picture with described reference picture.
A kind of picture element flaw pick-up unit, comprising:
Videoscanning unit, for carrying out videoscanning to substrate to be measured;
Picture extracting unit, meets pre-conditioned frame of video picture for extracting;
Registration unit, obtains registration picture for described frame of video picture and reference picture are carried out registration;
Defect pixel determining unit, determines defect pixel for described registration picture and described reference picture being analyzed.
In an embodiment wherein, described defect pixel determining unit comprises:
Sample area determining unit, for determining the sample area in described registration picture according to the sampled point in described reference picture;
Contrast unit, for being contrasted the corresponding region in described sample area and described reference picture, judges whether there is described defect pixel in described sample area;
Defect pixel acquiring unit, for obtaining the described defect pixel of described sample area.
In an embodiment wherein, before videoscanning unit carries out videoscanning to substrate to be measured, insert corresponding filter.
In an embodiment wherein, what to described substrate to be measured, videoscanning unit carried out that videoscanning is specially that videoscanning unit repeatedly uses the filter of different wave length to repeat carries out videoscanning to described substrate to be measured.
In an embodiment wherein, also comprise consistance judging unit, described consistance judging unit, for obtaining the curve of spectrum of described sample area pixel, then judges the position of abnormity point according to the consistance of the described curve of spectrum.
In an embodiment wherein, the difference of the pixel grey scale of the corresponding region in the pixel grey scale of described sample area and described reference picture is greater than the pixel of described predetermined threshold value as defect pixel.
In an embodiment wherein, described extraction meets pre-conditioned frame of video picture and is specially the frame of video picture of the degree of correlation between extracted at equal intervals picture in preset range.
In an embodiment wherein, described pattern of carrying out videoscanning to substrate to be measured is substrate Move Mode or camera Move Mode.
In an embodiment wherein, at the uniform velocity move described substrate to be measured by S type path and realize carrying out videoscanning to described substrate to be measured.
In an embodiment wherein, described registration unit comprises:
Correlation calculating unit, for calculating the degree of correlation between described frame of video picture and described reference picture;
First picture registration unit, for being mapped described frame of video picture corresponding for described degree of correlation maximal value and described reference picture appropriate section as registration picture.
In an embodiment wherein, described registration unit comprises:
Unique point acquiring unit, for obtaining a pair unique point between described frame of video picture and described reference picture;
Parametric solution unit, for obtaining according to described unique point coordinate
In d
1and d
2, wherein
Represent the coordinate (x, y) of described frame of video picture and described reference picture coordinate (x
0, y
0) between affine transformation relationship, wherein a
1, b
1, a
2and b
2all equal 1;
Second picture registration unit, for according to formula
Described frame of video picture is carried out corresponding translation and aims at as registration picture with described reference picture.
Above-mentioned picture element flaw detection method and device are by video capture and extraction meets pre-conditioned frame picture, take pictures relative to common camera and can improve the speed of detection, the quick detection to base board defect to be measured when can be implemented in without high precision physical alignment by carrying out registration to frame of video picture and reference picture and locate motion scan.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the picture element flaw detection method in an embodiment;
Fig. 2 is the structural representation of the Systems for optical inspection in an embodiment;
Fig. 3 is the schematic diagram that the camera in an embodiment scans substrate to be measured;
Frame of video picture and reference picture are carried out the process flow diagram that registration obtains registration picture in an embodiment by Fig. 4;
Fig. 5 be in another embodiment frame of video picture and reference picture are carried out the process flow diagram that registration obtains registration picture;
Fig. 6 a to Fig. 6 d is the schematic diagram obtaining frame of video picture in an embodiment and carry out registration;
Fig. 7 is the schematic diagram of frame of video picture in an embodiment and reference picture registration;
Described registration picture and described reference picture are being analyzed the process flow diagram determining defect pixel in Fig. 1 by Fig. 8;
Fig. 9 be in an embodiment to the pixel grey scale comparative analysis schematic diagram chosen;
Figure 10 is the spectral analysis schematic diagram to the pixel chosen in an embodiment;
Figure 11 is the structural representation of the picture element flaw pick-up unit in an embodiment;
Figure 12 is the structural representation of the registration unit in an embodiment;
Figure 13 is the structural representation of the registration unit in another embodiment;
Figure 14 is the structural representation of the defect pixel determining unit in Figure 11.
