JP2017156197A - Measurement device and measurement method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a measurement device that is advantageous in terms of measurement accuracy.SOLUTION: A measurement device measuring a shape of a measured object 5 has a processing unit 100 that obtains information on a shape of a the measured object 5 having an identification unit for mutual lines provided on the basis of an image obtained by photographing the measured object 5 having pattern light including a plurality of lines projected. The processing unit 100 is configured to: acquire a location where luminance peaks or a location where a slope of a luminance distribution peaks from the luminance distribution in a direction crossing the line of an image; identify a location belonging to the pixel including the identification unit and a location excluding a location belonging to a pixel within a prescribed range from the pixel including the identification unit in a direction where the line extends and of the acquired location; and obtain the information on the shape of the measured object 5 on the basis of the identified location.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a measuring device and a measuring method.

  As an apparatus for measuring the shape of an object to be measured, an optical measurement apparatus using a pattern projection method is known. In the pattern projection method, a predetermined pattern is projected onto an object to be imaged, the pattern in the captured image is detected, and distance information at each pixel position is calculated from the principle of triangulation, thereby measuring the shape of the object to be measured. Seeking.

  As a typical pattern used in the pattern projection method, there is a pattern (dot line pattern) that alternately includes a bright line and a dark line and places a cutting point (dot) on the bright line or the dark line (Patent Document 1). ). When the dot line pattern is used, it is possible to identify which bright line on the mask pattern that is the pattern generation unit is the projected bright line (or dark line) from the coordinate information of the detected dot. Therefore, information about the shape of the entire object to be measured can be acquired by one imaging.

Japanese Patent No. 2517062

  The pattern is detected by, for example, detecting the peak of the brightness value of the captured image, the peak of the brightness gradient, and the like. The peak detection accuracy decreases due to the influence of noise and systematic errors included in the captured image. In particular, when a dot line pattern is used, an error may occur in the peak detection position near the dot. Due to this error, the measurement accuracy of the measurement object decreases.

  An object of the present invention is to provide a measurement device that is advantageous in terms of measurement accuracy, for example.

  In order to solve the above-described problem, the present invention is a measuring apparatus for measuring the shape of an object to be measured, in which pattern light including a plurality of lines provided with an identification unit for identifying each other line is projected. A processing unit that obtains information on the shape of the object to be measured based on an image obtained by imaging the object to be measured, and the processing unit has a luminance peak from a luminance distribution in a direction intersecting the line of the image. Or the position where the gradient of the luminance distribution reaches a peak, and among the acquired positions, the position belonging to the pixel including the identification unit and the pixel including the identification unit in the direction in which the line extends A position excluding a position belonging to a pixel in the range is specified, and information on the shape of the object to be measured is obtained based on the specified position.

  According to the present invention, for example, it is possible to provide a measurement device that is advantageous in terms of measurement accuracy.

It is the schematic which shows the structure of the measuring device as one Embodiment of this invention. It is a figure which shows an example of a dot line pattern. It is a figure which shows the image around a dot. It is a figure which shows the luminance distribution in each evaluation cross section. It is a figure which shows the relationship between the distance from a dot, and the systematic error amount of an edge, or the accidental error amount of a peak. It is a figure which shows the detail of an employment point determination part. It is a figure which shows the candidate of the selection method of an employment point. It is a figure which shows the number of adoption points corresponding to each candidate. It is a figure which shows the systematic error and accidental error in the to-be-tested surface of each candidate. It is a figure which shows the model fitting error of each candidate. It is a figure which shows the relationship between a to-be-tested surface area and the number of adoption points about each candidate. It is a figure which shows the relationship between a to-be-tested surface area and a model fitting error about each candidate.

  Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings.

  FIG. 1 is a schematic diagram showing a configuration of a measuring apparatus as one embodiment of the present invention. The measuring device measures the shape (for example, a three-dimensional shape, a two-dimensional shape, a position, and a posture) of the measurement object 5 using a pattern projection method. As shown in FIG. 1, the measurement device includes a projection unit 2, an imaging unit 3, and a processing unit 100.

