JP2007218922A - Image measuring device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide image measuring device designed so as to automatically and efficiently achieve an entire process from locating operation to three-dimensional measurement with high degree of accuracy, and moreover, without having to choose the measuring object to be portable. <P>SOLUTION: This device is equipped with a rough locating section which roughly measures positional relationship among measuring objects in each image from paired images of measuring objects photographed in different directions; data setting section which defines one image as reference image and the other image as search image out of the paired images, sets reference data block on the reference image from the above positional relation obtained by the rough locating section, and then provides search area on the search image to set search data block in the above search area; and association determining section for obtaining associated relation between the search data block and reference data block set in the search area. The data setting section implements at least one setting among setting of reference data block on the reference image, setting of search area on the search image and setting of search data block on the search image, from the positional relation obtained by the rough locating section. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a technique for automating three-dimensional measurement by automating a pre-stage orientation work when performing three-dimensional measurement from different camera positions and automatically acquiring an initial value of stereo matching.

  When performing three-dimensional measurement in ground photogrammetry, measurement is performed according to the flow shown in FIG. That is, it is necessary to perform stereo imaging (a) → position detection (b) → location (c) → stereo model formation (d) → three-dimensional measurement (e) of an object. These processes are mainly performed by a computer. Among them, position detection (b) and three-dimensional measurement (e) are conventionally performed manually. The position detection (b) is a pre-processing for orientation that obtains the position and tilt of the camera to be photographed.

  By obtaining the positional relationship between the camera and the measurement object by the orientation (c), a stereo model that can be stereoscopically viewed can be formed, and three-dimensional measurement can be performed. The process of position detection (b) for performing orientation (c) is an operation for obtaining coordinate positions on each of the corresponding six or more points captured in different cameras.

  There are two types of three-dimensional measurement (e): point measurement and surface measurement.

  In the case of point measurement, the measurement points on the object are manually measured through human hands, but in order to achieve automation and improve accuracy, work is performed to mark the object.

  In the case of surface measurement, it is automatically performed using a technique called stereo matching by image processing. At that time, initial settings such as determination of a template image and setting of a search width have to be performed manually.

  The position detection (b) is performed by selecting six or more measurement points on the object in the left and right images taken by the operator, and linking the measurement points on the object and coordinate positions while observing each image. The detection is performed. However, these operations need to be performed while stereoscopically viewing, and thus require skill, and are complicated, difficult, and difficult.

  In particular, the task of determining measurement points, associating left and right images, and detecting detailed position coordinates is likely to cause individual differences, and it is difficult to obtain sufficient accuracy for each person. In addition, some people could not measure. In order to avoid such a problem, processing such as attaching a mark to an object may be performed.

  However, the increase in the work of attaching a mark to the object itself is a minus, and since there are some objects that cannot be easily attached with a mark, this method is not so popular.

  On the other hand, there is also a technique in which two cameras are firmly fixed on a stereo carriage and three-dimensional measurement is performed without performing an orientation work.

  However, in that case, the positional relationship between the two cameras on the carriage must never come, and the measurement environment and measurement object are greatly limited. At the same time, such a device is large, heavy, difficult to carry, and expensive, so that the three-dimensional measurement method by this method is not very popular.

  Further, in the point measurement of the three-dimensional measurement (e), when a mark or the like is not pasted on the measurement object, it is necessary for the operator to instruct and measure the measurement point while observing the photographed image. . For this reason, when there were many measurement points, it took time and effort. In addition, when trying to measure with high accuracy, individual differences are inevitably caused and there is a situation where measurement is impossible.

  The above situation can be avoided if the mark is attached to the object and measured, but in that case, as described above, a new time and effort is required to attach the mark, and it may be difficult to attach the mark depending on the object. Sometimes it was impossible to measure.

  In the case of surface measurement, it is necessary to manually determine a template image when performing automatic measurement by stereo matching as a preprocessing, and determine an optimal search width. Furthermore, if there is a mismatching point or the like, it is necessary to correct it manually, which makes it difficult to automate after all.

  SUMMARY OF THE INVENTION In view of the problems of the prior art, the present invention provides an image measuring apparatus having a simple configuration that can be carried out efficiently and with high accuracy from the orientation work to the measurement, can be carried regardless of the measurement object. The purpose is that.

  Examples of the solution of the present invention are as described in the claims.