Embodiment
In one embodiment, as shown in Figure 1, a kind of picture element flaw detection method, comprises the steps:
S110, inserts corresponding filter.
As shown in Figure 2, filter 50 can carry out filtering with the consistance of test material.The consistance of material affects product quality key factor.When the individual components of material changes that even material type changes, from often None-identified in appearance.After filter 50 filtering, reference spectra curve is set up in spectral analysis passing material being carried out to multi-feature wavelength, then just can the consistance of the aspect such as test material, technique by the curve of spectrum of detected materials and reference spectra curve comparison.
If spectral detection will be carried out for the process materials consistency problem of substrate 20 to be measured, so before camera 60, add filter 50.Then by fixing on a mobile platform 10 for substrate 20 to be measured.Micro optical system 40 is regulated to focus on the layer to be detected of substrate 20 to be measured.Light source 30 is broad spectrum light source, and light source 30 provides illumination for giving micro optical system 40.Filter plate or the tunable optic filter of what filter 50 was concrete can the be characteristic wavelength added in imaging systems, filter 50 is for carrying out multispectral detection to substrate 20 surface to be measured.The multispectral process analyzed that detects will be described in more detail below, if do not need the consistency analysis carrying out material, also can omit the step inserting corresponding filter.
S120, carries out videoscanning to substrate to be measured.
After camera 60 focuses on, the reference position of the scope determination camera video scanning detected as required and path, can adopt camera Move Mode or substrate Move Mode.For the ease of the switching of camera 60 between macro check and micro examination (adding microexamination), substrate Move Mode can be adopted.In a specific embodiment, as shown in Figure 3, at the uniform velocity move by S type path the videoscanning that substrate 20 to be measured realizes substrate to be measured.
If spectral detection will be carried out for the process materials consistency problem of substrate 20 to be measured, carry out videoscanning to substrate 20 to be measured to be specially: the light intensity of light source 30 is adjusted to unanimously, adopt the filter 50 of corresponding different wave length, each rear movement substrate 20 to be measured repeated of the filter 50 of different wave length that uses carries out videoscanning.
S130, extracts and meets pre-conditioned frame of video picture.
Can process the video data of shooting after video capture.By using video mode to take substrate 20 to be measured, the speed of detection greatly can be improved.
The frame number of a lot of video capture devices collection per second and the resolution of every frame picture are inversely proportional to.For the object of high-speed mobile, in order to see the details of object movement clearly, the temporal resolution of video just very important.Present high speed camera, a lot of per secondly can take 1,000,000 frames, to tackle the needs of high-speed capture.In the present embodiment, should be the resolution more focusing on frame of video picture, even can be less than 24 frames per second as frame number per second, because should be enough relative to the rate travel of substrate 20 to be measured.In addition, the precision that high-resolution picture can strengthen detection is obtained.The frame number that the video of concrete shooting is per second should be determined according to the speed of scanning.After video acquisition completes, video is converted into frame of video picture, then only needs to process frame of video picture.
Extraction meets pre-conditioned frame of video picture and is specially the frame of video picture of the degree of correlation between extracted at equal intervals picture in preset range.In a specific embodiment, the degree of correlation between extracted at equal intervals picture comprises in the method for the frame of video picture of preset range: first calculate the degree of correlation between picture, then the degree of correlation between picture and preset range are contrasted, select the interval of satisfactory frame of video picture.In the present embodiment, preset range is choose the little picture of the degree of correlation, and this preset range can reduce the repeating part between the photograph of front and back, decreases unnecessary repetitive operation, indirectly improves the speed of scanning.If but the degree of correlation between two width pictures arranges too small, the pixel of a lot of scanning will be omitted between so adjacent frame of video picture, a lot of pixels can be caused undetected, the error rate of detection can be increased like this.Therefore, the preset range of the degree of correlation needs to set as required.