  The projection unit 2 includes, for example, a light source unit 21, a pattern generation unit 22, and a projection optical system 23, and projects a predetermined pattern onto the measurement object 5. The light source unit 21 uniformly illuminates the pattern generated by the pattern generation unit 22 with light emitted from the light source (for example, Kohler illumination). The pattern generation unit 22 generates a pattern (pattern light) to be projected onto the object 5 to be measured. In this embodiment, the pattern generation unit 22 includes a mask on which a pattern is formed by chromium plating on a glass substrate. However, the pattern generation unit 22 may be configured by a DLP (Digital Light Processing) projector, a liquid crystal projector, or the like that can generate an arbitrary pattern. The projection optical system 23 is an optical system that projects the pattern generated by the pattern generation unit 22 onto the measurement object 5.

  FIG. 2 is a diagram illustrating a dot line pattern that is an example of a pattern that is generated by the pattern generation unit 22 and projected onto the measurement object 5. As shown in FIG. 2, the dot line pattern is a periodic line pattern (stripe pattern) in which bright portions BP formed by bright lines and dark portions DP formed by dark lines are alternately arranged in the X direction in the drawing. ). Further, dots DT are provided between the bright portions so as to cut the bright portions in the direction in which the bright portions extend on the bright line BP (Y direction orthogonal to the X direction). The dot DT is an identification unit for identifying each other's bright line. Since the positions of the dots are different on each bright line, an index indicating which line on the pattern generation unit 22 each projected bright line corresponds to is projected from the coordinate (position) information of the detected dot. Each bright line can be identified.

  Returning to FIG. 1, the imaging unit 3 includes, for example, an imaging optical system 31 and an imaging element 32, and captures an image of the object to be measured 5 and acquires an image. In this embodiment, the imaging unit 3 captures the object to be measured 5 on which the dot line pattern is projected, and acquires an image (distance image) including a portion corresponding to the dot line pattern. The imaging optical system 31 is an imaging optical system for imaging the dot line pattern projected on the measurement object 5 on the imaging element 32. The image pickup device 32 is an image sensor including a plurality of pixels for picking up an image of the measurement object 5 on which a pattern is projected, and is configured by, for example, a CMOS sensor or a CCD sensor.

  The processing unit 100 obtains the shape of the measurement object 5 based on the image acquired by the imaging unit 3. The processing unit 100 includes a control unit 101, an image storage unit 102, and a detection unit 103. The control unit 101 controls operations of the projection unit 2 and the imaging unit 3, specifically, projection of a pattern onto the measurement target 5, imaging of the measurement target 5 on which the pattern is projected, and the like. The image storage unit 102 stores the image acquired by the imaging unit 3. The detecting unit 103 uses the image stored in the image storage unit 102 to detect a bright line (measurement line) in the image and calculate the coordinates of the dots.

  The image sent from the image storage unit 102 to the detection unit 103 is subjected to a smoothing filter and is subjected to a process for reducing the difference in luminance value between the measurement line and the dot. This is to suppress the influence of the brightness value difference between the bright line and the dot on the image on the detection accuracy of the measurement line. In the processed image, the luminance distribution of the cross section in the direction intersecting the bright line (X direction in FIG. 2) is evaluated for each pixel coordinate in the bright line parallel direction (Y direction in FIG. 2), and the peak or edge is determined. To detect. In the present embodiment, for each coordinate in the Y direction, a total of three points are detected: a peak where the luminance is maximum, an edge where the gradient of the luminance distribution is maximum, and an edge where the gradient is minimum.

  Peaks or edges are connected in the X direction, and a measurement line is detected. The position of the dot is detected based on the luminance distribution in the X direction of the measurement line. Specifically, the zero-cross point is detected after processing such as applying a differential filter to the luminance distribution of the measurement line, and the coordinates of the zero-cross point are detected as the coordinates of the dots. In this embodiment, the coordinates of a total of three points are detected for one dot: a peak at which the luminance is a maximum value, an edge at which the gradient of the luminance distribution is a maximum, and an edge at which the luminance distribution is a minimum.

