CN116045852B - Three-dimensional morphology model determining method and device and three-dimensional morphology measuring equipment - Google Patents

Three-dimensional morphology model determining method and device and three-dimensional morphology measuring equipment Download PDF

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CN116045852B
CN116045852B CN202310336051.0A CN202310336051A CN116045852B CN 116045852 B CN116045852 B CN 116045852B CN 202310336051 A CN202310336051 A CN 202310336051A CN 116045852 B CN116045852 B CN 116045852B
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CN116045852A (en
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吴征宇
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Slate Intelligent Technology Shenzhen Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06T17/05—Geographic models
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G06T7/44—Analysis of texture based on statistical description of texture using image operators, e.g. filters, edge density metrics or local histograms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
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    • G06T2207/20064—Wavelet transform [DWT]

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Abstract

The invention provides a three-dimensional morphology model determining method and device and three-dimensional morphology measuring equipment, wherein the method comprises the following steps: collecting an image sequence of a sample to be detected, wherein the image sequence comprises a plurality of sample images to be detected with different focal planes; determining a plurality of focus evaluation values of each pixel point in a plurality of sample images to be tested based on a focus evaluation operator; determining a focusing grade model based on the focusing evaluation values, determining initial positions of all pixel points based on the focusing grade model, and determining an initial three-dimensional morphology model based on the initial positions; determining a focus evaluation peak value based on the focus level model, and determining a confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value; and correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model. According to the method, the initial three-dimensional morphology model is corrected based on the confidence evaluation value, and the accuracy of the determined target three-dimensional morphology model is improved.

Description

Three-dimensional morphology model determining method and device and three-dimensional morphology measuring equipment
Technical Field
The invention relates to the technical field of three-dimensional morphology measurement, in particular to a three-dimensional morphology model determining method and device and three-dimensional morphology measurement equipment.
Background
The three-dimensional shape measurement is widely applied to industries such as aerospace, medical equipment and integrated circuits, and along with the development of ultra-precise processing technology, a microstructure is gradually expanded from a workpiece with a simple structure and regular shape to a workpiece with a complex structure and irregular shape, and the high-precision and high-reliability surface shape measurement of the microstructure is increasingly important. The three-dimensional surface morphology of the microstructure can have a remarkable influence on the reliability and the service performance of the device, and meanwhile, the quality of workpiece processing can be reflected, so that the quality of the workpiece is improved. Therefore, the improvement of the quality and the accuracy of the determined three-dimensional morphology model has important significance for ensuring the high performance and the high stability of the product.
The existing three-dimensional morphology model determining method is divided into a non-optical determining method and an optical determining method, wherein a zooming determining method in the optical determining method is widely applied to the advantages of high precision, high efficiency, no damage and the like. However, the zoom determination method has the following technical problems: the zoom determination method is an optical method for determining the three-dimensional morphology model by combining small depth-of-field measurement and precise vertical scanning of an optical system, and the obtained three-dimensional morphology model is easy to generate noise, so that the determined three-dimensional morphology model is low in accuracy.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method and apparatus for determining a three-dimensional morphology model and a three-dimensional morphology measurement device, so as to solve the technical problem in the prior art that the accuracy of the determined three-dimensional morphology model is not high.
In one aspect, the present invention provides a method for determining a three-dimensional morphology model, including:
collecting an image sequence of a sample to be detected, wherein the image sequence comprises a plurality of sample images to be detected with different focal planes;
determining a plurality of focus evaluation values of each pixel point in the plurality of sample images to be tested based on a focus evaluation operator;
determining a focus level model based on the plurality of focus evaluation values, determining an initial position of each pixel point based on the focus level model, and determining an initial three-dimensional morphology model based on the initial position;
determining a focus evaluation peak value based on the focus level model, and determining a confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value;
and correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model.
In some possible implementations, the focus evaluation operator is:
Figure SMS_1
in the method, in the process of the invention,
Figure SMS_2
is a focus evaluation value; />
Figure SMS_3
Vertical lines after wavelet filtering for sample image to be measuredManaging information; />
Figure SMS_4
The horizontal texture information of the sample image to be detected after wavelet filtering; />
Figure SMS_5
The diagonal texture information of the sample image to be detected after wavelet filtering; m is the total line number of the pixel points in the selected sample image to be detected; n is the total column number of the pixel points in the selected sample image to be detected; and I is an absolute value operation symbol.
