CN113137939A - Unpacking method based on binary characteristic pattern matching - Google Patents

Unpacking method based on binary characteristic pattern matching Download PDF

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CN113137939A
CN113137939A CN202110548214.2A CN202110548214A CN113137939A CN 113137939 A CN113137939 A CN 113137939A CN 202110548214 A CN202110548214 A CN 202110548214A CN 113137939 A CN113137939 A CN 113137939A
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CN113137939B (en
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达飞鹏
文浩
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Southeast University
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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
    • G01B11/25—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures by projecting a pattern, e.g. one or more lines, moiré fringes on the object
    • G01B11/254—Projection of a pattern, viewing through a pattern, e.g. moiré
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images

Abstract

The invention discloses an unwrapping method based on binary feature pattern matching. The method is different from the traditional binocular matching system, and the absolute phase of the measured object can be obtained only by one camera and one projector. The method needs to acquire the principal value phase of a standard plane, the fringe order corresponding to the principal value phase and a binarized feature image in advance. When the measured object is measured, the main value phase of the measured object and the main value phase of the standard plane are used for pre-matching, then the binary characteristic image modulated by the measured object and the binary characteristic image of the standard plane are accurately matched, and then the unmatched points are subjected to completion processing, so that the accurate absolute phase can be obtained. The method provided by the invention not only can correctly solve the stripe order aiming at the measuring conditions of a plurality of isolated objects, but also needs an additional picture to carry out the unpacking operation, thereby improving the speed of the unpacking while ensuring the robustness of the unpacking.

Description

Unpacking method based on binary characteristic pattern matching
Technical Field
The invention relates to an unwrapping method based on binary feature pattern matching, and belongs to the technical field of three-dimensional measurement.
Background
Optical three-dimensional measurement has become a research hotspot in the field of computer vision. The structured light has the advantages of high speed, high precision, wide measurement range, wide material adaptability and the like, is one of the most common optical three-dimensional measurement technologies, and is widely applied to the fields of manufacturing industry, industrial detection, medicine, man-machine interaction and the like. The phase-based three-dimensional reconstruction technique is widely used due to its high-precision characteristic, and to obtain the three-dimensional morphology of the object to be measured, it is necessary to obtain and expand the principal value phase of the object to be measured.
The method for acquiring the main value phase includes two methods, namely Fourier Transform Profilometry (FTP) and phase measurement profile (PSP), the PSP has the basic idea that a group of multiple grating fringe images with phase difference are used, a phase value corresponding to each pixel point in the group of images is calculated, and then height information of an object to be measured is acquired according to the phase value.
The unwrapping algorithm mainly comprises a space-based unwrapping algorithm and a time-based unwrapping algorithm, wherein the space-based unwrapping algorithm can obtain a faster unwrapping speed, but needs to solve the stripe order of an object with suddenly changing depth and a plurality of isolated objects difficultly, and the time unwrapping algorithm can accurately solve the stripe order of the object, but needs a longer measuring time.
In a three-dimensional reconstruction system, its matching typically occurs between two cameras or multiple cameras. Taking two cameras as an example, the matching process is a process in which one camera acquires a point in an image, and coordinates of the matching point in the image acquired by the other camera are obtained through an algorithm. Among the matching algorithms, there are mainly matching based on image gradation and matching algorithms based on image features. The matching algorithm based on the image gray scale mainly selects a sub-window area in an image acquired by one camera, takes the area as a template, and then calculates and searches the most similar sub-area with the same size in the image acquired by the other camera by utilizing a corresponding matching cost function. And the feature matching is to perform matching calculation on the geometric features of the object surface in the image acquired by the camera, so that the feature matching can only obtain the information of some feature matching points. The invention provides a unwrapping method based on binary characteristic pattern matching, which is used for carrying out pre-matching through a principal value phase and carrying out matching through binary characteristics, thereby realizing rapid and accurate absolute phase solving.
Disclosure of Invention
The technical problem is as follows: conventional spatial unwrapping algorithms are limited by the inability to phase unwrapp isolated objects, while temporal unwrapping algorithms are widely used due to their robustness. However, the time unwrapping algorithm generally needs a plurality of pictures to acquire the fringe order when the main value phase is unfolded, so that the measurement speed is reduced, and in order to meet the requirement of rapid measurement and not greatly reduce the measurement precision, the invention provides an unwrapping method based on binary characteristic pattern matching.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
an unwrapping method based on binary feature pattern matching comprises the following steps:
step 1: for better subsequent matching, a binary feature image is designed.