  The identification unit 104 included in the processing unit 100 identifies which bright line each dot belongs to based on the coordinates of the dots detected by the detection unit 103. At the time of identification, the position and inclination of the epipolar line in the pattern coordinate system are calculated for each dot. The position and inclination of the epipolar line are calculated by projecting a straight line extending in the line-of-sight direction from the selected dot position onto the coordinate system of the pattern. Since the position of the selected dot in the coordinate system of the pattern is on this epipolar line, it is possible to determine which bright line the selected dot belongs to by searching the pattern for the measurement line having the dot on the epipolar line. Identify. In the search, the evaluation function representing the consistency of identification is minimized. At that time, it is possible to use an algorithm of BP (Belief Propagation) or GC (Graph Cut).

  Further, in the present embodiment, three points are detected: a peak at which the luminance is maximum for each dot, an edge at which the gradient of the luminance distribution is maximum, and an edge at which the luminance is minimum. Identification may be performed in association with each other. Further, identification may be performed by imposing a constraint condition that each point belongs to the same dot.

The processing unit 100 further includes a detection point calculation unit 105. The detection point calculation unit 105 calculates the number of peaks and edges (detection points) existing in the area where the measurement object 5 exists in the image stored in the image storage unit 102. First, the detection point calculation unit 105 calculates the area of the region where the measurement object 5 exists in the image. For example, a Sobel filter is applied to the image, the contour information of the measurement object 5 is calculated, and the number of pixels corresponding to the area of the test surface of the measurement object 5 is calculated from the contour information. As an object to be measured 5, a 15 mm × 15 mm × 15 mm cube is considered, and it is assumed that a certain surface is photographed with an inclination of 45 degrees with respect to the imaging unit 3. The effective surface area of the imaged surface is 10.6 mm × 10.6 mm = 112.36 mm ² . When the pixel size per pixel is 150 μm × 150 μm, the dimension of the test surface is equivalent to 5000 pixels.

  If the repetition pitch of the bright part BP and the dark part DP in the bright line vertical direction (X direction) of the dot line pattern projected on the test surface is 8 pixels, detection of one peak and two edges in one cycle of the pattern Since this is a point, the detection point density is one point for every 2.7 pixels in the X direction. If the detection point density is the same in the Y direction, the number of detection points on the surface to be detected is 1875. The above is an example of the detection point calculation process performed by the detection point calculation unit 105.

  FIG. 3 shows an image in the vicinity of the dot DT when the dot line pattern is projected onto a certain reference plane. Here, an image was obtained by simulation. The horizontal axis X and the vertical axis Y of the image correspond to the position of the image pickup surface in the image pickup device 32, and also correspond to the respective axes in FIG. The coordinates of the detection points are calculated from optical image information (luminance distribution) in the evaluation section in the X direction perpendicular to the Y direction in which each line of the dot line pattern extends.

  FIG. 4 shows optical images (luminance distributions) in the X-direction evaluation section A that is not affected by the dot DT, the X-direction evaluation section B in the vicinity of the dot DT, and the X-direction evaluation section C that passes through the dot DT. 4 is the pixel position (pixel) of the image sensor 32, and the vertical axis is the luminance (arbitrary unit au). One pixel on the horizontal axis corresponds to 150 μm. In FIG. 4, in the optical image for each evaluation section, the peak position where the luminance is maximum (maximum) is indicated by a circle, and the edge position is indicated by a triangle.

  The peak position can be obtained by calculating an extreme value from the luminance distribution, and the edge position can be obtained by calculating the extreme value from a luminance gradient obtained by first-order differentiation of the luminance distribution. The edge position may be obtained as a position determined from an evaluation value (extreme value or reference value) obtained by evaluating the luminance gradient. The edge position can also be obtained by calculating a position that is a median value between the maximum value and the minimum value of luminance.