In some possible implementations, the focus level model is:
Figure SMS_6
in the method, in the process of the invention,
Figure SMS_7
peak value of focusing grade model; />
Figure SMS_8
Is the initial position; />
Figure SMS_9
Is the mean value of the focusing grade model; />
Figure SMS_10
Is the standard deviation of the focus level model.
In some possible implementations, the determining the confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value includes:
determining a maximum focus evaluation value of the plurality of focus evaluation values;
determining an error of the maximum focus evaluation value and the focus evaluation peak value;
and determining a confidence evaluation value of each pixel point based on the error and the focus evaluation peak value.
In some possible implementations, the confidence evaluation value is:
Figure SMS_11
in the method, in the process of the invention,
Figure SMS_12
the confidence evaluation value of the pixel point of the x row and the y column is obtained; />
Figure SMS_13
The maximum focus evaluation value and the focus evaluation peak error of the pixel point of the x-th row and the y-th column are obtained.
In some possible implementations, the correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model includes:
determining invalid pixel points of which the confidence evaluation values are smaller than a confidence evaluation threshold value in the initial three-dimensional morphology model;
and deleting the invalid pixel points to obtain the target three-dimensional morphology model.
In some possible implementations, after deleting the invalid pixel point to obtain the target three-dimensional morphology model, the method further includes:
rasterizing the target three-dimensional morphology model to obtain a plurality of grid areas;
determining a center point of the grid region, and determining the distance between each pixel point in the grid region and the center point;
and determining an outlier pixel point with a distance larger than a distance threshold value in the grid area, and deleting the outlier pixel point to obtain the optimized three-dimensional morphology model.
In some possible implementations, before the acquiring the image sequence of the sample to be measured, the method further includes:
placing the sample to be tested at a preset position;
acquiring a plurality of groups of exposure time, and acquiring a plurality of groups of sample exposure images to be detected corresponding to the plurality of groups of exposure time;
determining a plurality of image focus evaluation values of the plurality of groups of sample exposure images to be tested based on the focus evaluation operator;
and determining the maximum image focusing evaluation value in the plurality of image focusing evaluation values, and taking the exposure time corresponding to the maximum image focusing evaluation value as the optimal exposure time.
In another aspect, the present invention further provides a three-dimensional morphology model determining apparatus, including:
the image sequence acquisition unit is used for acquiring an image sequence of a sample to be detected, wherein the image sequence comprises a plurality of sample images to be detected with different focal planes;
a focus evaluation value determining unit for determining a plurality of focus evaluation values of each pixel point in the plurality of sample images to be tested based on a focus evaluation operator;
an initial three-dimensional morphology model determining unit configured to determine a focus level model based on the plurality of focus evaluation values, determine an initial position of each of the pixel points based on the focus level model, and determine an initial three-dimensional morphology model based on the initial position;
a confidence evaluation value determining unit configured to determine a focus evaluation peak value based on the focus level model, and determine a confidence evaluation value of each of the pixels based on the plurality of focus evaluation values and the focus evaluation peak value;
and the target three-dimensional morphology model determining unit is used for correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model.
In another aspect, the present invention also provides a three-dimensional topography measurement apparatus comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor is coupled to the memory, and is configured to execute the program stored in the memory, so as to implement the steps in the three-dimensional topography measurement method in any one of the possible implementation manners.
The beneficial effects of adopting the embodiment are as follows: after an initial three-dimensional morphology model of a sample to be measured is determined, firstly, determining a focus evaluation peak value based on a focus grade model, and determining confidence evaluation values of all pixel points based on a plurality of focus evaluation values and the focus evaluation peak value; and then correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model, denoising the initial three-dimensional morphology model according to the confidence evaluation value, and improving the accuracy of the obtained target three-dimensional morphology model, namely: the real appearance of the measured sample is restored to the maximum extent.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an embodiment of a method for determining a three-dimensional morphology model according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of an optical path system according to the present invention;
FIG. 3 is a flow chart of the embodiment of S104 in FIG. 1 according to the present invention;
FIG. 4 is a flow chart of one embodiment of S105 of FIG. 1 according to the present invention;
FIG. 5 is a flowchart illustrating an embodiment of deleting outlier pixels according to the present invention;
FIG. 6 is a flow chart of an embodiment of determining an optimal exposure time according to the present invention;
FIG. 7 is a schematic structural diagram of an embodiment of a three-dimensional morphology model determining apparatus provided by the present invention;
fig. 8 is a schematic structural diagram of an embodiment of a three-dimensional morphology model determining apparatus provided by the present invention.