Step 2: nine pictures of a three-frequency method and a designed binary characteristic image are projected onto a standard plane to obtain a main value phase of the standard plane, a fringe order corresponding to the main value phase and a binary characteristic image.
And step 3: when a measured object is measured, a group of phase shift images and a binary characteristic image are projected to the surface of the measured object and collected, then a principal value phase solving algorithm is used for obtaining the principal value phase of the surface of the measured object, and a binary characteristic image is obtained.
And 4, step 4: and performing pre-matching by using the principal value phase information of the surface of the measured object and the principal value phase information on the standard plane to obtain matching candidate points corresponding to the points on the surface of the measured object.
And 5: and matching the candidate points by using the information of the binary characteristic image by using a matching algorithm to obtain the optimal matching point.
Step 6: and after the correct matching points are obtained, assigning the fringe orders corresponding to the matching points on the standard plane to the points corresponding to the surface of the measured object, namely obtaining the fringe orders corresponding to all the points on the surface of the measured object, and then unfolding the main value phase.
Further, the step 1 of designing the binary feature image comprises the following steps:
in order to make the characteristics of the binary characteristic picture unique, a speckle image is used for auxiliary design, and the formula of the speckle image is as follows:
Figure BDA0003074311320000021
where N represents the total number of simulated speckles, R represents the maximum diameter of the simulated speckles, and I0Intensity representing simulated speckle, i.e. the grey scale of the speckle image, (x)k,yk) Is the speckle center position coordinate. Then using a threshold value TsanbanThe method comprises the following steps of dividing the image into binary characteristic images, wherein a specific formula is as follows:
Figure BDA0003074311320000022
wherein, TsanbanThe segmentation threshold value for binarizing the speckle image determines the degree of uniqueness of the binary image feature, and is empirically set to 3.
Further, the step 2 of obtaining the principal value phase of the standard plane, the fringe order corresponding to the principal value phase, and the binarized feature image includes the following steps:
for nine pictures of three frequency method, the three-step phase shift pictures can be divided into three groups, wherein the formula of one group of pictures is shown as the following formula, and the formulas of the other two groups are except for
Figure BDA0003074311320000031
Except for the differences, the others are the same.
Figure BDA0003074311320000032
Wherein, (x, y) represents the coordinates of the pixel; i', (x, y) is called the mean intensity; i "(x, y) is called modulation intensity; i isn(x, y) represents the nth digital raster image;
Figure BDA0003074311320000033
called wrapped phase (wrapped phase), can be obtained by the following formula:
Figure BDA0003074311320000034
the three-frequency method is three groups of stripes with different wavelengths, and the formula and the phase-solving method of each group of stripes are consistent with the method. Thus for projection 3 sets of wavelengths λ1、λ2、λ3Of phase-shifted fringes of (1), where1<λ2<λ3And using the above formula to obtain three wrapped phases
Figure BDA0003074311320000035
Due to the fact that
Figure BDA0003074311320000036
The wavelength of (2) is minimum, so the precision is highest, and the wavelength is taken as the main value phase; then separately superposed using the heterodyne principle
Figure BDA0003074311320000037
And
Figure BDA0003074311320000038
to obtain a frequency of lambda12、λ23Phase of
Figure BDA0003074311320000039
Specifically, the formula is shown as follows:
Figure BDA00030743113200000310
Figure BDA00030743113200000311
Figure BDA00030743113200000312
Figure BDA00030743113200000313
then the frequency is lambda12、λ23To obtain a phase with only one period in the whole field range
Figure BDA00030743113200000314
Finally, the following formula is used for obtaining the phase of the main value
Figure BDA00030743113200000315
The corresponding fringe order.
Figure BDA00030743113200000316
Figure BDA00030743113200000317
k1(x,y)＝k12(x,y)+Round[(λ12/λ1)]×k123(x,y)
Characteristic binary image I for a standard plane acquired by a cameraf(x, y) binarization using the following formula
Figure BDA00030743113200000318
Wherein
Figure BDA00030743113200000319
For the binarized feature image, I' (x, y) is the average intensity of the acquired high-frequency phase shift map in the three-frequency method, and is obtained by using the following formula
Figure BDA0003074311320000041
Wherein I1(x,y)、I2(x,y)、I3And (x, y) are three images corresponding to the high-frequency phase shift diagram in the three-frequency method.
Further, the principal value phase and the binarized feature image of the surface of the measured object are obtained in step 3, and the obtaining method is the same as the method for obtaining the information on the standard plane in step 2.
Further, the step 4 of pre-matching the surface of the measured object with the point on the standard plane comprises the following steps.