  According to the present invention, the positional relationship of the measurement object in each image is roughly determined from a pair of images obtained by photographing the measurement object from different directions, and one image is the reference image and the other image is the pair of images. Is determined as a search image, a reference data block is set in the reference image based on the positional relationship obtained by the approximate position measurement unit, a search area is set in the search image, a search data block is set in the search area, and the search is performed. In determining the correspondence between the search data block set in the area and the reference data block, the reference data block is set in the reference image, the search area is set in the search image, and the search data based on the approximate positional relationship. By configuring so that at least one of the block settings is performed, the correspondence can be accurately obtained.

  The image measurement apparatus of the present invention includes a rough position measurement unit that roughly obtains a positional relationship of a measurement object in each image from a pair of images obtained by photographing the measurement object from different directions, and one image in the pair of images. Is set as a reference image, the other image is set as a search image, a reference data block is set in the reference image based on the positional relationship obtained by the approximate position measurement unit, a search area is provided in the search image, and search data is set in the search area. A data setting unit for setting a block; and a correspondence determining unit for obtaining a correspondence relationship between a search data block set in the search region and a reference data block, and the data setting unit is obtained by the approximate position measurement unit. Configured to perform at least one of the setting of the reference data block to the reference image, the setting of the search area to the search image and the setting of the search data block based on the determined positional relationship It is characterized in that was.

  The pair of images includes a feature pattern image whose positional relationship is known in advance, and the approximate position measurement unit includes the position of the feature pattern image in the reference image and the feature pattern image in the search image. Based on the positional relationship, the positional relationship of the measurement object in each of the pair of images can be roughly determined.

  The data setting unit can be configured to set a search area range in the search image based on the position of the feature pattern image in the search image.

  The data setting unit includes a search area in the search image based on a positional relationship between the position of the feature pattern image and the position of the search area in the search image, or the position of the feature pattern image in the reference image and the position of the reference data block. It is possible to configure so as to set the range.

  The data setting unit includes a search area in the search image based on a positional relationship between the position of the feature pattern image and the position of the search area in the search image, or the position of the feature pattern image in the reference image and the position of the reference data block. Can be configured to set the size of the range.

  The data setting unit is configured to increase the width or area of the search area range of the search image as the feature pattern image and search area in the search image are separated from the feature pattern image and reference data block in the reference image. Can be configured.

  The data setting unit may be configured to set a plurality of search areas having different sizes.

  The data setting unit can be configured to set a reference data block in the reference image based on the position of the feature pattern image in the reference image.

  The data setting unit can be configured to set the size of the reference data block in the reference image based on the position of the feature pattern image in the reference image and the reference data block position.

  The data setting unit can be configured to increase the height or width of the reference data block in the reference image as the position of the feature pattern image in the reference image and the reference data block position are separated.

  The data setting unit can be configured to set a plurality of reference data blocks having different sizes based on a feature pattern image in the reference image.

  First, the outline of the image measuring apparatus according to the present invention will be described with reference to FIG.

  The image measuring device 30 includes an approximate position measuring unit 31, a data setting unit 32, and a correspondence determining unit 33.

  The approximate position measurement unit 31 roughly obtains the positional relationship of the measurement object in each image from a pair of images obtained by photographing the measurement object from different directions, using a feature pattern image whose positional relationship is known in advance. It is configured.

  The data setting unit 32 determines one of the pair of images as a reference image and the other as a search image, sets a reference data block in the reference image based on the positional relationship obtained by the approximate position measurement unit 31, and searches the search image. It is configured to set the area.

  The correspondence determination unit 33 is configured to obtain a correspondence relationship between the search data block set in the search area and the reference data block.

  By using this image measuring device 30, it is possible to automate the three-dimensional measurement described in detail below and improve the measurement accuracy.

  Hereinafter, a shape measuring apparatus using the image measuring apparatus of the present invention will be described in detail.

  FIG. 1 shows a flow of measurement in the shape measuring apparatus, and FIG. 2 is a block diagram conceptually showing the configuration.

a. Stereo shooting of an object First, a feature pattern is applied to the measurement object 0 from the feature pattern projection unit 3 according to an instruction from the controller 4. Then, the image of the measurement object 0 is taken by the left and right image taking units 1 and 2, and the image data is transferred to the feature pattern extracting unit 5.