The computing formula of the degree of correlation between concrete picture is C (x, y)=corr (X (x1, y1), Y (x2, y2)), wherein X (x1, y1) be that the first width picture meta is set to (x1, y1) gray scale of the pixel at place, Y (x2, y2) is that the second width picture meta is set to (x2, the gray scale of pixel y2), C (x, y) is the degree of correlation between two width pictures when shift position is (x, y).The similarity of the content of two width pictures is large, and so the degree of correlation of this two width picture is just large.If the similarity of the content of two width pictures is little, so the degree of correlation of this two width picture is just little.
S140, carries out registration by frame of video picture and reference picture and obtains registration picture.
If need to process the frame of video picture of conversion, these frame of video pictures must be carried out registration.Find out the position of the pixel in frame of video picture, then error analysis and material, process consistency analysis are carried out to relevant pixel.
In a specific embodiment, as shown in Figure 4, frame of video picture and reference picture are carried out registration to obtain registration picture and comprise:
S141, calculates the degree of correlation between frame of video picture and reference picture.
The degree of correlation between using in above-mentioned formulae discovery frame of video picture and reference picture.The computation process of the degree of correlation of two width pictures is also the coordinate of mobile two width pictures thus the process of mating two width pictures.When the content similar portion of two width picture corresponding parts is less time, the degree of correlation so between this position two width picture can be lower.Continuous movement two width picture, when the content similarity of two width picture corresponding parts is higher time, the degree of correlation between two width pictures also can increase accordingly, until the appropriate section of two width pictures overlaps completely, its degree of correlation also can reach maximum.
S143, is mapped frame of video picture corresponding for degree of correlation maximal value and reference picture appropriate section as registration picture.
The position that the degree of correlation of two width pictures is maximum, the part of two also namely corresponding width picture appropriate section overlaps.Corresponding frame of video picture and reference picture part picture are mapped, thus as registration picture.
In another specific embodiment, as shown in Figure 5, frame of video picture and reference picture are carried out registration to obtain registration picture and comprise:
S142, obtains a pair unique point between frame of video picture and reference picture.
Coordinate (x, y) and the reference picture coordinate (x of frame of video picture
0, y
0) between affine transformation relationship be
Need the three pairs of unique points found out respectively between reference picture and frame of video picture according to the expression formula of affine transformation matrix, these three pairs of unique points have same characteristic features, to obtain affine transformation matrix.Then affined transformation is carried out to frame of video picture and reach registration object.But in fact due to the setting of parallel sweep, only there is translation transformation in reference picture and frame of video picture, i.e. a
1, b
1, a
2and b
2all equal 1, as long as obtain d
1and d
2if, so find out a pair unique point.
S144, according to the correlation parameter in the affine transformation matrix that unique point coordinate is obtained between frame of video picture and reference picture.
Can obtain according to unique point coordinate
In d
1and d
2.
S146: according to affine transformation matrix frame of video picture carried out corresponding translation and aim at as registration picture with reference picture.
After affine transformation matrix is determined, frame of video picture and reference picture just can be mapped.According to affine transformation matrix, frame of video picture being done corresponding translation just can by frame of video picture registration.
In a specific embodiment, as shown in figures 6 a-6d, video V becomes frame of video picture V1, V2 through conversion ... V10.By to frame of video picture V1, V2 ... the degree of correlation between V10 calculates, and then selects satisfactory frame of video picture F1, F2 and F3 according to the degree of correlation preset.By the method described by step S141-S143 or step S142-S146, satisfactory frame of video picture F1, F2 and F3 and reference picture R is carried out registration, obtain registration picture P1, P2 and P3.