And (3) carrying out pre-matching on the principal value phase of the standard plane and the principal value phase of the surface of the measured object obtained in the step (2) and the step (3). For a point (x) on the surface of the object to be measuredc,yc) Assuming that the phase of the principal value corresponding to the point is
Figure BDA0003074311320000042
Having a principal value phase on the same line on the standard plane and a principal value phase of the point
Figure BDA0003074311320000043
The same point, whose coordinate is (x)c,yi) N, these points are referred to as (x)c,yc) The candidate points of (1).
Further, the step 5 of matching the candidate points includes the following steps.
For these candidate points, the (2 × M +1,2 × N +1) region around the point is selected as a sub-region, where M represents the row parameters and N represents the column parameters. And then matching the point to be matched and the candidate point by using the following formula:
Figure BDA0003074311320000044
wherein Ic(x, y) and Is(x, y) respectively represent the image to be matched and the matched image, rho is the matching degree of the image, and the value range is [0,1 ]]Wherein the more ρ is close to 1, it represents Ic(x, y) and IsThe higher the correlation degree of the corresponding point in (x, y), i.e. the higher the correct matching of the two points. And comparing the matching degrees of the candidate points, and selecting the point with the highest matching degree as the corresponding matching point.
Has the advantages that: the invention provides an unwrapping method based on binary feature pattern matching. Compared with the traditional space unwrapping method, the method can not well solve the problem that the isolated objects are unwrapped, and the traditional time unwrapping algorithm needs to project a plurality of additional pictures to unwrapp.
Drawings
FIG. 1 is a schematic diagram of the overall algorithm of the present invention.
Fig. 2 is a schematic diagram of obtaining a binary feature image from a speckle image.
FIG. 3 is a diagram of the principal value phase and its fringe order of the etalon plane.
Fig. 4 is a schematic diagram of a feature image of a standard plane and an image after binarization.
Fig. 5 is a schematic diagram of a principal value phase and a binarization characteristic image of a surface of a measured object.
FIG. 6 is a graph showing the results of the present invention. The left hand side is the fringe order solved by the present invention, and the right hand side is the absolute phase solved by the fringe order.
Detailed Description
For a detailed description of the technical contents, construction features, objects achieved and effects thereof, the present invention will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, in order to solve the principal value phase unwrapping problem, the adopted technical scheme is an unwrapping method based on binary feature pattern matching, and the unwrapping method includes the following steps:
step 1: a characteristic binary image is designed by using a speckle image, and the speckle image formula is as follows:
Figure BDA0003074311320000051
where N represents the total number of simulated speckles, R represents the maximum diameter of the simulated speckles, and I0Intensity representing simulated speckle, i.e. the grey scale of the speckle image, (x)k,yk) Is the speckle center position coordinate. Then using a threshold value TsanbanThe method comprises the following steps of dividing the image into binary characteristic images, wherein a specific formula is as follows:
Figure BDA0003074311320000052
wherein, TsanbanFor the segmentation threshold value for binarizing the speckle image, which determines the unique degree of the binary image feature, the value is taken as 3 according to the experience, and the obtained result is shown in fig. 2.
Step 2: nine pictures of a three-frequency method and a designed binary characteristic image are projected onto a standard plane to obtain a main value phase of the standard plane, a fringe order corresponding to the main value phase and a binary characteristic image.
For nine pictures of three frequency method, the three-step phase shift pictures can be divided into three groups, wherein the formula of one group of pictures is shown as the following formula, and the formulas of the other two groups are except for
Figure BDA0003074311320000053
Except for the differences, the others are the same.
Figure BDA0003074311320000054
Wherein, (x, y) represents the coordinates of the pixel; i' (x, y) is referred to as mean intensity; i "(x, y) is called modulation intensity; i isn(x, y) represents the nth digital raster image;
Figure BDA0003074311320000055
called wrapped phase (wrapped phase), can be obtained by the following formula:
Figure BDA0003074311320000056
the three-frequency method is three groups of stripes with different wavelengths, and the formula and the phase-solving method of each group of stripes are consistent with the method. Thus for projection 3 sets of wavelengths λ1、λ2、λ3Of phase-shifted fringes of (1), where1<λ2<λ3And using the above formula to obtain three wrapped phases
Figure BDA0003074311320000057
Due to the fact that
Figure BDA0003074311320000058
The wavelength of (2) is minimum, so the precision is highest, and the wavelength is taken as the main value phase; then separately superposed using the heterodyne principle
Figure BDA0003074311320000061
And
Figure BDA0003074311320000062
to obtain a frequency of lambda12、λ23Phase of
Figure BDA0003074311320000063
Specifically, the formula is shown as follows:
Figure BDA0003074311320000064
Figure BDA0003074311320000065
Figure BDA0003074311320000066
Figure BDA0003074311320000067
then the frequency is lambda12、λ23To obtain a phase with only one period in the whole field range
Figure BDA0003074311320000068
Finally, the following formula is used for obtaining the phase of the main value
Figure BDA0003074311320000069
The corresponding fringe order. The phase of the principal value and its corresponding fringe order in the standard plane are obtained as shown in FIG. 3.