  As shown in FIG. 3, the left and right image capturing units 1 and 2 include an optical system 11, a CCD 12, a CCD driver 13, an operational amplifier 14, an A / D converter 15, and the like.

  5 and 6 show examples of feature patterns. These feature patterns 20 are formed in a circular shape, but may have any shape other than a circle as long as the position of the mark image by the feature pattern projection is required.

  The feature pattern projection unit 3 may be anything that can project the feature pattern 20, such as a slide projector or a laser pointer.

  FIG. 4 is a detailed block diagram of the feature pattern extraction unit 5.

  Image data transferred by the left and right image capturing units 1 and 2 is converted into digital data by the A / D converter 15 of FIG. 3 and transferred to the feature pattern projection image memory 51 of the feature pattern extraction unit 5.

  Next, in response to an instruction from the controller 4, the feature pattern projection is stopped, the left and right image capturing units 1 and 2 capture an image without a feature pattern, and the feature pattern extraction unit 5 stores the digital image Transfer data.

  When the image transfer to the feature pattern projection image memory 51 and the feature pattern absence image memory 52 is completed, the two images are differentiated through the image difference calculator 53 in accordance with an instruction from the controller 4. Then, the difference image is taken into the feature pattern image memory 54.

  As a result, the data in the feature pattern image memory 54 is only information obtained by deleting the image information of the measurement object 0, that is, information on the feature pattern 20 (mark image data).

b. In order to perform the position detection orientation processing, the position of the mark image is detected by the feature pattern projection.

  The feature pattern position detection unit 55 detects the feature pattern coordinate position in the feature pattern image memory 54.

  The feature pattern 20 may have any shape as long as its position is clearly known, but here, the feature pattern 20 as shown in FIGS. 5 and 6 is assumed. Further, any number of feature patterns may be used as long as there are 6 or more feature patterns as shown in FIGS.

  Since the feature pattern image memory 54 contains no information other than the mark image obtained by the feature pattern projection, erroneous detection can be eliminated. Furthermore, since it becomes easy to perform position detection automatically, stable position coordinate detection can always be performed with high accuracy without individual differences.

  Here, a case will be described in which the template matching method is used for the approximate position detection of points and the moment method is used for detailed position detection.

  As the template matching method, a residual sequential test method (SSDA method), which is a kind of correlation method, will be described, but a normalized correlation method or the like may be used. For detailed position detection, the LOG filter method or the like may be used instead of the moment method.

  Hereinafter, the position detection process will be described.

(Rough position detection)
1. Register the template image.

  As the template image, a simulation image similar to one mark of the projection feature pattern 20 of FIGS. 5 and 6 may be created, or an actual image may be selected and used.

  2. A point where S> R (a, b) is searched in the entire image (see Equation 1).

  The closer R (a, b) is to 0, the higher the degree of similarity. S is determined in advance with an appropriate value. In this case, since image information other than the feature pattern has been erased, it can be easily determined. A normalized correlation method or the like may be used for template matching, but the processing can be further speeded up by using a residual sequential test method.

[Residual sequential test (SSDA method)]
The principle diagram of the SSDA method is shown in FIG.

The point where the residual R (a, b) is minimized is the mark position to be obtained.

  In the addition of the expression, if the value of R (a, b) exceeds the past residual minimum value, the addition is aborted, and the calculation process is performed to move to the next (a, b), thereby speeding up the processing. Can measure.

  3. R (a, b) The mark position of the projection feature pattern is determined by the number of marks according to conditions such as the minimum and the interval between adjacent mark positions, and set as position coordinates.

(Detailed position determination)
1. Search region determination For the point determined by the above-described (approximate position detection), a search region centered on the point is set.

2. Determination of Mark Area For the search area, an area having a density value equal to or higher than a threshold is set as one mark (see FIG. 8).

  As the threshold value, an appropriate value is determined in advance. The image is almost 0 because the difference processing is applied except for the mark.

3. Center of gravity position calculation The moment method is used to calculate the center of gravity position coordinates.

[Moment method]
As shown in FIG. 8, the following equation is applied to points that are equal to or higher than the threshold value T (mark K).

  The center of gravity position can be calculated up to the sub-pixel position by Expression 2 and Expression 3.

  4). Perform steps 1-3 for the number of points.

  Thus, the position coordinates of the mark image by the feature pattern projection as shown in FIG. 5 or FIG. 6 can be calculated.