  According to FIG. 4, the edge positions in the respective evaluation sections are different from each other. For example, the difference between the edge position of the evaluation section A and the edge position of the evaluation section C is d. This is a phenomenon associated with the optical image becoming asymmetric due to the influence of dots, and is a so-called systematic error that always occurs as long as three-dimensional information is acquired at this position. In FIG. 4, the evaluation sections B and C affected by the dot DT have deteriorated contrast as compared with the evaluation section A in the X direction that is not affected by the dot DT. In addition to the above-described systematic errors, there are errors that vary depending on the measurement due to the random noise of the captured image, so-called accidental errors. This error increases as the contrast deteriorates. That is, in the evaluation cross section affected by the dot DT, the influence due to the chance error can be large. According to FIG. 4, the position shift due to the evaluation cross section such as the edge position is not observed at the peak detection position. However, if an accidental error occurs due to contrast degradation, a peak position shift can occur.

  FIG. 5 is a diagram showing the relationship between the distance from the dot and the systematic error amount of the edge or the accidental error amount of the peak. The horizontal axis indicates the distance on the pixel in the bright line parallel direction (Y direction). Also, the vertical axis represents each error amount, and represents an example of a calculation result of a 3σ value when detection is performed for a systematic error and detection is performed 20 times for a coincidence error. What is plotted with a circle is a systematic error when an edge point is detected, and what is plotted with a triangle is a chance error when a peak point is detected. It can be seen that both errors increase as the dots get closer. The accuracy information storage unit 106 included in the processing unit 100 stores information (detection accuracy information) as shown in FIG.

  As a method of suppressing the measurement error, it is conceivable that a detection point near the dot is not used for model fitting, or a detection error of the detection point near the dot is corrected. When selecting a detection point that is not used or correcting an error based on the distance from the dot, if the dot position cannot be detected accurately due to an accidental error, the selection or correction may be misjudged. . In the present embodiment, as described below, detection points that are not used for model fitting are selected in units of pixels (150 μm × 150 μm) from the dot, so that they are selected in the case of selecting in terms of distance from the dot (unit: μm). However, the dot position detection accuracy is not required.

  The detected point number information obtained by the detected point number calculation unit 105 and the detection accuracy information (FIG. 5) stored in the accuracy information storage unit 106 are sent to the adopted point determination unit 107 to determine points used later for model fitting. Is done. FIG. 6 is a diagram showing details of the adoption point determination unit 107. Employment point determination unit 107 includes adoption point candidate storage unit 400, comparison unit 403, and error calculation unit 402.

  Accidental errors can affect the accuracy of detection points in the X direction. Therefore, it is conceivable to use a range in the bright line parallel direction (Y direction) starting from the dot as a criterion for determining the adoption point. FIG. 7 is a diagram illustrating a method for selecting recruitment points. When the candidate (A) in FIG. 7 does not delete the detection point, the candidate (B) in FIG. 7 deletes the detection point included in the center pixel of the dot, and the candidate (C) in FIG. When all the pixels are deleted, the candidate (D) in FIG. 7 is a case where the pixels adjacent to the dots are also deleted. In the present embodiment, it is assumed that dots exist over three pixels in the Y direction. The coordinate information of the dot position applies a differential filter to the luminance distribution of the measurement line, and detects the zero-cross point coordinates as the position coordinates of the feature portion. In the present embodiment, the center pixel among the three dots is detected as the dot coordinates. Further, one peak and two edges are detected for each pixel. In FIG. 7, the peak is indicated by a circle and the edge is indicated by a triangle. The detection point of the center pixel of the dot is indicated by a square.

  The adoption point candidate storage unit 400 stores candidates for the adoption point selection method shown in FIG. 7, and also uses the detection point number information obtained by the detection point calculation unit 105 to determine the number of adoption points for each candidate. . As shown in FIG. 2, the interval between the dots with respect to the bright line parallel direction (Y direction) changes randomly within the test surface. In this embodiment, the minimum interval is 6 pixels, the maximum interval is 10 pixels, and the average interval is Equivalent to 8 pixels. The number of points adopted for each candidate in this case is shown in FIG. 8A to 8D correspond to FIGS. 7A to 7D. As shown in FIG. 8, the candidate (A) has 1875 points, the candidate (B) has 1688 points, the candidate (C) has 1125 points, and the candidate (D) has points. 750 points. The score information including the position information of each point is sent from the recruitment point candidate storage unit 400 to the comparison unit 403.