Figure BDA00030743113200000610
Figure BDA00030743113200000611
k1(x,y)＝k12(x,y)+Round[(λ12/λ1)]×k123(x,y)
Characteristic binary image I for a standard plane acquired by a cameraf(x, y), binarization was performed using the following formula, and the obtained result is shown in fig. 4.
Figure BDA00030743113200000612
Wherein
Figure BDA00030743113200000613
For the binarized feature image, I' (x, y) is the average intensity of the acquired high-frequency phase shift map in the three-frequency method, and is obtained by using the following formula
Figure BDA00030743113200000614
Wherein I1(x,y)、I2(x,y)、I3And (x, y) are three images corresponding to the high-frequency phase shift diagram in the three-frequency method.
And step 3: projecting a group of phase shift images and the binary characteristic images to the surface of the measured object when the measured object is measured, collecting the images, and then obtaining the principal value phase of the surface of the measured object by using the principal value phase solving algorithm
Figure BDA00030743113200000615
And acquiring a binarized feature image
Figure BDA00030743113200000616
The results are shown in FIG. 5.
And 4, step 4: for a point (x) on the surface of the object to be measuredc,yc) Assuming that the phase of the principal value corresponding to the point is
Figure BDA00030743113200000617
Having a principal value phase on the same line on the standard plane and a principal value phase of the point
Figure BDA00030743113200000618
The same point, whose coordinate is (x)c,yi) N, these points are referred to as (x)c,yc) The candidate points of (1).
And 5: for these candidate points, the (2 × M +1,2 × N +1) region around the point is selected as a sub-region, where M represents the row parameters and N represents the column parameters. And then matching the point to be matched and the candidate point by using the following formula:
Figure BDA0003074311320000071
wherein Ic(x, y) and Is(x, y) each representsThe image to be matched and the matched image, rho is the matching degree of the image, and the value range is [0,1 ]]Wherein the more ρ is close to 1, it represents Ic(x, y) and IsThe higher the correlation degree of the corresponding point in (x, y), i.e. the higher the correct matching of the two points. And comparing the matching degrees of the candidate points, and selecting the point with the highest matching degree as the corresponding matching point.
Step 6: after the correct matching points are obtained, the fringe orders corresponding to the matching points on the standard plane are assigned to the points corresponding to the surface of the measured object, that is, the fringe orders corresponding to all the points on the surface of the measured object can be obtained, then the main value phase can be expanded, and the fringe orders and the results after the main value phase is expanded refer to fig. 6.

Claims (6)

1. A unpacking method based on binary characteristic pattern matching is characterized by comprising the following steps:
step 1: designing a binary characteristic image;
step 2: projecting nine pictures of a three-frequency method and a designed binary characteristic image onto a standard plane to obtain a main value phase of the standard plane, a fringe order corresponding to the main value phase and a binary characteristic image;
and step 3: projecting a group of phase shift images and a binary characteristic image to the surface of a measured object when the measured object is measured, collecting the images, then obtaining main value phase information of the surface of the measured object by using a main value phase solving algorithm, and obtaining a binary characteristic image;
and 4, step 4: carrying out pre-matching by using the principal value phase information of the surface of the measured object and the principal value phase information on the standard plane to obtain matching candidate points corresponding to the points on the surface of the measured object;
and 5: matching the matching candidate points by using a matching algorithm according to the information of the binary characteristic image to obtain the optimal matching points;
step 6: and after the optimal matching point is obtained, assigning the fringe order corresponding to the matching point on the standard plane to the point corresponding to the surface of the measured object, namely obtaining the fringe order corresponding to all the points on the surface of the measured object, and then unfolding the main value phase.
2. The unwrapping method based on binary feature pattern matching according to claim 1, wherein: the specific method of the step 1 comprises the following steps: the speckle image is used for designing a binary characteristic image in an auxiliary mode, and the speckle image formula is as follows:
Figure FDA0003074311310000011
where N represents the total number of simulated speckles, R represents the maximum diameter of the simulated speckles, and I0Intensity representing simulated speckle, i.e. the grey scale of the speckle image, (x)k,yk) Is the speckle center position coordinate; then using a threshold value TsanbanThe method comprises the following steps of dividing the image into binary characteristic images, wherein a specific formula is as follows:
Figure FDA0003074311310000012
wherein, TsanbanA segmentation threshold for binarizing the speckle image.
