Core position measuring method and core position measuring device

Abstract

In this core position measuring method, a feature quantity vector is calculated for each contour extracted from an end surface image, for each of a plurality of luminance threshold value candidates prepared in advance. A first set including first feature quantity vector candidates satisfying a structural condition of common cladding is generated. A luminance threshold value candidate corresponding to any of the first feature quantity vector candidates of the first set is selected as a first luminance threshold value for extracting a contour of the common cladding. A second set including second feature quantity vector candidates satisfying a structural condition of a plurality of cores is generated. A luminance threshold value candidate corresponding to any of the second feature quantity vector candidates of the second set is selected as a second luminance threshold value for extracting contours of the plurality of cores.

Description

Core position measuring method and core position measuring device

[0001] The present disclosure relates to a core position measurement method and a core position device for a multi-core optical fiber (hereinafter referred to as "MCF"). This application claims priority to Japanese Application No. 2023-051229, filed on March 28, 2023, and incorporates the entire contents of said Japanese application by reference.

[0002] An MCF is an optical fiber having multiple cores made of a glass material, a common cladding also made of a glass material surrounding the multiple cores, and a resin layer coating the common cladding. Therefore, when transmitting an optical signal to each core of an MCF or measuring the optical characteristics of each core, it is necessary to identify a target core from the multiple cores included in the MCF. A versatile method for core identification involves optically measuring the positions of the multiple cores and the common cladding by optically observing the end face of the MCF. A method described in Patent Document 1 is known as an optical observation method and apparatus applicable to MCFs. In the method described in Patent Document 1, an imaging device having a mirror and a camera is directly faced to the fiber end face. In this configuration, illumination light input laterally from an LED or the like to the fiber is emitted from the fiber end face, and the fiber end face is imaged by the camera.

[0003] International Publication No. WO2013 / 077002

[0004] The core position measurement method disclosed herein measures, from an end face image of an MCF having multiple cores and a common cladding surrounding the multiple cores, the relative positions of the multiple cores with respect to the common cladding, which has a different brightness from the multiple cores, in the end face image. The core position measurement method includes first to fifth steps. In the first step, one or more contours extracted from the end face image using one brightness threshold candidate are associated with each of multiple brightness threshold candidate values ​​prepared in advance. In the first step, one or more feature vectors are calculated that are associated one-to-one with the one or more contours corresponding to one brightness threshold candidate value. In the second step, a first set is generated that includes one or more first feature vector candidates, which are feature vectors that satisfy structural conditions for the common cladding among the one or more feature vectors calculated in the first step. In the third step, for each of the one or more first feature vector candidates belonging to the first set, the distance from one first target feature point, whose position is specified by one of the first feature vector candidates, to first peripheral feature points, whose positions are specified by the remaining first feature vector candidates, is calculated. Furthermore, in the third step, a brightness threshold candidate corresponding to a first feature vector candidate that has the greatest degree of match between the position of the first target feature point and the first peripheral feature point is selected as a first brightness threshold value for extracting the outline of the common cladding. In the fourth step, a second set of second feature vector candidates, which are feature vectors that satisfy the structural conditions of the multiple cores, is generated from the one or more feature vectors calculated in the first step. In the fifth step, for each second feature vector candidate belonging to the second set, the distance from one second target feature point, whose position is specified by one second feature vector candidate, to the second peripheral feature points, whose positions are specified by the remaining second feature vector candidates, is calculated. In the fifth step, a brightness threshold candidate corresponding to a second feature vector candidate that has the greatest degree of match between the position of the second target feature point and the second peripheral feature point is selected as a second brightness threshold value for extracting the outlines of the multiple cores.

[0005] In the core position measurement method disclosed herein, each of the one or more feature vectors calculated in the first step is a position vector whose components are represented by a plurality of structural data quantified as feature amounts of the corresponding contours. Furthermore, when the feature vector is expressed by m components, the first target feature point, the first peripheral feature point, the second target feature point, and the second peripheral feature point are defined as points in an m-dimensional spatial coordinate system with the origin at the start point of the position vector. m is an integer equal to or greater than 2. The "positional consistency" of the first target feature point is evaluated based on a match score obtained by adding up a decreasing function of the distance to each of the first peripheral feature points calculated for the first target feature point. The "positional consistency" of the second target feature point is evaluated based on a match score obtained by adding up a decreasing function of the distance to each of the second peripheral feature points calculated for the second target feature point. Furthermore, in the core position measurement method of the present disclosure, the relative positions of multiple cores with respect to the common cladding are measured based on structural data constituting a first feature vector candidate corresponding to the first brightness threshold value selected in the third step, and structural data constituting a second feature vector candidate corresponding to the second brightness threshold value selected in the fifth step.

[0006] FIG. 1 is a diagram showing the structure of an MCF to be measured and the structure of a core position measurement device according to the present disclosure. FIG. 2 is a diagram showing end face images and brightness distributions for various measurement targets. FIG. 3 is a flowchart for explaining the overall configuration of a core position measurement method according to the present disclosure. FIG. 4 is a flowchart for explaining each step of common cladding measurement and core measurement in the core position measurement method according to the present disclosure. FIG. 5 is a diagram showing a data structure and the like for supplementarily explaining each step of the core position measurement method according to the present disclosure. FIG. 6 is a diagram for explaining the operation of a device that performs the display processing shown in FIG. 3. FIG. 7 is an image in which measurement results obtained by the core position measurement method and core position measurement device according to the present disclosure are superimposed on an end face image.