  The comparison unit 403 compares the score information with a predetermined threshold value, and sends information related to the adoption point candidate exceeding the threshold value to the error calculation unit 402. In the present embodiment, the minimum model score information stored in the model information storage unit 111 included in the processing unit 100 is used as a threshold value. In model fitting, fitting is performed by associating model points on the model with detection points where distance information is detected. Therefore, the number of detected points needs to be larger than that of model points. If the number of detected points is significantly smaller than model points, model fitting cannot be performed or a large position / orientation calculation error may occur. In the present embodiment, the minimum model score is 800 points.

  According to the score information shown in FIG. 8, the number of adopted points in the case of the candidate (D) is below the threshold value of 800 points. Therefore, the adoption points of candidate (A), candidate (B), and candidate (C) are sent to error calculation section 402.

  The error calculation unit 402 calculates an assumed amount of error at the time of model fitting from the score information by each candidate and the detection accuracy information sent from the accuracy information storage unit 106. FIG. 9 is a diagram showing systematic errors and chance errors in each candidate test surface. For each error, an in-plane double average square root (RMS) of the error is calculated in a sufficiently wide region so that locality for each candidate does not appear. In addition, the effects of both edge point detection and peak point detection are taken into account for both accidental errors and systematic errors. As shown in FIG. 5, since the error amount of the systematic error decreases rapidly at a position away from the dot, the error greatly decreases as the non-adopted region is expanded from (A) to (C). On the other hand, since the chance error has a relatively small dependence on the distance from the dot, even if the non-adopted area is expanded from (A) to (C), the improvement effect of the systematic error is not obtained.

  A value obtained by adding the point information shown in FIG. 8 to the error shown in FIG. 9 is an estimated amount of accuracy after model fitting for each candidate. When the number of distance points associated with a model in model fitting is n, the model fitting error is the detection point accuracy multiplied by the square root of n. FIG. 10 shows the result of normalization after calculating the square sum of the coincidence error and systematic error shown in FIG. 9 as the model fitting error and multiplying it by the square root of the number of detection points shown in FIG. According to this result, the model fitting error of the candidate (C) is minimized, and is selected as the final adoption point determination criterion.

  The determination criterion thus selected is sent to the detection point selection unit 108 in the processing unit 100. The detection point selection unit 108 performs selection (specification) of points used in model fitting based on the determination criterion of the candidate (C) and the detection point information detected by the detection unit 103. That is, in addition to the center pixel of the dot, measurement points included in a pixel one pixel away from the center pixel of the dot in the direction parallel to the bright line are excluded. Next, the distance information calculation unit 109 in the processing unit 100 uses the detection point information selected (specified) by the detection point selection unit 108 and the index (number, etc.) of each measurement line identified from the dot information. Then, distance information (three-dimensional information) of the measurement object 5 at each pixel is calculated from the principle of triangulation.

  Finally, the position / orientation calculation unit 110 in the processing unit 100 performs model fitting based on the three-dimensional information obtained by the distance information calculation unit 109 and the model information stored in the model information storage unit 111, and Calculate the position and orientation.

  As described above, the measurement apparatus having the configuration of the present embodiment can suppress the influence of the measurement accuracy of the measurement object due to the measurement error of the detection point in the vicinity of the dot, regardless of the measurement accuracy of the dot position. As described above, according to the present embodiment, it is possible to provide a measurement device that is advantageous in terms of measurement accuracy.