3. The unwrapping method based on binary feature pattern matching according to claim 1, wherein: the specific method of the step 2 comprises the following steps: firstly, acquiring a main value phase of a standard plane; then, obtaining a fringe order corresponding to the main value phase by using a three-frequency method;
for nine pictures of three frequency method, the three-step phase shift pictures can be divided into three groups, wherein the formula of one group of pictures is shown as the following formula, and the formulas of the other two groups are except for
Figure FDA0003074311310000013
Except for the difference, the others are the same;
Figure FDA0003074311310000014
wherein, (x, y) represents the coordinates of the pixel; i' (x, y) is referred to as mean intensity; i "(x, y) is called modulation intensity; i isn(x, y) represents the nth digital raster image;
Figure FDA0003074311310000021
called wrapped phase (wrapped phase), can be obtained by the following formula:
Figure FDA0003074311310000022
the three-frequency method, i.e. three groups of stripes with different wavelengths, and the formula and the phase-solving method of each group of stripes are all consistent with the method, so that the projection 3 groups of the stripes with the wavelengths of lambda are in accordance with the method1、λ2、λ3Of phase-shifted fringes of (1), where1<λ2<λ3And using the above formula to obtain three wrapped phases
Figure FDA0003074311310000023
Due to the fact that
Figure FDA0003074311310000024
The wavelength of (2) is minimum, so the precision is highest, and the wavelength is taken as the main value phase; then separately superposed using the heterodyne principle
Figure FDA0003074311310000025
And
Figure FDA0003074311310000026
to obtain a frequency of lambda12、λ23Phase of
Figure FDA0003074311310000027
Specifically, the formula is shown as follows:
Figure FDA0003074311310000028
Figure FDA0003074311310000029
Figure FDA00030743113100000210
Figure FDA00030743113100000211
then the frequency is lambda12、λ23To obtain a phase with only one period in the whole field range
Figure FDA00030743113100000212
Finally, the following formula is used for obtaining the phase of the main value
Figure FDA00030743113100000213
The corresponding fringe order;
Figure FDA00030743113100000214
Figure FDA00030743113100000215
k1(x,y)＝k12(x,y)+Round[(λ12/λ1)]×k123(x,y)
finally, for the standard plane feature binary image I collected by the cameraf(x, y), binarizing using the following formula:
Figure FDA00030743113100000216
wherein
Figure FDA00030743113100000217
For a binarized characteristic image, I' (x, y) is the average intensity of a collected medium-high frequency phase shift image in a three-frequency method, and is obtained by using the following formula;
Figure FDA00030743113100000218
wherein I1(x,y)、I2(x,y)、I3And (x, y) are three images corresponding to the high-frequency phase shift diagram in the three-frequency method.
4. The unwrapping method based on binary feature pattern matching according to claim 1, wherein: the specific method of the step 4 comprises the following steps: pre-matching the principal value phase of the standard plane and the principal value phase of the surface of the measured object obtained in the step 2 and the step 3; for a point (x) on the surface of the object to be measuredc,yc) Assuming that the phase of the principal value corresponding to the point is
Figure FDA0003074311310000031
Which has n principal value phases on the same line on the standard plane and the principal value phase of the point
Figure FDA0003074311310000032
The same point, whose coordinate is (x)c,yi) N, these points are referred to as (x)c,yc) The candidate points of (1).
5. The unwrapping method based on binary feature pattern matching according to claim 1, wherein: the specific method of the step 5 comprises the following steps: for the candidate points, selecting a (2 × M +1,2 × N +1) region around the point as a sub-region, wherein M represents a row parameter, and N represents a column parameter; and then matching the point to be matched and the candidate point by using the following formula:
Figure FDA0003074311310000033
wherein Ic(x, y) and Is(x, y) respectively represent the image to be matched and the matched image, rho is the matching degree of the image, and the value range is [0,1 ]]Wherein the more ρ is close to 1, it represents Ic(x, y) and IsThe higher the correlation degree of the corresponding points in (x, y), i.e. the higher the correct matching of the two points; and comparing the matching degrees of the candidate points, and selecting the point with the highest matching degree as the corresponding matching point.
6. The unwrapping method based on binary feature pattern matching according to claim 2, wherein: t issanbanIs 3.
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