[0007] [Problem to be Solved by the Present Disclosure] As a result of examining the prior art, the inventors discovered the following problem: In the method of Patent Document 1, the brightness and contrast of the common cladding and multiple cores in an MCF photographed by an imaging means may vary due to differences in the coating color of the MCF, variations in the refractive index profile, and coating thickness, etc. Variations in brightness and contrast can make it difficult to identify each core from the elements that make up the end face of the MCF, making it difficult to automate core identification.

[0008] The present disclosure provides a core position measurement method and a core position measurement device that enable automatic identification of the elements that make up the end face of an MCF and high-precision measurement of the core position within a common cladding, even if there are variations in brightness and contrast in the end face image of the MCF.

[0009] [Effects of the Present Disclosure] The core position measurement method and core position measurement device of the present disclosure enable automatic identification of the elements that make up the end face of the MCF and highly accurate measurement of the core position within the common cladding, even if there are variations in brightness and contrast in the end face image of the MCF.

[0010] [Description of Embodiments of the Present Disclosure] First, the contents of the embodiments of the present disclosure will be individually listed and described.

[0011] The core position measurement method disclosed herein includes: (1) a core position measurement method for measuring, from an end face image of an MCF having multiple cores and a common cladding surrounding the multiple cores, the relative positions of the multiple cores with respect to the common cladding, which has a different brightness from the multiple cores in the end face image, the method comprising first to fifth steps. In the first step, one or more contours extracted from the end face image using one brightness threshold candidate are associated with each of multiple brightness threshold candidate values ​​prepared in advance. In the first step, one or more feature vectors are calculated that are associated one-to-one with the one or more contours corresponding to one brightness threshold candidate value. In the second step, a first set is generated that includes one or more first feature vector candidates, which are feature vectors that satisfy the structural conditions of the common cladding among the one or more feature vectors calculated in the first step. In the third step, for each of the one or more first feature vector candidates belonging to the first set, the distance from one first target feature point, whose position is specified by one first feature vector candidate, to first peripheral feature points, whose positions are specified by the remaining first feature vector candidates, is calculated. Furthermore, in the third step, a brightness threshold candidate corresponding to a first feature vector candidate that has the greatest degree of match between the position of the first target feature point and the first peripheral feature point is selected as a first brightness threshold value for extracting the outline of the common cladding. In the fourth step, a second set of second feature vector candidates, which are feature vectors that satisfy the structural conditions of the multiple cores, is generated from the one or more feature vectors calculated in the first step. In the fifth step, for each second feature vector candidate belonging to the second set, the distance from one second target feature point, whose position is specified by one second feature vector candidate, to the second peripheral feature points, whose positions are specified by the remaining second feature vector candidates, is calculated. In the fifth step, a brightness threshold candidate corresponding to a second feature vector candidate that has the greatest degree of match between the position of the second target feature point and the second peripheral feature point is selected as a second brightness threshold value for extracting the outlines of the multiple cores.

[0012] In the core position measurement method of the present disclosure, each of the one or more feature vectors calculated in the first step is a position vector whose components are represented by multiple structural data quantified as feature amounts of the corresponding contours. Furthermore, when the feature vector is expressed by m components, the first target feature point, the first peripheral feature point, the second target feature point, and the second peripheral feature point are defined as points in an m-dimensional spatial coordinate system with the origin being the starting point of the position vector. m is an integer of 2 or greater. As an example, when the feature amount of the common cladding is defined by three types of numerical data, the feature amount vector of the common cladding is expressed by three vector components. Furthermore, when the feature amount of each core in an MCF having M cores is defined by three types of numerical data, the feature amount vector of the core in the MCF is expressed by (3 × M) vector components. M is an integer of 2 or greater. The "positional consistency" of the first target feature point is evaluated based on a match score obtained by adding a decreasing function of the distance to each first peripheral feature point calculated for the first target feature point. The "degree of positional agreement" of the second target feature point is evaluated based on a match score obtained by adding a decreasing function of the distance to each second peripheral feature point calculated for the second target feature point. The decreasing function may be an inverse. Alternatively, the square of the inverse may be used as the decreasing function. Furthermore, in the core position measurement method disclosed herein, the relative positions of multiple cores with respect to the common cladding are measured based on structure data constituting the first feature vector candidate corresponding to the first brightness threshold value selected in the third step and structure data constituting the second feature vector candidate corresponding to the second brightness threshold value selected in the fifth step.

[0013] With the above configuration, even if variations in brightness and contrast occur in the end face image of the MCF due to individual differences in the MCF being observed or variations in the lighting conditions, it is possible to automatically identify the elements that make up the end face of the MCF and measure the core position within the common cladding with high accuracy.

[0014] (2) In the above (1), the plurality of brightness threshold candidate values ​​may be prepared, including a plurality of first brightness threshold candidate values ​​for extracting the outline of the common cladding and a plurality of second brightness threshold candidate values ​​for extracting the outline of the multiple cores. In this case, the first step includes a sixth step and a seventh step. In the sixth step, a feature vector to be selected in the second step is calculated for each of the plurality of first brightness threshold candidate values. In the seventh step, a feature vector to be selected in the fourth step is calculated for each of the plurality of second brightness threshold candidate values. This configuration is particularly effective when the MCF end face image contains defects such as brightness bleeding around the core, bright spots within the core, or image defects in the common cladding. In other words, because the brightness threshold candidate values ​​for extracting the outline of the common cladding and the outline of the multiple cores are prepared in advance, it is possible to narrow down the range of brightness threshold values ​​that are optimal for each type of end face component, such as the common cladding and the multiple cores, thereby enabling more precise selection of brightness threshold values.