Obtain registration picture by frame of video picture and reference picture R are carried out registration, avoid the process of high precision physical alignment and location motion scan, improve the speed of scanning, decrease the complexity of scanning.In a specific embodiment, as shown in Figure 7, reference picture R and frame of video picture P1, P2, P3, P4 and P5 of being obtained by the filter 50 of different wave length by after translation registration, 1 on reference picture R, 2,3,4,5 these 5 reference point can find the pixel of correspondence at different frame of video picture P1, P2, P3, P4 and P5.
S150, is analyzed registration picture and reference picture and determines defect pixel.The form determination defect pixel that can be contrasted by individual element; Also can contrast first subregion, find out defect area and determine defect pixel in this region again.Specifically, as shown in Figure 8, step S150 can comprise step S152 to step S156.
S152, according to the sample area in the sampled point determination registration picture in reference picture.Pixel corresponding with the sampled point in reference picture in registration picture is referred to as corresponding point, with the border circular areas that these corresponding point are the center of circle, or centered by these corresponding point or a certain rectangular area of angle point, also can be using other graphics fields that these corresponding point are unique point as sample area.The selection of figure and size can preset according to the particular content that will detect, as the selection of circle, the selection etc. of rectangle.
S154, contrasts the corresponding region in sample area and reference picture, to judge in sample area whether defectiveness pixel.Such as the average gray of the corresponding region in the average gray of sample area and reference picture can be contrasted.If gray scale difference exceedes setting value, then think defectiveness pixel in sample area.Method above by zonule averaging method selected pixels gray scale can reduce the impact that registration error is brought, and reduces the possibility of flase drop, indirectly improves accuracy of detection.
S156, determines the defect pixel of sample area.The difference of the gray scale of the respective pixel in the pixel grey scale of sample area and reference picture R is greater than the pixel of predetermined threshold value as defect pixel.In a specific embodiment, as shown in Figure 9, horizontal ordinate is corresponding each pixel, and ordinate is gray-scale value.Compared with the gray scale of pixel 1,2,3,4,5 in this 5 width frame picture of P1, P2, P3, P4 and P5 and pixel 1,2,3,4,5 gray scale in reference picture R, major part is all within threshold line.Except the gray scale of pixel 3 in a certain width frame of video picture is beyond the scope of threshold line, and the pixel exceeding threshold line scope is considered to defect pixel, and therefore pixel 3 is defect pixels.
In one embodiment, if carry out videoscanning to substrate 20 to be measured to be specially the different filter 50 of employing, that repeats after the filter 50 of each use different wave length carries out videoscanning to substrate 20 to be measured, obtains the spectrum of the characteristic wavelength of corresponding filter 50.By obtaining the curve of spectrum of sample area pixel, then judge the position of abnormity point according to the consistance of the curve of spectrum.As shown in Figure 10, horizontal ordinate is wavelength, ordinate is gray scale, the curve of spectrum of corresponding 1,2,3 these 3 pixels in different registration pictures is depicted in figure, same curves is the gray scale under the same pixel corresponding wavelength that different filter 50 obtains under same registration picture, and the different curves of same pixel are the gray scale under the same pixel corresponding wavelength that different filter 50 obtains under different registration picture.Consistance according to the curve of spectrum can determine abnormal pixel, thus can judge the position of abnormity point.Concrete, the curve of spectrum of pixel 3 is that exception has appearred in these three wavelength of 550nm, 600nm, 650nm at wavelength.The curve of spectrum of pixel 1 is that exception has appearred in these two wavelength of 650nm, 700nm at wavelength, and therefore pixel 1 and 3 is considered to abnormity point, and its materials and process does not meet consistance.This detection method the consistance of Alternative material can make classification and Detection, achieves the detection to process materials consistency problem.
Above-mentioned picture element flaw detection method is by video capture and extraction meets pre-conditioned frame picture, take pictures relative to common camera and can improve the speed of detection, the quick detection to base board defect to be measured when can be implemented in without high precision physical alignment by carrying out registration to frame of video picture and reference picture and locate motion scan.By being analyzed the grey scale pixel value in sample area, can have for defect is detected, improve accuracy of detection.