  Detailed position detection may be performed from the beginning without performing approximate position detection, or position detection may be performed by another algorithm.

  In any case, since the image has only feature pattern information, the position can be calculated at high speed and with high accuracy.

c. Orientation Next, the coordinate values obtained by the feature pattern position detection unit 55 are sent to the posture calculation unit 6 to perform orientation calculation.

  By this calculation, the positions of the left and right cameras are obtained.

These parameters are obtained by the following coplanar conditional expression.

  The origin of the model coordinate system is taken as the left projection center, and the line connecting the right projection centers is taken as the X axis. For the scale, the base line length is taken as the unit length. The parameters to be obtained at this time are 5 of the left camera Z-axis rotation angle κ1, the Y-axis rotation angle φ1, the right-hand camera Z-axis rotation angle κ2, the Y-axis rotation angle φ2, and the X-axis rotation angle ω2. One rotation angle. In this case, the X-axis rotation angle ω1 of the left camera is 0, so it is not necessary to consider.

Under such conditions, the coplanar conditional expression of Expression 4 becomes Expression 5, and each parameter can be obtained by solving this expression.

Here, the following relational expression for coordinate transformation is established between the model coordinate system XYZ and the camera coordinate system xyz.

  Using these formulas, the unknown parameters are obtained by the following procedure.

1. The initial approximate value is usually 0.

2. The coplanar conditional expression 5 is Taylor-expanded around the approximate value, and the value of the differential coefficient when linearized is obtained by Expressions 6 and 7, and an observation equation is established.

3. The least square method is applied to obtain a correction amount for the approximate value.

4). Correct approximate values.

5. Using the corrected approximate value, the above 2. ~ 5. Repeat until the operation is converged.

  If some mark is in the shape of the object and the position is not detected well, it may not converge. In that case, one point is deleted and the above steps 1 to 5 are performed, and the converged parameter or the best parameter is used.

d. Stereo model formation Next, an image is converted into a stereo model coordinate system that can be viewed stereoscopically according to parameters determined by orientation, and a stereo model is formed.

The conversion formula to model coordinates is as follows.

  In this way, three-dimensional measurement is possible.

e. Three-dimensional measurement (1) Point measurement Using six or more feature patterns 20 as shown in FIG. 6 and performing the same processing as the position detection (b) for a plurality of marks, the three-dimensional coordinates are automatically increased. It is required for accuracy.

(2) Surface Measurement By using the projection feature pattern 20 as shown in FIG. 6, it is possible to set the initial value of stereo matching.

  How to determine the initial value in the case of surface measurement will be described.

  FIG. 9 and FIG. 10 are explanatory diagrams, and a part of the projection feature pattern as shown in FIG. 6 is extracted. FIG. 9 shows a reference image as a reference, and FIG. 10 shows a search image for searching. These reference / search images may be either left or right images. For example, the left image is determined as the reference image, and the right image is determined as the search image.

  The projected feature patterns correspond to the reference images S'1, S'2, S'5, and S'6 on the search image with S1, S2, S5, and S6.

  The corresponding point search is performed by performing stereo matching on the search area in the search image using the reference data block in the reference image as a template image. Image correlation processing or the like is used for stereo matching.

[Determination of search area]
A method for determining the search area based on the projection feature pattern will be described.

  The projection feature patterns S1 to S2 (horizontal direction) are defined as a width A, S1 to S5 (vertical direction) as a width B, and a portion surrounded by S1, S2, S5, and S6 is defined as a search region R1. Similarly, the portion surrounded by S2, S3, S6, and S7 is determined as the search region R2,.

  As shown in FIG. 11, mark images based on actual projection feature patterns are not always on the same line depending on the shape of the object and the orientation of the CCD. Therefore, a quadrilateral that can obtain the maximum A and B widths from the four points of each projection feature pattern is created and used as a search area. For example, in FIG. 11, the maximum widths that can be taken by S1, S2, S5, and S6 are the horizontal direction A1 and the vertical direction B1, and the rectangular area created by this is the search area R1, and similarly S2, S3, S6, and S7. The search area R2 is a quadrilateral created with the maximum widths A2 and B2 that can be taken. In this way, there are some overlapping areas, but the boundary between the R1 and R2 areas can be reliably searched.

  This is to determine the range of the search area in the search image based on the position of the mark image in the search image that has already been obtained.

  FIG. 12 shows a modification of the search area determination.