[0015] (3) In the above (1) or (2), at least one of the following may be displayed: a numerical value of the structure data of the first feature vector candidate corresponding to the first brightness threshold value, image data generated based on the structure data of the first feature vector candidate corresponding to the first brightness threshold value, a numerical value of the structure data of the second feature vector candidate corresponding to the second brightness threshold value, and image data generated based on the structure data of the second feature vector candidate corresponding to the second brightness threshold value. This configuration allows visual confirmation of the state of the end face of the MCF being measured.

[0016] (4) In any of (1) to (3) above, the range of the plurality of brightness threshold candidates may be a range from the maximum brightness to the minimum brightness of the end face image.

[0017] (5) In any of (1) to (4) above, the plurality of structure data may include data relating to at least one of the center of gravity position and size of the corresponding contour on the end face image.

[0018] The core position measurement device of the present disclosure (6) includes a calculation unit that performs any one of the core position measurement methods (1) to (5) above, and a memory that stores a plurality of brightness threshold candidate values ​​in advance and a correspondence table calculated by the calculation unit. The correspondence table includes a first correspondence table indicating the correspondence between first feature vectors and corresponding brightness threshold candidate values, and a second correspondence table indicating the correspondence between second feature vectors and corresponding brightness threshold candidate values. This configuration enables automatic identification of elements that constitute the end face of an MCF and highly accurate measurement of core positions within the common cladding, even when variations in brightness and contrast occur in an image of the end face of an MCF due to individual differences in the MCF being observed or variations in lighting conditions.

[0019] The core position measurement device disclosed herein (7) is a core position measurement device for measuring, from an end face image of an MCF having multiple cores and a common cladding surrounding the multiple cores, the relative positions of the multiple cores with respect to the common cladding, which has a different brightness from the multiple cores in the end face image, and includes a memory and a calculation unit. The memory stores multiple brightness threshold candidate values. The calculation unit performs steps 1 to 5 to select a first brightness threshold value for extracting the contour of the common cladding and a second brightness threshold value for extracting the contours of the multiple cores. In the first step, one or more contours extracted from the end face image are associated with each of the multiple brightness threshold candidate values ​​read from the memory using one brightness threshold candidate value. Furthermore, in the first step, one or more feature vectors are calculated that are associated one-to-one with one or more contours corresponding to one brightness threshold candidate value. In the second step, a first correspondence table is stored in memory, which indicates a correspondence relationship between one or more first feature vector candidates, which are feature vectors that satisfy the structural conditions of the common cladding among the one or more feature vectors calculated in the first step, and brightness threshold value candidates corresponding to each of the one or more first feature vector candidates among the multiple brightness threshold value candidates. In the third step, for each of the one or more first feature vector candidates in the first correspondence table, a distance from a first target feature point whose position is specified by the one first feature vector candidate to a first peripheral feature point whose position is specified by the remaining first feature vector candidates is calculated. Furthermore, in the third step, a brightness threshold value candidate corresponding to a first feature vector candidate that maximizes the degree of match between the position of the first target feature point and the first peripheral feature point is selected as a first brightness threshold value among the multiple brightness threshold value candidates. In the fourth step, a second correspondence table is stored in memory, which indicates a correspondence relationship between second feature vector candidates, which are feature vectors that satisfy the structural conditions of the multiple cores among the one or more feature vectors calculated in the first step, and brightness threshold value candidates corresponding to each of the multiple brightness threshold value candidates.In the fifth step, for each of the second feature vector candidates in the second correspondence table, a distance from one second target feature point, the position of which is specified by the one second feature vector candidate, to each of the second surrounding feature points, the positions of which are specified by the remaining second feature vector candidates, is calculated. Furthermore, in the fifth step, a brightness threshold candidate corresponding to the second feature vector candidate that maximizes the degree of match between the positions of the second target feature point and the second surrounding feature points is selected as the second brightness threshold.

[0020] In the core position measurement device of the present disclosure, each of the one or more feature vectors calculated in the first step is a position vector whose components are multiple pieces of digitized structural data of the corresponding contour. Furthermore, when the feature vector is composed of m components, the first target feature point, the first peripheral feature point, the second target feature point, and the second peripheral feature point are defined as points in an m-dimensional spatial coordinate system with the origin at the start point of the feature vector. m is an integer greater than or equal to 2. The degree of match of the positions of the first target feature points is evaluated based on a match score obtained by adding a decreasing function of the distance to each first peripheral feature point calculated for the first target feature point. The degree of match of the positions of the second target feature points is evaluated based on a match score obtained by adding a decreasing function of the distance to each second peripheral feature point calculated for the second target feature point. Furthermore, in the core position measurement device of the present disclosure, the calculation unit measures the relative positions of the multiple cores with respect to the common cladding based on structural data constituting the first feature vector candidate corresponding to the first brightness threshold selected in the third step and structural data constituting the second feature vector candidate corresponding to the second brightness threshold selected in the fifth step.

[0021] With the above configuration, even if variations in brightness and contrast occur in the image of the end face of the MCF due to individual differences in the MCF being observed or variations in the way the light hits it, it is possible to automatically identify the elements that make up the end face of the MCF and to measure the core position within the common cladding with high accuracy.

[0022] (8) In the above (7), the memory may store a plurality of first brightness threshold candidate values ​​for extracting the outline of the common cladding and a plurality of second brightness threshold candidate values ​​for extracting the outline of the multiple cores. In this case, the calculation unit performs the sixth and seventh steps as the first step. In the sixth step, a feature vector to be selected in the second step is calculated for each of the multiple first brightness threshold candidate values. In the seventh step, a feature vector to be selected in the fourth step is calculated for each of the multiple second brightness threshold candidate values. This configuration is particularly effective when the MCF end face image contains defects such as brightness bleeding around the core, bright spots within the core, and image defects in the common cladding. In other words, because the brightness threshold candidate values ​​for extracting the outline of the common cladding and the outline of the multiple cores are prepared in advance, it is possible to narrow down the range of brightness threshold values ​​that are optimal for each type of end face component, such as the common cladding and the multiple cores, thereby enabling more precise selection of brightness threshold values.