In one embodiment, as shown in figure 11, a kind of picture element flaw pick-up unit 100 comprises videoscanning unit 110, picture extracting unit 120, registration unit 130, defect pixel determining unit 140 and consistance judging unit 150.
Videoscanning unit 110 is for carrying out videoscanning to substrate 20 to be measured.The pattern of carrying out videoscanning to substrate 20 to be measured is substrate Move Mode or camera Move Mode.For the ease of the switching of camera 60 between macro check and micro examination (adding microexamination), substrate Move Mode can be adopted.In the present embodiment, at the uniform velocity move substrate 20 to be measured by S type path to realize carrying out videoscanning to substrate 20 to be measured.By videoscanning unit 110, substrate 20 to be measured is taken, greatly can improve the speed of detection.The frame number of a lot of video capture devices collection per second and the resolution of every frame picture are inversely proportional to.For the object of high-speed mobile, in order to see the details of object movement clearly, the temporal resolution of video just very important.Present high speed camera, a lot of per secondly can take 1,000,000 frames, to tackle the needs of high-speed capture.In the present embodiment, should be the resolution more focusing on frame of video picture, even can be less than 24 frames per second as frame number per second, because should be enough relative to the rate travel of substrate 20 to be measured.In addition, the precision that high-resolution picture can strengthen detection is obtained.The frame number that the video of concrete videoscanning unit 110 shooting is per second should be determined according to the speed of scanning.After video acquisition completes, video is converted into frame of video picture, then only needs to process frame of video picture.
Picture extracting unit 120 meets pre-conditioned frame of video picture for extracting.The frame of video picture of the degree of correlation between picture in preset range is extracted at concrete picture extracting unit 120 interval.In the present embodiment, preset range is choose the little picture of the degree of correlation, and this preset range can reduce the repeating part between the photograph of front and back, decreases unnecessary repetitive operation, indirectly improves the speed of scanning.If but the degree of correlation between two width pictures arranges too small, the pixel of a lot of scanning will be omitted between so adjacent frame of video picture, a lot of pixels can be caused undetected, the error rate of detection can be increased like this.Therefore, the preset range of the degree of correlation needs to set as required.The computing formula of the degree of correlation between concrete picture is C (x, y)=corr (X (x1, y1), Y (x2, y2)), wherein X (x1, y1) be that the first width picture meta is set to (x1, y1) gray scale of the pixel at place, Y (x2, y2) is that the second width picture meta is set to (x2, the gray scale of pixel y2), C (x, y) is the degree of correlation between two width pictures when shift position is (x, y).The similarity of the content of two width pictures is large, and so the degree of correlation of this two width picture is just large.If the similarity of the content of two width pictures is little, so the degree of correlation of this two width picture is just little.
If need to process the frame of video picture of conversion, these frame of video pictures must be carried out registration.Find out the position of the pixel in frame of video picture, then error analysis and material, process consistency analysis are carried out to relevant pixel.
Registration unit 130 obtains registration picture for frame of video picture and reference picture are carried out registration.Defect pixel determining unit 140 determines defect pixel for registration picture and reference picture being analyzed.
In one embodiment, as shown in figure 12, registration unit 130 comprises correlation calculating unit 132 and the first picture registration unit 134.
Correlation calculating unit 132 is for calculating the degree of correlation between frame of video picture and reference picture.The degree of correlation between correlation calculating unit 132 adopts in above-mentioned formulae discovery frame of video picture and reference picture.The computation process of the degree of correlation of two width pictures is also the coordinate of mobile two width pictures thus the process of mating two width pictures.When the content similar portion of two width picture corresponding parts is less time, the degree of correlation so between this position two width picture can be lower.Continuous movement two width picture, when the content similarity of two width picture corresponding parts is higher time, the degree of correlation between two width pictures also can increase accordingly, until the appropriate section of two width pictures overlaps completely, its degree of correlation also can reach maximum.