Detailed Description
In order to make the technical solution of the embodiments of the present invention clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the schematic drawings are not drawn to scale. A flowchart, as used in this disclosure, illustrates operations implemented according to some embodiments of the present invention. It should be appreciated that the operations of the flow diagrams may be implemented out of order and that steps without logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to or removed from the flow diagrams by those skilled in the art under the direction of the present disclosure. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor systems and/or microcontroller systems.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention provides a three-dimensional morphology model determining method and device and three-dimensional morphology model determining equipment, and the three-dimensional morphology model determining method and device are described below.
Fig. 1 is a schematic flow chart of an embodiment of a three-dimensional morphology model determining method provided by the present invention, where, as shown in fig. 1, the three-dimensional morphology model determining method includes:
s101, collecting an image sequence of a sample to be detected, wherein the image sequence comprises a plurality of sample images to be detected with different focal planes;
s102, determining a plurality of focus evaluation values of each pixel point in a plurality of sample images to be tested based on a focus evaluation operator;
s103, determining a focusing grade model based on a plurality of focusing evaluation values, determining initial positions of all pixel points based on the focusing grade model, and determining an initial three-dimensional morphology model based on the initial positions;
s104, determining a focus evaluation peak value based on the focus level model, and determining a confidence evaluation value of each pixel point based on a plurality of focus evaluation values and the focus evaluation peak value;
s105, correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model.
Compared with the prior art, after an initial three-dimensional morphology model of a sample to be measured is determined, firstly, a focus evaluation peak value is determined based on a focus grade model, and a confidence evaluation value of each pixel point is determined based on a plurality of focus evaluation values and the focus evaluation peak value; and then correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model, denoising the initial three-dimensional morphology model according to the confidence evaluation value, and improving the accuracy of the obtained target three-dimensional morphology model, namely: the real appearance of the measured sample is restored to the maximum extent.
It should be noted that: the manner of collecting the image sequence of the sample to be measured in step S101 may be collecting the image sequence of the sample to be measured based on the optical path system in real time, or may be called from a storage medium storing the image sequence of the sample to be measured collected based on the optical path system.
In an embodiment of the present invention, as shown in fig. 2, the optical path system 10 includes a white light source 11, a field stop 12, a lens 13, a beam splitter 14, a microscope 15, a barrel lens 16, a charge coupled device (Charge Coupled Device, CCD) camera 17, and an electric displacement stage 18, and a sample to be measured is disposed on the light emitting side of the microscope 15.
Specifically, the principle of the optical path system 10 for collecting the image sequence of the sample to be measured is as follows: the white light source 11 is used for generating an incident light source, and a collimating light source is generated through the lens 13 after the illumination intensity is regulated through the view field diaphragm 12; the collimated light source is reflected to the microscope objective 15 through the beam splitter 14, then is converged on the sample to be detected, and the light irradiated on the sample to be detected is reflected to the microscope objective 15 again and is transmitted to the barrel lens 16 to be amplified and imaged on the CCD camera 17.
The electric displacement table 18 is used for driving the sample to be measured to move along the horizontal direction or the vertical direction.
It should be noted that: the plurality of images of the sample to be measured with different focal planes means that the electric displacement table 18 drives the sample to be measured to move along the vertical direction to obtain a plurality of images. The motorized displacement stage 18 is moved horizontally for adjusting the measurement area.
In order to facilitate adjusting the illumination environment of the sample to be measured, in some embodiments of the present invention, as shown in fig. 2, the optical path system 10 further includes an annular light source 19, where the annular light source 19 is disposed around the microscope objective 15, for adjusting the illumination environment of the imaging field of view of the microscope objective 15.
In a specific embodiment of the present invention, the plurality of images of the sample to be measured in the image sequence are a plurality of images of the sample to be measured collected in a vertical direction with equal interval variation.