[0023] (9) In the above (7) or (8), the core position measurement device of the present disclosure may include a monitor. The monitor displays at least one of the following: the numerical values ​​of the structure data of the first feature vector candidate corresponding to the first brightness threshold, image data generated based on the structure data of the first feature vector candidate corresponding to the first brightness threshold, the numerical values ​​of the structure data of the second feature vector candidate corresponding to the second brightness threshold, and image data generated based on the structure data of the second feature vector candidate corresponding to the second brightness threshold. This configuration allows visual confirmation of the state of the end face of the MCF being measured.

[0024] (10) In any of (7) to (9) above, the range of the plurality of brightness threshold candidates may be a range from the maximum brightness to the minimum brightness of the end face image.

[0025] (11) In any of (7) to (10) above, the plurality of structure data may include data relating to at least one of the center of gravity position and size of the corresponding contour on the end face image.

[0026] As described above, each aspect listed in the [Description of Embodiments of the Present Disclosure] section can be applied to all of the remaining aspects individually or to all combinations of these remaining aspects.

[0027] [Details of the embodiments of the present disclosure] Specific examples of the core position measurement method and core position measurement device according to the present disclosure will be described in detail below with reference to the accompanying drawings. Note that the present disclosure is not limited to these examples, but is defined by the claims, and is intended to include all modifications within the meaning and scope equivalent to the claims. Furthermore, in the description of the drawings, identical elements are given the same reference numerals, and duplicate explanations will be omitted.

[0028] Fig. 1 is a diagram showing the structure of an MCF (multi-core optical fiber) to be measured and the structure of a core position measurement device of the present disclosure (denoted as "measurement device" in Fig. 1). The upper part of Fig. 1 (denoted as "measurement device" in Fig. 1) shows the MCF to be measured. The lower part of Fig. 1 (denoted as "device configuration" in Fig. 1) shows an example configuration of the core position measurement device of the present disclosure.

[0029] The MCF 1 shown in the upper part of FIG. 1 includes a glass optical fiber extending along a central axis (hereinafter referred to as the "fiber axis AX") and a resin coating 13 provided on the outer periphery of the glass optical fiber. The glass optical fiber has an end face 12a, a first core 11a and a second core 11b each extending along the fiber axis AX, and a common cladding 12 surrounding the first core 11a and the second core 11b. The end face 12a is photographed to measure the core position. When observing the end face 12a of the MCF 1, the resin coating 13 at the tip portion of the MCF 1, including the end face 12a, is usually removed. Furthermore, MCFs with three or more cores and MCFs with a dummy core for identification purposes are also subject to measurement by the core position measurement method and core position measurement device of the present disclosure. The dummy cores are also referred to as markers.

[0030] 1 includes, as a configuration enabling attitude control of the MCF 1 having the above-described structure, a stage 22 for adjusting the attitude of the MCF 1 while holding the MCF 1, and a drive unit 120 for driving the stage 22. Furthermore, as a configuration for capturing an image of the end face of the MCF 1, the core position measurement device 2 includes: an illumination device 23a that outputs illumination light laterally to the MCF 1 through the resin coating 13; an illumination device 23b that outputs illumination light laterally to the tip portion of the MCF 1 from which a portion of the resin coating 13 has been removed; a mirror 24 that reflects the illumination light emitted from the end face 12a of the MCF 1; and a camera 21 that captures an image of the end face 12a of the MCF 1 via the mirror 24. Furthermore, as a configuration for implementing the core position measurement method of the present disclosure, the core position measurement device 2 includes: a control unit 100 including a calculation unit 110 that performs the core position measurement method of the present disclosure; a monitor 140; a drawing unit 130 that generates image data and the like to be displayed on the monitor 140; and a memory 150. The control unit 100 controls all peripheral devices such as the camera 21, the lighting device 23a, the lighting device 23b, the driving unit 120, the drawing unit 130, and the memory 150.

[0031] In the core position measurement device 2, first, illumination light is output from the illumination device 23a to the side of the MCF 1 through the resin coating 13, while illumination light is output from the illumination device 23b to the side of the tip portion of the MCF 1 from which a portion of the resin coating 13 has been removed. The wavelength of the output illumination light is a visible light wavelength of 0.4 μm to 0.7 μm or a near-infrared light wavelength of 0.7 μm to 2.2 μm. The near-infrared light wavelength is suitable for the illumination light wavelength because it is not easily scattered by the scattering material in the colored resin coating 13 of the MCF 1 and does not easily cause a difference in the end face observation image depending on whether the resin coating 13 is colored or not. The illumination light output from the illumination device 23a is partially scattered inside the resin coating 13 and at the interface between the resin coating 13 and the common cladding 12, and then propagates through the common cladding 12, the first core 11a, and the second core 11b of the MCF 1. Furthermore, illumination light output from the illumination device 23b also propagates through the common cladding 12, the first core 11a, and the second core 11b of the MCF 1. As a result, illumination light from the illumination devices 23a and 23b is emitted from the end face 12a of the MCF 1. The illumination light emitted from the common cladding 12, the first core 11a, and the second core 11b located on the end face 12a reaches the camera 21 via the mirror 24, and the end face 12a is photographed by the camera 21. Image data of the end face of the MCF 1 photographed by the camera 21 is stored in the memory 150. Note that the illumination device 23a may be positioned so that the portion of the MCF 1 closest to the illumination device 23a is 5 cm to 100 cm, or 10 cm to 50 cm, away from the end face 12a. By irradiating the illumination light to a portion of the MCF 1 sufficiently distant from the end face 12a, the difference between the transmission loss when the illumination light propagates through the core and reaches the end face 12a and the transmission loss when the illumination light propagates through the cladding and reaches the end face 12a becomes large. This makes it possible to maintain a large contrast between the core and the cladding in the end face image captured by the camera 21, thereby enabling the core and the cladding to be detected more reliably. Furthermore, although not shown in FIG. 1 , the illumination light may be incident on the end face of the MCF 1 opposite the end face 12a, which is 20 cm to 20 m or 50 cm to 10 m away from the end face 12a.This makes it possible to selectively increase the brightness of the core, and to more reliably detect the core.