  For example, since each projection feature pattern has already been obtained as a corresponding point on the left and right images, it is efficient to set the search start position and end position of the search area as areas near the projection point. That is, in the example of FIG. 11, when the search is advanced by S1 to S5 in the vertical direction by the search width A1, as the vicinity of the line of S5 is approached, a useless search region (a region where no corresponding point clearly exists) is obtained. Out.

  Therefore, until D1 which is 1/2 of S1 to S5 in FIG. 12 (vertical direction), the same horizontal point as S1 is set as the start point of the search area, and D1 and S5 are connected on the line connecting D1 and S5. To the horizontal search start point.

  If such a process is set as a search area for each projection point, and stereo matching is performed while the search areas are somewhat overlapped, a somewhat efficient search can be performed.

  FIG. 13 and FIG. 14 further improve the efficiency of search of the search area by setting the search data block in the search area.

  For example, when the positions on the search area corresponding to the reference data blocks T1, T2, T3... On the reference image shown in FIG. 9 in the section A1 of the marks S1 to S2 in the search image, As shown in FIG. 13, the search data block is divided into several blocks such as U1, U2, U3,. In this way, it is possible to shorten the search area search time and efficiently search. In this case, the range of the search data block can be determined by how many reference data blocks are taken. For example, if n reference data blocks are set between A ′ of the reference images S′1 to S′2, A1 / n or A1 / (n−1) may be set. (N-1) is the case where the search is performed by slightly overlapping the search data blocks.

  This is to set the range of the search area in the search image based on the position of the mark image and the position of the search area already obtained. In other words, it can be said that the range of the search area is set in the search image based on the position of the mark image in the reference image and the positional relationship with the reference data block position.

  A modification of the method of FIG. 13 is shown in FIG. This is a variable search data block size. That is, since it is known that the corresponding points with respect to the reference data block are close to each other in the vicinity of the marks S1 and S2, the size of the search data block is reduced and the corresponding point position becomes uncertain as the distance increases. It is. Accordingly, the reference data block has the maximum search data block size at the position A ′ / 2.

  The size of these search data blocks is determined from the search area A1 of S1 to S2.

For example, if n is the number of reference data blocks between A ′,
・ Search data block size in the section of S1-S1 + A1 / 2:
(1 + t × i / n) × A1 / n,
-Search data block size in the section of S1 + A1 / 2 to S2:
(1 + t × (n−i) / n) × A1 / n,
However, i is the position of the search data block corresponding to each reference data block, i.e., i = 1 to n. T is a constant of magnification and is set to a predetermined value. For example, if you want to double the search data block size at the S1 position (U1) at the S1 + A1 / 2 position, select 2.

  These search data block sizes may be determined based on the marks S′1, S′2 and the reference data blocks T1, T2,... In the reference image. At that time, the above-mentioned A1 is set to A ', and the terms taking the magnification A1 / A' into consideration are multiplied.

  Thus, by setting the search area and the search data block, it is possible to perform the corresponding point search efficiently, that is, at high speed and with high reliability.

  In this method, based on the positional relationship between the position of the mark image in the search image and the position of the search area, based on the positional relationship between the position of the mark image in the reference image and the position of the reference data block, if the view is changed, The size of the search area is set in the search image.

[Determination of reference data block]
The reference data block serving as a reference is determined from S′1, S′2, S′5, S′6 on the reference image, or S1, S2, S5, S6 on the search image. For example, if the reference image (FIG. 9) S′1 to S′2 is A ′ and S′1 to S′5 is B ′, the horizontal reference data block width is A ′ / n, and the vertical direction is B ′. / M can be determined.

  Alternatively, the size may be proportional to the ratio of the left and right images. For example, the horizontal direction is set to A '/ A * n, and the vertical direction is set to B' / B * m.

  n and m can be determined as appropriate sizes depending on the relationship between the size of the object to be measured and the number of pixels, but may be set as appropriate constants depending on the values of A ′ and B ′.

  FIG. 15 shows a method of performing stereo matching in the same manner as in the above process while obtaining correlation products with three reference data block sizes instead of one. Also in this case, these three sizes can be determined based on the information of A ′ and B ′.