Next, a measurement method that takes into account the dimensions of the test surface will be described. FIG. 11 is a diagram showing the relationship between the test surface area and the number of points employed for each candidate. 800 points which are threshold values for the number of points adopted are indicated by dotted lines in the figure. As shown in FIG. 11, for any candidate, the number of points adopted is proportional to the area of the test surface. When compared with the threshold value, only the candidate (A) exceeds the threshold value until the surface area to be tested is 53.3 mm ² . Moreover, beyond the test surface area of up to 80.0 mm ² candidate (A) or candidate (B) the threshold, until 120.0Mm ² exceed 80.0 mm ² candidate (A), (B) or ( If C) exceeds the threshold and exceeds 120.0 mm ² , all candidates exceed the threshold.

FIG. 12 is a diagram showing the relationship between the test surface area and the model fitting error for each candidate. As shown in FIG. 11, candidates (A) are selected until the surface area to be examined is 53.3 mm 2. When the test area is 53.3 mm ² to 80.0 mm ^2, the error (accuracy) when the candidate (A) is selected is compared with the error (accuracy) when the candidate (B) is selected. Is selected (in this embodiment, (B)). Similarly, the candidate (C) is selected when the surface area to be examined is from 80.0 mm ² to 120.0 mm ^2, and the candidate (D) is selected when it is larger than 120.0 mm ² . The model fitting error due to the selected candidate is indicated by a solid line in FIG.

As described above, by changing the candidate to be selected according to the area of the test surface, it is possible to cope with measurement of test objects of various sizes. For example, in the case of a small work having a surface area of 53.3 mm ² or less, if (D) is selected from candidates (B), the number of distance points used for model fitting may be less than that of model points, and thus position and orientation cannot be estimated. . Or, the model fitting error may increase significantly. However, in the adopted point selection method of the present embodiment, a candidate that can always obtain the number of distance points necessary for model fitting can be selected.

Further, for example, a case where the candidate (A) is adopted and a case where the candidate (D) is adopted are compared in a large workpiece having a surface area of 120 mm ² or more. In the case of 200 mm ² which is the maximum test surface area shown in FIG. 9, adopting the candidate (D) results in an error amount of 45% compared to the case of adopting the candidate (A).

  As described above, by assuring the number of detection points necessary for model fitting and excluding detection points that can be a cause of deterioration in measurement accuracy, a decrease in measurement accuracy can be suppressed. In the above-described embodiment, after the measurement line is identified, the adopted point range is determined before calculating the distance information. However, after the measurement line is identified and the distance information is calculated, the same as the above is performed. It is also possible to determine the adoption point range and not adopt the distance information.

  The dot is necessary for identifying the measurement line, but after being used for identification, it is included in the measurement point included in the pixel of the dot or in the surrounding pixels until the final three-dimensional measurement result is derived. It is possible to arbitrarily decide at which stage the measurement point is not adopted.

  In the above-described embodiment, the threshold value is the minimum model score information stored in the model information storage unit 110. This threshold value may be determined based on the number of detection points obtained when measuring a workpiece having the smallest test surface among the workpieces to be measured by the measuring apparatus. As described above, the accuracy of the measuring device is determined by the detection accuracy of each point and the number of detection points. When detection points are not deleted, or when detection points are similarly deleted regardless of the workpiece size, the number of detection points increases as the workpiece size increases, so the measurement accuracy improves. In other words, the accuracy of the device is limited by the measurement accuracy of the minimum test surface of the workpiece to be measured, and when measuring a workpiece larger than that, the accuracy of detection at each point should be equal to or greater than the above threshold score. In this case, the measurement accuracy will not be worse than the guaranteed value.

  In the above embodiment, the number of detection points is calculated based on the contour information obtained from the image. However, when the distance point group information is obtained first, the detection area is obtained from the inclination information of the test surface obtained from the information and the measurement object information. You may calculate based on. Further, in the above-described embodiment, the number of detection points is derived based on the actual measurement image. However, the number of detection points is derived based on the estimated minimum number of detection points based on the measurement object information and the maximum inclination angle information of the object surface to be imaged. May be. In this case, an assumed number of detection points smaller than the actually detected number of detection points is compared with a threshold value, and the non-adopted pattern is not switched in view of actual measurement conditions. However, in addition to the fact that the pattern is switched by a simple method, it is not affected by various detection errors such as errors in contour information that can occur in the above-described determination method.