[0032] 2 shows end face images and brightness distributions for various measurement targets (labeled "Brightness Distribution" in FIG. 2). The upper part of FIG. 2 (labeled "End Face Pattern 1" in FIG. 2) shows an ideal end face image 4a and brightness distribution 5a of the measurement target. The lower part of FIG. 2 (labeled "End Face Pattern 2" in FIG. 2) shows an end face image 4b ​​and brightness distribution 5b of the measurement target that includes a blurred region.

[0033] As shown in the upper part of Fig. 2, an ideal end face image 4a captured by camera 21 includes a common cladding image 41a, a first core image 42a, and a second core image 43a of the measurement target. The common cladding image 41a, the first core image 42a, and the second core image 43a are distinguished by differences in brightness. The end face image 4a is stored in memory 150 as image data. To explain this distinguishing method, the upper part of Fig. 2 also shows a brightness distribution 5a along a line image 44a that passes through the center of the first core image 42a and the center of the second core image 43a. Note that the line image 44a essentially represents a one-dimensional pixel array on monitor 140.

[0034] When an appropriate brightness threshold value 51a for extracting the contour of the common cladding image 41a is set, the contour of the common cladding image 41a is extracted from the end face image 4a by extracting pixels whose brightness changes across the brightness threshold value 51a. Note that, as shown in FIG. 2 , when the brightness of each of the first core image 42a and the second core image 43a is higher than the brightness of the common cladding image 41a, not only the contour of the common cladding image 41a but also the contours of the first core image 42a and the second core image 43a are extracted from the end face image 4a. The coordinates of the common cladding center 53a on the monitor 140 are calculated as the center of gravity of the area surrounded by the contour of the common cladding image 41a. Next, when an appropriate brightness threshold value 52a for extracting the contours of the first core image 42a and the second core image 43a is set, the contours of the first core image 42a and the second core image 43a are extracted from the end face image 4a by extracting pixels whose brightness changes across the brightness threshold value 52a. By calculating the center of gravity of the region surrounded by the outlines of the first core image 42a and the second core image 43a, the coordinates of the first core center 54a and the second core center 55a on the monitor 140 are calculated. In the example of Fig. 2, the brightness of the first core image 42a and the second core image 43a is higher than the brightness of the common cladding image 41a. Therefore, the brightness threshold value 52a for extracting the core outline is set higher than the brightness threshold value 51a for extracting the common cladding outline, but the magnitude relationship between the brightness and the brightness threshold value may be reversed depending on how the illumination light is input.

[0035] On the other hand, the end face image 4b ​​including defects captured by the camera 21 includes, as shown in the lower part of Figure 2, the common cladding image 41b, first core image 42b, and second core image 43b of the measurement target, as well as defects such as a chip 45b on the outer edge of the common cladding image 41b, a brightness blur 46b around the first core image 42b, and a bright spot 47b located inside the second core image 43b. The end face image 4b ​​is also stored as image data in the memory 150. These defects can be reduced by adjusting the cutting method of the end face of the measurement target, the type of illumination, the method of inputting the illumination light, etc. However, in practice, some defects will inevitably occur. To explain the identification method in this case, the lower part of Figure 2 also shows a brightness distribution 5b along a line image 44b passing through the center of the first core image 42b and the center of the second core image 43b. Note that the line image 44b essentially represents a one-dimensional pixel array on the monitor 140.

[0036] 2 , an appropriate brightness threshold 51b for extracting the contour of the common cladding image 41b is set, and the coordinates of the common cladding center 53b on the monitor 140 are finally calculated. However, if the brightness threshold 51b is similar to the brightness of the chip 45b, the extracted contour of the common cladding image 41b will be affected by the chip 45b and will be inaccurate, resulting in an error in the calculated coordinates of the common cladding center 53b. Furthermore, an appropriate brightness threshold 52b for extracting the contours of the first core image 42b and the second core image 43b is set, and the coordinates of the first core center 54b and the second core center 55b on the monitor 140 are finally calculated. However, if the brightness threshold value 52b is close to the brightness of the blur 46b or the brightness of the bright spot 47b, the contours of the extracted first core image 42b and second core image 43b will be affected by the blur 46b and the bright spot 47b and will be inaccurate, resulting in an error in the calculated coordinates of the first core center 54b and the second core center 55b on the monitor 140.

[0037] In the example shown at the bottom of Figure 2, the brightness thresholds 51b and 52b for extracting the contours of the common cladding image 41b, the first core image 42b, and the second core image 43b are shown as appropriate levels that are not affected by defects. However, in practice, the optimal brightness thresholds can easily change depending on the nature of the defect and the brightness distribution in the endface image of the object being measured. For this reason, it has traditionally been difficult to mechanically set the brightness thresholds. As a result, there have been cases where an operator has to intervene to set the appropriate brightness thresholds, which has led to measurement errors due to human error and reduced efficiency in core position measurement.