First picture registration unit 134 is for being mapped frame of video picture corresponding for degree of correlation maximal value and reference picture appropriate section as registration picture.The position that the degree of correlation of two width pictures is maximum, the part of two also namely corresponding width picture appropriate section overlaps.Corresponding frame of video picture and reference picture part picture are mapped by the first picture registration unit 134, thus as registration picture.
In another embodiment, as shown in figure 13, registration unit 130 comprises unique point acquiring unit 131, parametric solution unit 133 and second picture registration unit 135.
Unique point acquiring unit 131 is for obtaining a pair unique point between frame of video picture and reference picture.Parametric solution unit 133 is for obtaining according to unique point coordinate
In d
1and d
2, wherein
Represent the coordinate (x, y) of frame of video picture and reference picture coordinate (x
0, y
0) between affine transformation relationship.Need the three pairs of unique points found out respectively between reference picture and frame of video picture according to the expression formula of affine transformation matrix, these three pairs of unique points have same characteristic features, to obtain affine transformation matrix.Then affined transformation is carried out to frame of video picture and reach registration object.But in fact due to the setting of parallel sweep, only there is translation transformation in reference picture and frame of video picture, i.e. a
1, b
1, a
2and b
2all equal 1, as long as obtain d
1and d
2if, so find out a pair unique point.Second picture registration unit 135 is for according to formula
Frame of video picture is carried out corresponding translation and aims at as registration picture with reference picture.After affine transformation matrix is determined, frame of video picture and reference picture just can be mapped.Frame of video picture is done corresponding translation according to affine transformation matrix by second picture registration unit 135 just can by frame of video picture registration.
Obtain registration picture by frame of video picture and reference picture R are carried out registration, avoid the process of high precision physical alignment and location motion scan, improve the speed of scanning, decrease the complexity of scanning.In a specific embodiment, as shown in Figure 7, reference picture R and frame of video picture P1, P2, P3, P4 and P5 of being obtained by the filter 50 of different wave length by after translation registration, 1 on reference picture R, 2,3,4,5 these 5 reference point can find the pixel of correspondence at different frame of video picture P1, P2, P3, P4 and P5.
In one embodiment, as shown in figure 14, defect pixel determining unit 140 comprises sample area determining unit 142, contrast unit 144 and defect pixel acquiring unit 146.
Sample area determining unit 142 is for according to the sample area in the sampled point determination registration picture in reference picture.Pixel corresponding with the sampled point in reference picture in registration picture is referred to as corresponding point, with the border circular areas that these corresponding point are the center of circle, or centered by these corresponding point or a certain rectangular area of angle point, also can be using other graphics fields that these corresponding point are unique point as sample area.The selection of figure and size can preset according to the particular content that will detect, as the selection of circle, the selection etc. of rectangle.Contrast unit 144, for being contrasted the corresponding region in sample area and reference picture, to judge in sample area whether defectiveness pixel.Defect pixel acquiring unit 146 is for obtaining the defect pixel of sample area.The difference of the pixel grey scale of the corresponding region in the pixel grey scale of sample area and reference picture is greater than the pixel of predetermined threshold value as defect pixel by concrete defect pixel acquiring unit 146.
In a specific embodiment, as shown in Figure 9, horizontal ordinate is corresponding each pixel, and ordinate is gray-scale value.Compared with the gray scale of pixel 1,2,3,4,5 in this 5 width frame picture of P1, P2, P3, P4 and P5 and pixel 1,2,3,4,5 gray scale in reference picture R, major part is all within threshold line.Except the gray scale of pixel 3 in a certain width frame of video picture is beyond the scope of threshold line, and the pixel exceeding threshold line scope is considered to defect pixel, and therefore pixel 3 is defect pixels.