  In the above embodiment, the model is described assuming that a three-dimensional model is used to obtain the position and orientation measurement result. For example, in order to obtain the surface information of a certain test surface, it is sufficient if there is a two-dimensional model of the plane, and even in this case, the selection method of this embodiment is effective.

  Note that the pattern generated by the pattern generation unit 22 and projected onto the measurement object 5 is not limited to the pattern shown in FIG. 2 but may be a pattern in which light and dark are reversed. The pattern generated by the pattern generation unit 22 and projected onto the measurement object 5 is not limited to a bright part and a dark part, and may be a pattern including a plurality of lines such as a gradation pattern and a multicolor pattern. The line may be a straight line or a curved line. In addition, the identification unit is not limited to a dot, and may be any code that can identify each line, such as a round shape or a portion with a narrow width. The position where the dot is provided may be on the bright part or on the dark part.

  As mentioned above, although embodiment of this invention has been described, this invention is not limited to these embodiment, A various change is possible within the range of the summary.

2 Projection unit 3 Imaging unit 5 Object to be measured 100 Processing unit

Claims (7)

A measuring device for measuring the shape of an object to be measured,
Information on the shape of the object to be measured is obtained based on an image obtained by imaging the object to be measured on which pattern light including a plurality of lines provided with an identification unit for identifying each other line is projected. A processing unit,
The processor is
From the luminance distribution in the direction intersecting the line of the image, obtain a position where the luminance is a peak, or a position where the gradient of the luminance distribution is a peak,
Among the acquired positions, in the direction in which the line extends, the position belonging to the pixel including the identification unit and the position excluding the position belonging to a pixel in a predetermined range from the pixel including the identification unit, A measuring apparatus characterized in that information on the shape of the object to be measured is obtained based on the specified position.   The measuring apparatus according to claim 1, wherein the predetermined range is determined so that the number of the specified positions satisfies a number necessary for obtaining the information.   The measuring apparatus according to claim 2, wherein the necessary number is determined based on a size or a shape of the object to be measured.   The measuring apparatus according to claim 1, wherein the predetermined range is a range that satisfies the required number and has a small accuracy when the information is obtained. The processor is
Of the position where the luminance reaches a peak or the position where the gradient of the luminance distribution reaches a peak, the position belonging to the pixel including the identification unit, and the pixels in the first range in the direction in which the line extends from the pixel including the identification unit First accuracy of the information on the shape of the measurement object obtained based on the position excluding the position belonging to
Of the position where the luminance reaches a peak or the position where the gradient of the luminance distribution reaches a peak, the position belonging to the pixel including the identification unit, and the first range in the direction in which the line extends from the pixel including the identification unit Calculates a second accuracy of the information on the shape of the measurement object obtained based on a position excluding a position belonging to a pixel in a different second range,
5. The measuring apparatus according to claim 1, wherein the predetermined range is determined by comparing the calculated first accuracy and second accuracy. 6. A projection unit that projects the pattern light onto the measurement object;
An imaging unit that captures an image of the object to be measured on which the pattern light is projected;
The measurement apparatus according to claim 1, wherein the processing unit obtains information on a shape of the measurement object based on the image acquired by the imaging unit. Measurement for obtaining information on the shape of the object to be measured based on an image obtained by imaging the object to be measured on which pattern light including a plurality of lines provided with an identification unit for identifying each other line is projected A method,
From the luminance distribution in the direction intersecting the line, obtain the position where the luminance is the peak, or the position where the gradient of the luminance distribution is the peak,
Among the acquired positions, in the direction in which the line extends, the position belonging to the pixel including the identification unit and the position excluding the position belonging to a pixel in a predetermined range from the pixel including the identification unit,
Information on the shape of the object to be measured is obtained based on the specified position.

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