[0038] Next, the core position measurement method of the present disclosure, using the core position measurement device 2 shown in the lower part of FIG. 1, will be described in detail with reference to FIGS. 3 to 7. The following description assumes that the MCF 1 shown in the upper part of FIG. 1 is the measurement target, and that common cladding measurement and core measurement are performed using an endface image containing defects, such as the endface image 4b ​​shown in the lower part of FIG. 2, as the endface image of the measurement target. Therefore, brightness threshold candidates for extracting the outline of the common cladding and brightness threshold candidates for extracting the outlines of each of the multiple cores are pre-stored in the memory 150. When using an endface image containing defects in this manner, the process of extracting the outline of the common cladding and the process of extracting the outlines of each of the multiple cores are performed separately. On the other hand, when common cladding measurement and core measurement are performed using an ideal endface image 4a, the brightness threshold candidates pre-stored in the memory 150 do not need to be distinguished between the brightness threshold candidate for extracting the outline of the common cladding and the brightness threshold candidate for extracting the outlines of each of the multiple cores. When using such an ideal end face image 4a, the contour extraction of the common cladding and the contour extraction of the multiple cores may be performed in a single process. The core position measurement method of this embodiment may be performed using a device other than the core position measurement device 2 shown in the lower part of FIG. 1 . For example, the camera 21 and the calculation unit 110 may be separate devices. The driving unit 120 may not be provided. If the driving unit 120 is not provided, the MCF 1 may be held by a fixed stage 22.

[0039] First, Fig. 3 is a flowchart for explaining the overall configuration of the core position measurement method of the present disclosure. Fig. 4 is a flowchart for explaining each step of the common cladding measurement and core measurement in the core position measurement method of the present disclosure. Fig. 5 is a diagram showing a data structure etc. for supplementary explanation of each step of the core position measurement method of the present disclosure (indicated as "Terminology" in Fig. 5). Fig. 6 is a diagram for explaining the operation of the device that performs the display processing shown in Fig. 3. Furthermore, Fig. 7 is an image in which the measurement results obtained by the core position measurement method and core position measurement device of the present disclosure are superimposed on an end face image (indicated as "Measurement Results" in Fig. 7).

[0040] The left side of FIG. 5 (labeled "common cladding" in FIG. 5) shows a diagram for explaining terminology related to the measurement of the common cladding of the MCF to be measured in the core position measurement method of the present disclosure, and the right side of FIG. 5 (labeled "core" in FIG. 5) shows a diagram for explaining terminology related to the measurement of the core. The upper part of FIG. 5 (labeled "luminance distribution" in FIG. 5) shows the relationship between the luminance distribution of the common cladding and the core and the luminance threshold. The middle part of FIG. 5 (labeled "correspondence table" in FIG. 5) shows a correspondence table showing the relationship between the feature vector for each luminance threshold and its evaluation. The lower part of FIG. 5 (labeled "match score calculation" in FIG. 5) shows a diagram for explaining a method for calculating the luminance threshold required for measurement. The upper part of FIG. 7 (labeled "image to be processed" in FIG. 7) shows an image of the end face of the MCF to be measured. The middle part of Fig. 7 (labeled "Outline Extraction Result" in Fig. 7) shows a diagram in which the extracted outlines of the common cladding and core are superimposed on the end face image shown in the upper part of Fig. 7. The bottom part of Fig. 7 (labeled "Center of Gravity Extraction Result" in Fig. 7) shows a diagram in which the extracted centers of gravity of the common cladding and core are superimposed on the end face image shown in the upper part of Fig. 7.

[0041] The overall operation of the core position measurement method of the present disclosure assumes that the MCF 1 shown in the upper part of Fig. 1 is the measurement target, and that an end face image including a defect such as end face image 4b ​​shown in the lower part of Fig. 2 is used, and is performed by the calculation unit 110 according to the flowchart shown in Fig. 3. Note that peripheral devices such as the drawing unit 130 are entirely controlled by the control unit 100.

[0042] First, in step ST10, the calculation unit 110 takes in the end face image 4 of the measurement target stored in the memory 150. The end face image 4 is an end face image including a defect. The memory 150 stores not only the end face image 4 previously photographed by the camera 21 but also the brightness threshold value B for extracting the outline of the common cladding 12 as a brightness threshold value candidate. ｃｌａｄ，ｎ and a brightness threshold value B for extracting the contours of the first core 11a and the second core 11b. ｃｏｒｅ，ｎ The brightness threshold value table 200 is also stored. Specifically, the brightness threshold value B ｃｌａｄ，ｎ As B ｃｌａｄ，０ From B ｃｌａｄ，２５５ 256 values ​​are prepared up to the brightness threshold B ｃｏｒｅ，ｎ As B ｃｏｒｅ，０ From B ｃｏｒｅ，２５５ 256 values ​​up to 10 ... ｃｌａｄ，ｎ and a brightness threshold B for extracting the contours of the first core 11a and the second core 11b. ｃｏｒｅ，ｎ However, a common brightness threshold value for extracting the outline of the common cladding 12 and the outlines of the first core 11 a and the second core 11 b may be stored in the memory 150.

[0043] Next, in step ST20, a brightness threshold B for extracting the outline of the common cladding 12 is set. ｃｌａｄ，ｎ are taken from the memory 150 to the calculation unit 110 as brightness threshold candidate values, and in step ST30, the calculation unit 110 determines the brightness threshold value that is optimal for measuring the contour of the common cladding 12.