The step S101 specifically includes: determining an initial position and a final position of a sample to be detected, determining an acquisition interval according to the initial position and the final position, and acquiring a plurality of images of the sample to be detected based on the acquisition interval.
In some embodiments of the present invention, determining the initial position of each pixel point based on the focus level model in step S103 is specifically: determining a maximum focus evaluation value in the plurality of focus evaluation values, and bringing the maximum focus evaluation value into a focus level model to obtain an initial position, namely: the initial position is the position where the maximum focus evaluation value is located.
In some embodiments of the invention, the focus evaluation operator is:
Figure SMS_14
in the method, in the process of the invention,
Figure SMS_15
is a focus evaluation value; />
Figure SMS_16
The vertical texture information of the sample image to be detected after wavelet filtering; />
Figure SMS_17
The horizontal texture information of the sample image to be detected after wavelet filtering; />
Figure SMS_18
The diagonal texture information of the sample image to be detected after wavelet filtering; m is the total line number of the pixel points in the selected sample image to be detected; n is the total column number of the pixel points in the selected sample image to be detected; and I is an absolute value operation symbol.
It should be noted that: when a focus evaluation value of a certain pixel point is obtained, M, N may be set according to an actual application scenario, for example: when M and N are 3, the focus evaluation value of a certain pixel point is 9 pixel points selected 3*3 around the certain pixel point with the certain pixel point as the center, and the focus evaluation value of the 9 pixel points is taken as the pixel evaluation value of the certain pixel.
According to the embodiment of the invention, the pixel evaluation value in one area is used as the pixel evaluation value of a certain pixel point, so that the reliability of the pixel evaluation value of the pixel point can be improved.
In some embodiments of the invention, the focus level model is:
Figure SMS_19
in the method, in the process of the invention,
Figure SMS_20
peak value of focusing grade model; />
Figure SMS_21
Is the initial position; />
Figure SMS_22
Is the mean value of the focusing grade model; />
Figure SMS_23
Is the standard deviation of the focus level model.
Wherein, the determining the focus level model based on the plurality of focus evaluation values in step S103 is specifically: setting a focusing grade model as a Gaussian model, and then fitting a plurality of focusing evaluation values based on a Gaussian curve fitting algorithm and the Gaussian model to obtain the focusing grade model.
The specific fitting process is as follows: natural logarithms are obtained on two sides of a focusing grade model, a Gaussian function is converted into a unitary quadratic function, a focusing evaluation maximum value and left and right points thereof are taken for solving, the focusing grade model is obtained, and a mathematical expression for solving is as follows:
Figure SMS_24
Figure SMS_25
Figure SMS_26
in the method, in the process of the invention,
Figure SMS_27
is a unitary quadratic function.
In some embodiments of the present invention, as shown in fig. 3, step S104 includes:
s301, determining a maximum focus evaluation value in a plurality of focus evaluation values;
s302, determining errors of a maximum focus evaluation value and a focus evaluation peak value;
s303, determining a confidence evaluation value of each pixel point based on the error and the focus evaluation peak value.
According to the embodiment of the invention, denoising is performed by combining the focus evaluation value and the error of the focus evaluation peak value in the focus evaluation model obtained by fitting, so that the real appearance of the measured object can be restored to the maximum extent, and the accuracy of the determined target three-dimensional appearance model is further improved.
In a specific embodiment of the present invention, the confidence rating is:
Figure SMS_28
in the method, in the process of the invention,
Figure SMS_29
the confidence evaluation value of the pixel point of the x row and the y column is obtained; />
Figure SMS_30
The maximum focus evaluation value and the focus evaluation peak error of the pixel point of the x-th row and the y-th column are obtained.
In some embodiments of the present invention, as shown in fig. 4, step S105 includes:
s401, determining invalid pixel points with confidence evaluation values smaller than a confidence evaluation threshold value in an initial three-dimensional morphology model;
s402, deleting the invalid pixel points to obtain the target three-dimensional morphology model.
According to the embodiment of the invention, the invalid pixel points with the confidence evaluation value smaller than the confidence evaluation threshold value in the initial three-dimensional morphology model can eliminate the noise points in the determined target three-dimensional morphology model to a certain extent, and the accuracy of the target three-dimensional morphology model is improved.
It should be understood that: the confidence evaluation threshold may be set or adjusted according to the actual application scenario, which is not specifically limited herein.