  FIG. 16 is a further modification in the determination of the reference data block of the present invention. The regions near S′1 and S′2 may be relatively small because the correspondence is relatively obtained on the reference / search image, but the correspondence position becomes uncertain as the distance increases, so the reference data block Change size dynamically. For example, the reference data block is expanded as shown in FIG. 14 at the T1, T2, and T3 positions. The reference data block position is set to the maximum size at the A '/ 2 point. On the contrary, the reference data block size is gradually reduced from A ′ / 2 to S′2.

  By doing in this way, the reliability of stereo matching can be improved.

  These reference data block sizes are determined from the horizontal width A ′ of S ′ 1 to S ′ 2 and the vertical width B ′ of S ′ 1 to S ′ 5.

For example, if n and m are the reference data block numbers between A ′ and B ′,
Reference data block size in the interval S′1 to S′1 + A ′ / 2:
Horizontal direction: (1 + t × i / n) × A ′ / n,
Vertical direction: (1 + t × l / m) × B ′ / m
Reference data block size in the section from S′1 + A ′ / 2 to S′2 Horizontal direction: (1 + t × (n−i) / n) × A ′ / n,
Vertical direction: (1 + t × (m−i) / m) × B ′ / m
However, i and l are the respective reference data block positions, i.e., i = 1 to n and l = 1 to m.

  Here, t is a constant of magnification and is set to a desired value. For example, if it is desired to double the reference data block size at the position S′1 (T1) at the position S′1 + A ′ / 2, 2 is selected.

  In this way, the reference data block can be made variable.

  Further, if the three types of reference data blocks as shown in FIG. 15 are made variable as in the above-described example and the correlation product is taken, the corresponding points can be searched with higher reliability by performing stereo matching.

  The determination of these reference data blocks may be similarly performed by using the marks S1 and S2 in the search image. In this case, A ′ is A1 and B ′ is B1, and the reference image is multiplied by a term that takes into account the magnification A ′ / A1 of the search image.

  By doing as described above, each initial value is automatically obtained.

  Next, the actual measurement (stereo matching) procedure will be described.

  As an example, the case of the projection feature patterns S1 to S8 in FIG. 6 will be described with reference to FIGS.

  In the vertical direction, since the orientation process has been completed and the vertical parallax has been removed (aligned), it is only necessary to search on the same line of the reference image and the search image. Further, when setting the search data block, the search data block is appropriately set as described above in each search area.

1. For the search width A1 of the horizontal line L1 of the search area R1 from S1 to S2 of the search image,
The corresponding point search is sequentially performed on the reference data blocks T1, T2, T3,... In the reference image.

  These initial values are determined by the method described above.

When A1 on the line L1 in the search area R1 ends,
2. For the search region R2 of S2 to S3, the corresponding point search on A2 of the line L1 is sequentially repeated from the reference data block position determined in this region.

  3. Next, a corresponding point search is performed on A3 of the line L1 in the search region R3 of S3 to S4. The search area R3 is sequentially performed with the template size and position determined in this area.

  When the corresponding point search of the line L1 is completed, the process moves to the next line L2, and the corresponding point search is repeated in the same manner as 1 to 3 from the search width A1 of the search region R1.

  Repeat this for the required number of lines.

  In this way, the initial values for stereo matching, that is, the search area, the search data block, and the reference data block are automatically determined from the position of the projection feature pattern, and automatic measurement can be performed.

  Furthermore, since the search area of the reference / search image and the reference data block can be limited to within A ′ and B ′, processing much faster than normal stereo matching is possible. That is, normally, stereo matching is performed for one horizontal line (FIG. 6, line) at each search position, but the processing time is a fraction of the number of projection points.

  In addition, since the corresponding area on the reference / search image is limited and required in advance, mismatching can be greatly reduced. That is, the reliability of stereo matching can be greatly improved.

  In addition, since the reference data block, the search area, and the search data block can be appropriately determined based on the position information of the projection point, the reliability of stereo matching can be further improved.

  Further, since the reference data block and the search data block can be dynamically changed according to the search position, the reliability is further increased.

  And since these stereo matching can be performed on the image without a projection feature pattern, the false detection by a feature pattern does not arise. In addition, an image without a feature pattern has value as an image database, and an original image can be stored simultaneously with measurement.

  Furthermore, according to the above-described embodiment, in order to use an image in which the feature pattern is projected and an image in which the feature pattern is not projected, an operation of attaching a measurement mark to the object is not necessary, and in an object in which the mark cannot be applied. Can also be measured.