[0044] Following step ST30 in which the contour of the common cladding 12 is measured, in step ST40, a brightness threshold B for extracting the contours of the first core 11a and the second core 11b is calculated. ｃｏｒｅ，ｎ are taken as brightness threshold candidate values ​​from memory 150 into calculation unit 110. Then, in step ST50, calculation unit 110 determines the brightness threshold value that is optimal for measuring the contour of common cladding 12.

[0045] The optimal brightness thresholds determined by the calculation unit 110 in steps ST30 and ST50 are associated with contour information for each part included in the end face image 4 as the feature quantity to be measured. This contour information is defined by the contour centroid position and contour radius. More specifically, the contour radius is structure data corresponding to the average distance from the contour centroid to the contour. The contour centroid coordinates are two types of structure data defined by the horizontal distance from a reference point on the end face image 4 to the contour centroid and the vertical distance from the reference point to the contour centroid. In step ST60, the control unit 100 generates image data, etc. based on these structure data, and the rendering unit 130, in response to an instruction from the control unit 100, performs a display process in which the generated image data, etc. are displayed on the monitor 140.

[0046] Next, step 30 of the common cladding measurement shown in Fig. 3 will be described with reference to the flowchart of Fig. 4. Note that step 50 of the core measurement shown in Fig. 3 is also performed according to the flowchart of Fig. 4.

[0047] In step ST110, the calculation unit 110 sets one brightness threshold value read from the brightness threshold values ​​stored in the memory 150 as a brightness threshold value candidate. In step ST120, the calculation unit 110 extracts the contours of each part of the object to be measured from the end face image 4 using the selected brightness threshold value candidate. Note that the contours of each part are extracted as a set of pixels on the monitor 140 whose brightness changes across the selected brightness threshold value candidate. Note that image processing techniques such as removing noise by taking the average of adjacent pixels in the image before contour extraction, or reducing the influence of noise by fitting the extracted contour to a known curve such as a circular arc, are well known to those skilled in the art. By appropriately utilizing these techniques, contour errors can be reduced.

[0048] The extracted feature of the contour may be structural data relating to at least one of the center of gravity position and size of the contour on the monitor 140. For example, the horizontal coordinate data of the contour center of gravity relative to an arbitrary origin on the end face image 4 may be defined as feature F1, the vertical coordinate data of the contour center of gravity may be defined as feature F2, and the radius data of the contour may be defined as feature F3. The horizontal and vertical coordinate data of the contour center of gravity are given values ​​converted into distances based on the number of pixels displayed on the monitor 140. The radius data is given as the radius of a circle having the same area as the area enclosed by the contour. Furthermore, in the case of a multi-core system such as MCF1, the median value of the coordinates of the multiple contour centers of gravity may be used as the coordinate data of the contour center of gravity. Instead of the radius of a circle having the same area as the area enclosed by the contour, the radius data may be the radius of a circle circumscribing the contour.

[0049] After performing contour extraction in step ST120 using one brightness threshold candidate, the calculation unit 110 calculates multiple feature vectors V (F1, F2, F3) for one brightness threshold candidate in step ST130. This means that the contour of the common cladding 12 and the contours of the first core 11 a and the second core 11 b are extracted from the end face image 4 using one brightness threshold candidate, and a corresponding feature vector is calculated for each extracted contour.

[0050] Therefore, in step ST140, the calculation unit 110 selects feature vectors that satisfy the structural conditions of the common cladding 12 from the plurality of feature vectors calculated in step ST130 as feature vector candidates associated with the contour of the common cladding 12, and finally, the calculation unit 110 creates a correspondence table 210 in the memory 150 that indicates the correspondence between the selected feature vector candidates and the corresponding brightness threshold candidates. This correspondence table 210 corresponds to the correspondence table 210a shown in the middle left of Figure 5 that indicates a set of feature vector candidates for the contour of the common cladding 12. Note that when the feature vector candidate calculated for contour extraction of the common cladding 12 is associated with the n-th brightness threshold candidate, V ｃｌａｄ，ｎ (F1 ｎ , F2 ｎ , F3 ｎ ), where n is an integer between 0 and 255. On the other hand, as shown in the upper left of FIG. 5 , the brightness thresholds pre-stored in memory 150 may result in the extraction of the contour of the common cladding 12 itself or in the extraction of the contour of an unnecessary portion along with the contour of the common cladding 12. Therefore, in step ST140, the calculation unit 110 evaluates whether a feature vector candidate of the desired portion can be selected (represented by an evaluation of "1" in the middle left of FIG. 5 ) or not (represented by an evaluation of "0" in the middle left of FIG. 5 ), and the calculation unit 110 also records this evaluation result in the correspondence table 210 of memory 150.

[0051] The structural condition of the common cladding 12 used for the selection in step ST140 may be, for example, that the common cladding 12 has the largest size among the contours extracted using the same brightness threshold candidate, thereby enabling the contour of the common cladding 12 to be distinguished from the contours of the first core 11a and the second core 11b. Furthermore, since the shape of the common cladding is usually circular or elliptical, the structural condition may be that the contour closely matches the fitted circle or ellipse. This reduces errors in misidentifying brightness blurring occurring around the common cladding 12 as the contour of the common cladding 12. Furthermore, if an approximate value of the radius of the cladding (typically, for example, 62.5 μm) is known, the contour radius data may be close to the approximate value of the known cladding radius.

[0052] In step ST150, it is determined whether the operations from step ST110 to step ST140 have been performed for all brightness threshold values ​​previously stored in memory 150, and ultimately, the correspondence table 210a shown in the middle left of Figure 5 is obtained.