To further improve the accuracy of the obtained three-dimensional morphology model, in some embodiments of the present invention, as shown in fig. 5, after step S402, the method further includes:
s501, rasterizing the target three-dimensional morphology model to obtain a plurality of grid areas;
s502, determining a center point of a grid area, and determining the distance between each pixel point in the grid area and the center point;
s503, determining outlier pixel points with the distance larger than a distance threshold value in the grid area, and deleting the outlier pixel points to obtain the optimized three-dimensional morphology model.
According to the embodiment of the invention, after the target three-dimensional morphology model is obtained, the outlier pixel points are determined and removed, so that the obtained target three-dimensional morphology model can be further denoised, and the accuracy of the obtained optimized three-dimensional morphology model can be further improved.
It should be understood that: the distance threshold may be set or adjusted according to the actual application scenario, for example: the setting or adjustment can be performed according to the total number of pixel points in the target three-dimensional morphology model, which is not particularly limited herein.
Since the determined three-dimensional morphology model of the target will be inaccurate when the accuracy of the acquired image sequence is low, in order to ensure the accuracy of the acquired image sequence, in some embodiments of the present invention, as shown in fig. 6, before step S101, the method further includes:
s601, placing a sample to be tested at a preset position;
s602, acquiring a plurality of groups of exposure time, and acquiring a plurality of groups of sample exposure images to be detected corresponding to the plurality of groups of exposure time;
s603, determining a plurality of image focus evaluation values of a plurality of groups of sample exposure images to be tested based on a focus evaluation operator;
s604, determining the maximum image focusing evaluation value in the plurality of image focusing evaluation values, and taking the exposure time corresponding to the maximum image focusing evaluation value as the optimal exposure time.
According to the embodiment of the invention, the optimal exposure time is determined, and the image sequence is acquired based on the optimal exposure time, so that the technical problem of low image sequence precision caused by poor exposure time can be avoided, and the precision of the acquired image sequence is ensured.
The preset position in step S601 is near the focusing position.
In order to better implement the three-dimensional morphology model determining method in the embodiment of the present invention, correspondingly, as shown in fig. 7, on the basis of the three-dimensional morphology model determining method, the embodiment of the present invention further provides a three-dimensional morphology model determining apparatus, where the three-dimensional morphology model determining apparatus 700 includes:
an image sequence acquisition unit 701, configured to acquire an image sequence of a sample to be measured, where the image sequence includes a plurality of images of the sample to be measured with different focal planes;
a focus evaluation value determining unit 702 for determining a plurality of focus evaluation values of each pixel point in a plurality of sample images to be measured based on a focus evaluation operator;
an initial three-dimensional morphology model determining unit 703 configured to determine a focus level model based on the plurality of focus evaluation values, determine initial positions of the respective pixels based on the focus level model, and determine an initial three-dimensional morphology model based on the initial positions;
a confidence evaluation value determining unit 704 configured to determine a focus evaluation peak value based on the focus level model, and determine a confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value;
the target three-dimensional morphology model determining unit 705 is configured to correct the initial three-dimensional morphology model based on the confidence evaluation value, and obtain a target three-dimensional morphology model.
The three-dimensional morphology model determining apparatus 700 provided in the foregoing embodiment may implement the technical solution described in the foregoing three-dimensional morphology model determining method embodiment, and the specific implementation principle of each module or unit may refer to the corresponding content in the foregoing three-dimensional morphology model determining method embodiment, which is not described herein again.
As shown in FIG. 8, the present invention also correspondingly provides a three-dimensional topography measuring device 800. The three-dimensional topography measurement device 800 includes a processor 801, a memory 802, and a display 803. FIG. 8 illustrates only some of the components of the three-dimensional topography measurement device 800, but it should be understood that not all of the illustrated components need be implemented and that more or fewer components may alternatively be implemented.
The memory 802 may be an internal storage unit of the three-dimensional topography measurement device 800 in some embodiments, such as a hard disk or memory of the three-dimensional topography measurement device 800. The memory 802 may also be an external storage device of the three-dimensional topography measurement device 800 in other embodiments, such as a plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash memory Card (Flash Card) or the like provided on the three-dimensional topography measurement device 800.
Further, the memory 802 may also include both internal and external storage units of the three-dimensional topography measurement device 800. The memory 802 is used to store application software and various types of data for installing the three-dimensional topography measurement device 800.