If spectral detection will be carried out for the process materials consistency problem of substrate 20 to be measured, before videoscanning unit 110 carries out videoscanning to substrate 20 to be measured, insert corresponding filter 50.Carry out videoscanning to substrate 20 to be measured to be specially: the light intensity of light source 30 be adjusted to unanimously, adopt the filter 50 of corresponding different wave length, each rear movement substrate 20 to be measured repeated of the filter 50 of different wave length that uses carries out videoscanning.Consistance judging unit 150, for obtaining the curve of spectrum of sample area pixel, then judges the position of abnormity point according to the consistance of this curve of spectrum.As shown in Figure 10, horizontal ordinate is wavelength, ordinate is gray scale, the curve of spectrum of corresponding 1,2,3 these 3 pixels in different registration pictures is depicted in figure, same curves is the gray scale under the same pixel corresponding wavelength that different filter 50 obtains under same registration picture, and the different curves of same pixel are the gray scale under the same pixel corresponding wavelength that different filter 50 obtains under different registration picture.Consistance according to the curve of spectrum can determine abnormal pixel, thus can judge the position of abnormity point.Concrete, the curve of spectrum of pixel 3 is that exception has appearred in these three wavelength of 550nm, 600nm, 650nm at wavelength.The curve of spectrum of pixel 1 is that exception has appearred in these two wavelength of 650nm, 700nm at wavelength, and therefore pixel 1 and 3 is considered to abnormity point, and its materials and process does not meet consistance.This detection method the consistance of Alternative material can make classification and Detection, achieves the detection to process materials consistency problem.
Above-mentioned picture element flaw pick-up unit is by video capture and extraction meets pre-conditioned frame picture, take pictures relative to common camera and can improve the speed of detection, the quick detection to the defect of substrate to be measured when can be implemented in without high precision physical alignment by carrying out registration to frame of video picture and reference picture and locate motion scan.
The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (22)
1. a picture element flaw detection method, is characterized in that, comprises the steps:
Videoscanning is carried out to substrate to be measured;
Extract and meet pre-conditioned frame of video picture;
Described frame of video picture and reference picture are carried out registration and obtains registration picture;
Described registration picture and described reference picture are analyzed and determine defect pixel.
2. picture element flaw detection method as claimed in claim 1, it is characterized in that, described described registration picture and described reference picture being analyzed determines that the step of defect pixel comprises:
The sample area in described registration picture is determined according to the sampled point in described reference picture;
Corresponding region in described sample area and described reference picture is contrasted, judges whether there is described defect pixel in described sample area;
Determine the described defect pixel of described sample area.
3. picture element flaw detection method as claimed in claim 2, is characterized in that, described substrate to be measured is carried out to the step of videoscanning before, also comprise the step inserting corresponding filter.
4. picture element flaw detection method as claimed in claim 3, is characterized in that, described to substrate to be measured carry out videoscanning be specially repeatedly use the filter of different wave length to repeat videoscanning is carried out to described substrate to be measured.
5. picture element flaw detection method as claimed in claim 4, is characterized in that, also comprise the curve of spectrum obtaining described sample area pixel, then judge the position of abnormity point according to the consistance of the described curve of spectrum.
6. picture element flaw detection method as claimed in claim 2, is characterized in that, the difference of the pixel grey scale of the corresponding region in the pixel grey scale of described sample area and described reference picture is greater than the pixel of described predetermined threshold value as defect pixel.
7. picture element flaw detection method as claimed in claim 1, it is characterized in that, described extraction meets pre-conditioned frame of video picture and is specially the frame of video picture of the degree of correlation between extracted at equal intervals picture in preset range.
8. picture element flaw detection method as claimed in claim 1, is characterized in that, described pattern of carrying out videoscanning to substrate to be measured is substrate Move Mode or camera Move Mode.
9. picture element flaw detection method as claimed in claim 1, is characterized in that, at the uniform velocity moves described substrate to be measured realize carrying out videoscanning to described substrate to be measured by S type path.
10. as claimed in any one of claims 1-9 wherein picture element flaw detection method, is characterized in that, describedly described frame of video picture and reference picture is carried out the step that registration obtains registration picture and comprises:
Calculate the degree of correlation between described frame of video picture and described reference picture;
Described frame of video picture corresponding for described degree of correlation maximal value and described reference picture appropriate section are mapped as registration picture.