  In this way, since an image with only feature pattern information can be created by subtracting an image with and without a feature pattern, feature pattern position detection, orientation, stereo model formation, and three-dimensional measurement can be performed without human intervention. It is possible to perform automatically and accurately. That is, it is possible to eliminate the manual orientation work and the three-dimensional measurement work, which have conventionally required skill and are complicated, and it is possible to improve the reliability by performing everything automatically.

  Furthermore, it is not necessary to fix the camera firmly on the stereo platform, and it is possible to perform high-precision 3D measurement simply by installing two cameras roughly at the site and taking pictures. Regardless of this, there is an excellent effect that it can be measured easily.

  In addition, by increasing the number of feature pattern points, it becomes possible to set the initial value of stereo matching, that is, to automatically determine the search width and template image and perform stereo matching, and further reduce the stereo matching time and reliability. It has the outstanding effect of being able to automatically measure the surface shape of the object while greatly improving it.

The block diagram which shows the flow of measurement in the shape measuring apparatus of this invention. The block diagram which shows notionally the shape measuring apparatus of this invention. The conceptual diagram which shows the left-right image imaging | photography part of a shape measuring apparatus. The block diagram which shows the pattern extraction part of a shape measuring apparatus. Plan view showing an example of a projection pattern Plan view showing another example of projection pattern Explanatory drawing which shows the template matching by SSDA method. Explanatory drawing which shows a moment method. Explanatory drawing which shows a reference | standard image. Explanatory drawing which shows a search image. Explanatory drawing which shows a search image. Explanatory drawing which shows a search image. Explanatory drawing which shows a search image. Explanatory drawing which shows a search image. Explanatory drawing which shows a reference | standard image. Explanatory drawing which shows a reference | standard image. The block diagram which shows the outline | summary of the image measuring apparatus by this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 0 Measurement object 1, 2 Left-right image imaging | photography part 3 Pattern projection part 4 Controller 5 Pattern extraction part 6 Posture calculation part 7 Stereo model formation part 8 Shape measurement part 9 Template matching image 10 Shape measurement apparatus 11 Optical system 12 CCD
DESCRIPTION OF SYMBOLS 13 CCD driver 14 Operational amplifier 15 A / D converter 20 Projection pattern 30 Image measuring device 31 General position measurement part 32 Data setting part 33 Correspondence discrimination | determination part 51 Image memory for pattern projection 52 Image memory for no pattern 53 Image difference calculating part (instrument) )
54 pattern image memory 55 pattern position detector

Claims (6)

The measurement object on which the feature pattern whose positional relationship is known is projected is photographed from different directions, and the positional relationship of the measurement object in each image is determined from the pair of photographed images. An approximate position measuring unit which is roughly determined by detecting the position of the mark image by
Among a pair of images, one image is defined as a reference image, the other image is defined as a search image, a reference data block is set in the reference image, a search area is provided in the search image, and a search data block is set in the search area A data setting section;
A correspondence discriminating unit for obtaining a correspondence relationship between the search data block and the reference data block set in the search area by performing stereo matching on the reference data block using the reference data block as a template image; Prepared,
The data setting unit is configured to perform at least one of setting a search area on a search image and setting a search data block based on a position of a mark image detected by the approximate position measurement unit. Image measuring device.   The image measurement apparatus according to claim 1, wherein the data setting unit is configured to set a range of a search area in the search image based on a position of a mark image obtained by projecting a feature pattern in the search image. An image measuring device.   The image measurement apparatus according to claim 1, wherein the data setting unit includes a position of a mark image and a position of a search area by projection of a feature pattern in a search image, or a position and reference of a mark image by projection of a feature pattern in a reference image. An image measuring apparatus configured to set a search area range in a search image based on a positional relationship with a data block position.   The image measurement apparatus according to claim 1, wherein the data setting unit includes a position of a mark image and a position of a search area by projecting a feature pattern in a search image, or a position of a feature pattern image in a reference image and a reference data block position. An image measuring device configured to set a size of a search area range in a search image based on a positional relationship with   5. The image measurement apparatus according to claim 4, wherein the data setting unit is configured to search for a search image as the mark image and the search region by the feature pattern projection in the search image are separated from the feature pattern image and the reference data block in the reference image. An image measuring apparatus configured to set a large width or area of a search area.   6. The image measurement device according to claim 2, wherein the data setting unit is configured to set a plurality of search areas having different sizes.

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