The processor 801 may in some embodiments be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program code or processing data stored in the memory 802, such as the three-dimensional topography model determination method of the present invention.
The display 803 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 803 is used to display information at the three-dimensional topography measurement device 800 and to display a visual user interface. The components 801-803 of the three-dimensional topography measurement device 800 communicate with each other via a system bus.
In some embodiments of the present invention, when the processor 801 executes the three-dimensional morphology model determination program in the memory 802, the following steps may be implemented:
collecting an image sequence of a sample to be detected, wherein the image sequence comprises a plurality of sample images to be detected with different focal planes;
determining a plurality of focus evaluation values of each pixel point in a plurality of sample images to be tested based on a focus evaluation operator;
determining a focusing grade model based on the focusing evaluation values, determining initial positions of all pixel points based on the focusing grade model, and determining an initial three-dimensional morphology model based on the initial positions;
determining a focus evaluation peak value based on the focus level model, and determining a confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value;
and correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model.
It should be understood that: the processor 801, when executing the three-dimensional topography model determination program in the memory 802, may perform other functions in addition to the above functions, see in particular the description of the corresponding method embodiments above.
Further, the type of the three-dimensional topography measurement device 800 is not particularly limited in the embodiment of the present invention, and the three-dimensional topography measurement device 800 may be a portable three-dimensional topography measurement device such as a mobile phone, a tablet computer, a personal digital assistant (personaldigital assistant, PDA), a wearable device, a laptop computer (laptop), etc. Exemplary embodiments of portable three-dimensional topography measuring devices include, but are not limited to, portable three-dimensional topography measuring devices that are onboard IOS, android, microsoft or other operating systems. The portable three-dimensional topography measurement device described above may also be other portable three-dimensional topography measurement devices, such as a laptop computer (laptop) or the like having a touch sensitive surface (e.g., a touch panel). It should also be appreciated that in other embodiments of the invention, the three-dimensional topography measurement device 800 may not be a portable three-dimensional topography measurement device, but rather a desktop computer having a touch sensitive surface (e.g., a touch panel).
Correspondingly, the embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium is used for storing a computer readable program or instruction, and when the program or instruction is executed by a processor, the three-dimensional morphology model determining method steps or functions provided by the above method embodiments can be realized.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program stored in a computer readable storage medium to instruct related hardware (e.g., a processor, a controller, etc.). The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The three-dimensional morphology model determining method, the three-dimensional morphology model determining device and the computer tomography equipment provided by the invention are described in detail, and specific examples are applied to the principle and the implementation mode of the invention, and the description of the examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present invention, the present description should not be construed as limiting the present invention.

Claims (7)

1. A method for determining a three-dimensional morphology model, comprising:
collecting an image sequence of a sample to be detected, wherein the image sequence comprises a plurality of sample images to be detected with different focal planes;
determining a plurality of focus evaluation values of each pixel point in the plurality of sample images to be tested based on a focus evaluation operator;
determining a focus level model based on the plurality of focus evaluation values, determining an initial position of each pixel point based on the focus level model, and determining an initial three-dimensional morphology model based on the initial position;
determining a focus evaluation peak value based on the focus level model, and determining a confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value;
correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model;
the focusing evaluation operator is as follows:
Figure QLYQS_1
in the method, in the process of the invention,
Figure QLYQS_2
is a focus evaluation value; />
Figure QLYQS_3
The vertical texture information of the sample image to be detected after wavelet filtering; />
Figure QLYQS_4
The horizontal texture information of the sample image to be detected after wavelet filtering; />
Figure QLYQS_5
The diagonal texture information of the sample image to be detected after wavelet filtering; m is the total line number of the pixel points in the selected sample image to be detected; n is the total column number of the pixel points in the selected sample image to be detected; the I is an absolute value operation symbol;
the focus level model is:
Figure QLYQS_6
in the method, in the process of the invention,
Figure QLYQS_7
peak value of focusing grade model; />
Figure QLYQS_8
Is the initial position; />
Figure QLYQS_9
Is the mean value of the focusing grade model; />
Figure QLYQS_10
Standard deviation of the focus level model;
the determining a confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value includes:
determining a maximum focus evaluation value of the plurality of focus evaluation values;
determining an error of the maximum focus evaluation value and the focus evaluation peak value;
and determining a confidence evaluation value of each pixel point based on the error and the focus evaluation peak value.