11. picture element flaw detection methods as claimed in any one of claims 1-9 wherein, is characterized in that, the described registration that described frame of video picture and reference picture carried out obtains registration picture and comprises:
Obtain a pair unique point between described frame of video picture and described reference picture;
Obtain according to described unique point coordinate
In d
1and d
2, wherein
Represent the coordinate (x, y) of described frame of video picture and described reference picture coordinate (x
0, y
0) between affine transformation relationship, wherein a
1, b
1, a
2and b
2all equal 1;
According to formula
Described frame of video picture is carried out corresponding translation and aims at as registration picture with described reference picture.
12. 1 kinds of picture element flaw pick-up units, is characterized in that, comprising:
Videoscanning unit, for carrying out videoscanning to substrate to be measured;
Picture extracting unit, meets pre-conditioned frame of video picture for extracting;
Registration unit, obtains registration picture for described frame of video picture and reference picture are carried out registration;
Defect pixel determining unit, determines defect pixel for described registration picture and described reference picture being analyzed.
13. picture element flaw pick-up units as claimed in claim 12, it is characterized in that, described defect pixel determining unit comprises:
Sample area determining unit, for determining the sample area in described registration picture according to the sampled point in described reference picture;
Contrast unit, for being contrasted the corresponding region in described sample area and described reference picture, judges whether there is described defect pixel in described sample area;
Defect pixel acquiring unit, for obtaining the described defect pixel of described sample area.
14. picture element flaw pick-up units as claimed in claim 13, is characterized in that, before videoscanning unit carries out videoscanning to substrate to be measured, insert corresponding filter.
15. picture element flaw pick-up units as claimed in claim 14, it is characterized in that, what to described substrate to be measured, videoscanning unit carried out that videoscanning is specially that videoscanning unit repeatedly uses the filter of different wave length to repeat carries out videoscanning to described substrate to be measured.
16. picture element flaw pick-up units as claimed in claim 15, it is characterized in that, also comprise consistance judging unit, described consistance judging unit, for obtaining the curve of spectrum of described sample area pixel, then judges the position of abnormity point according to the consistance of the described curve of spectrum.
17. picture element flaw pick-up units as claimed in claim 13, is characterized in that, the difference of the pixel grey scale of the corresponding region in the pixel grey scale of described sample area and described reference picture is greater than the pixel of described predetermined threshold value as defect pixel.
18. picture element flaw pick-up units as claimed in claim 12, is characterized in that, described extraction meets pre-conditioned frame of video picture and is specially the frame of video picture of the degree of correlation between extracted at equal intervals picture in preset range.
19. picture element flaw pick-up units as claimed in claim 12, is characterized in that, described pattern of carrying out videoscanning to substrate to be measured is substrate Move Mode or camera Move Mode.
20. picture element flaw pick-up units as claimed in claim 12, is characterized in that, at the uniform velocity move described substrate to be measured realize carrying out videoscanning to described substrate to be measured by S type path.
21. picture element flaw pick-up units according to any one of claim 12 to 20, it is characterized in that, described registration unit comprises:
Correlation calculating unit, for calculating the degree of correlation between described frame of video picture and described reference picture;
First picture registration unit, for being mapped described frame of video picture corresponding for described degree of correlation maximal value and described reference picture appropriate section as registration picture.
22. picture element flaw pick-up units according to any one of claim 12 to 20, it is characterized in that, described registration unit comprises:
Unique point acquiring unit, for obtaining a pair unique point between described frame of video picture and described reference picture;
Parametric solution unit, for obtaining according to described unique point coordinate
In d
1and d
2, wherein
Represent the coordinate (x, y) of described frame of video picture and described reference picture coordinate (x
0, y
0) between affine transformation relationship, wherein a
1, b
1, a
2and b
2all equal 1;
Second picture registration unit, for according to formula
Described frame of video picture is carried out corresponding translation and aims at as registration picture with described reference picture.
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