2. The method of claim 1, wherein the confidence score is:
Figure QLYQS_11
in the method, in the process of the invention,
Figure QLYQS_12
the confidence evaluation value of the pixel point of the x row and the y column is obtained; />
Figure QLYQS_13
The maximum focus evaluation value and the focus evaluation peak error of the pixel point of the x-th row and the y-th column are obtained.
3. The method according to claim 1, wherein the correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model includes:
determining invalid pixel points of which the confidence evaluation values are smaller than a confidence evaluation threshold value in the initial three-dimensional morphology model;
and deleting the invalid pixel points to obtain the target three-dimensional morphology model.
4. The method of determining a three-dimensional morphology model according to claim 3, further comprising, after said deleting the invalid pixel point to obtain the target three-dimensional morphology model:
rasterizing the target three-dimensional morphology model to obtain a plurality of grid areas;
determining a center point of the grid region, and determining the distance between each pixel point in the grid region and the center point;
and determining an outlier pixel point with a distance larger than a distance threshold value in the grid area, and deleting the outlier pixel point to obtain the optimized three-dimensional morphology model.
5. The method of claim 1, further comprising, prior to said acquiring the image sequence of the sample to be measured:
placing the sample to be tested at a preset position;
acquiring a plurality of groups of exposure time, and acquiring a plurality of groups of sample exposure images to be detected corresponding to the plurality of groups of exposure time;
determining a plurality of image focus evaluation values of the plurality of groups of sample exposure images to be tested based on the focus evaluation operator;
and determining the maximum image focusing evaluation value in the plurality of image focusing evaluation values, and taking the exposure time corresponding to the maximum image focusing evaluation value as the optimal exposure time.
6. A three-dimensional morphology model determining apparatus, comprising:
the image sequence acquisition unit is used for acquiring an image sequence of a sample to be detected, wherein the image sequence comprises a plurality of sample images to be detected with different focal planes;
a focus evaluation value determining unit for determining a plurality of focus evaluation values of each pixel point in the plurality of sample images to be tested based on a focus evaluation operator;
an initial three-dimensional morphology model determining unit configured to determine a focus level model based on the plurality of focus evaluation values, determine an initial position of each of the pixel points based on the focus level model, and determine an initial three-dimensional morphology model based on the initial position;
a confidence evaluation value determining unit configured to determine a focus evaluation peak value based on the focus level model, and determine a confidence evaluation value of each of the pixels based on the plurality of focus evaluation values and the focus evaluation peak value;
the target three-dimensional morphology model determining unit is used for correcting the initial three-dimensional morphology model based on the confidence evaluation value to obtain a target three-dimensional morphology model;
the focusing evaluation operator is as follows:
Figure QLYQS_14
in the method, in the process of the invention,
Figure QLYQS_15
is a focus evaluation value; />
Figure QLYQS_16
The vertical texture information of the sample image to be detected after wavelet filtering; />
Figure QLYQS_17
The horizontal texture information of the sample image to be detected after wavelet filtering; />
Figure QLYQS_18
The diagonal texture information of the sample image to be detected after wavelet filtering; m is the total line number of the pixel points in the selected sample image to be detected; n is the total column number of the pixel points in the selected sample image to be detected; the I is an absolute value operation symbol;
the focus level model is:
Figure QLYQS_19
in the method, in the process of the invention,
Figure QLYQS_20
peak value of focusing grade model; />
Figure QLYQS_21
Is the initial position; />
Figure QLYQS_22
Is the mean value of the focusing grade model; />
Figure QLYQS_23
Standard deviation of the focus level model;
the determining a confidence evaluation value of each pixel point based on the plurality of focus evaluation values and the focus evaluation peak value includes:
determining a maximum focus evaluation value of the plurality of focus evaluation values;
determining an error of the maximum focus evaluation value and the focus evaluation peak value;
and determining a confidence evaluation value of each pixel point based on the error and the focus evaluation peak value.
7. A three-dimensional topography measurement device comprising a memory and a processor, wherein the memory is configured to store a program; the processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps in the three-dimensional morphology model determination method of any one of the preceding claims 1-5.
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