[0053] Next, in step ST160, the feature vector V ｃｌａｄ，ｎ Among these, each of the feature vectors for which the evaluation result is "1" becomes a feature vector candidate to be used in determining the brightness threshold value in step ST170. For example, if the feature vectors are defined as three-dimensional position vectors in step ST120 as described above, feature points P1 to P4, whose positions are specified by the feature vector candidates, are defined as points in a three-dimensional coordinate system as shown in the lower left of FIG. 5. In calculating the match score for each feature vector candidate, for example, when feature point P4 is set as the target feature point, the distances from feature point P4 to the remaining peripheral feature points, that is, feature points P1 to P3, are calculated. The match score S of feature point P4 ｃｌａｄis given a value obtained by adding the reciprocals of the obtained distances. Note that instead of the reciprocal, for example, the square of the reciprocal may be used as a decreasing function. In this case, in order for each component of the feature vector to contribute to the processing result, it is preferable that the units of the values ​​of the vector components are approximately the same. More specifically, the difference in non-zero values ​​between vector components should be within a range of 10 times the vector components. The match score S ｃｌａｄ The feature point for which σ is the largest has the highest degree of coincidence with the other feature points, i.e., the feature point is the one that is closest to the other feature points. This means that the change in the feature amount of the contour is small even when the optimum brightness threshold value for extracting the contour of the common cladding 12 is changed.

[0054] More specifically, regarding the contour of the common cladding 12, a feature vector V ｃｌａｄ，ｎ The match score S of the feature point P4 whose position is specified by ｃｌａｄ As shown in the lower left of FIG. 5, the match score S for feature point P4 is given by the sum of the reciprocals of the distances between feature points P1 to P3, whose positions are specified by the other candidate feature vectors. ｃｌａｄ When calculating the distance L1 from the feature point P4 whose position is specified by the feature vector candidate (a1, a2, a3) to the feature point P1 whose position is specified by the feature vector candidate (b1, b2, b3) is calculated as ((a1-b1) 2 +(a2-b2) 2 +(a3-b3) 2 ) 1 / 2 Similarly, the distance from the feature point P4 to the feature point P2 is given by L2, and the distance from the feature point P4 to the feature point P3 is given by L3. Therefore, the matching score S of the feature point P4 is ｃｌａｄ is (1 / L1+1 / L2+1 / L3).

[0055] In step 170, the brightness threshold candidate corresponding to the feature vector candidate of the feature point with the maximum matching score among the feature vector candidates is selected as the brightness threshold value for extracting the contour of the common cladding 12. This allows automatic setting of the brightness threshold value that is optimal for identifying the contour of the common cladding 12.

[0056] Next, step 50 of the core measurement shown in FIG. 3 will also be explained using the flowchart of FIG.

[0057] In step ST110, the calculation unit 110 sets one brightness threshold value read from the brightness threshold values ​​stored in the memory 150 as a brightness threshold value candidate. In step ST120, the calculation unit 110 uses the selected brightness threshold value candidate to extract the contours of each part of the measurement object from the end face image 4. The contours of each part are extracted as a set of pixels on the monitor 140 whose brightness changes across the selected brightness threshold value candidate.

[0058] The extracted feature of the contour may be structural data relating to at least one of the center of gravity position and size of the contour on the monitor 140. For example, the horizontal coordinate data of the contour center of gravity relative to an arbitrary origin on the end face image 4 may be defined as feature F1, the vertical coordinate data of the contour center of gravity may be defined as feature F2, and the radius data of the contour may be defined as feature F3. The horizontal and vertical coordinate data of the contour center of gravity are given values ​​converted into distances based on the number of pixels displayed on the monitor 140. The radius data is given as the radius of a circle having the same area as the area enclosed by the contour. Furthermore, in the case of a multi-core system such as MCF1, the median value of the coordinates of the multiple contour centers of gravity may be used as the coordinate data of the contour center of gravity. Instead of the radius of a circle having the same area as the area enclosed by the contour, the radius data may be the radius of a circle circumscribing the contour.

[0059] After performing contour extraction in step ST120 using one brightness threshold candidate, the calculation unit 110 calculates multiple feature vectors V (F1, F2, F3) for one brightness threshold candidate in step ST130. This means that the contour of the common cladding 12 and the contours of the first core 11 a and the second core 11 b are extracted from the end face image 4 using one brightness threshold candidate, and a corresponding feature vector is calculated for each extracted contour.

[0060] Therefore, in step ST140, the calculation unit 110 selects feature vectors that satisfy the structural conditions of the first core 11a and the second core 11b from the feature vectors calculated in step ST130 as feature vector candidates associated with each contour of the first core 11a and the second core 11b. Finally, the calculation unit 110 creates a correspondence table 210 in the memory 150 that indicates the correspondence between the selected feature vector candidates and the corresponding brightness threshold candidate. This correspondence table 210 corresponds to the correspondence table 210b shown in the middle right of FIG. 5 that indicates a set of feature vector candidates for each contour of the first core 11a and the second core 11b. Note that when the feature vector candidate calculated for contour extraction of the first core 11a and the second core 11b is associated with the n-th brightness threshold candidate, V ｃｏｒｅ，ｎ (F1 ｎ，１ , F2 ｎ，１ , F3 ｎ，１ , F1 ｎ，２ , F2 ｎ，２ , F3 ｎ，２), where n is an integer between 0 and 255. On the other hand, as shown in the upper right of FIG. 5 , the brightness thresholds pre-stored in memory 150 may be such that the contours of the first core 11a and the second core 11b themselves cannot be extracted, or only the contours of the first core 11a and the second core 11b can be extracted, or unnecessary contours may be extracted along with the contours of the first core 11a and the second core 11b. Therefore, in step ST140, the calculation unit 110 evaluates whether a feature vector candidate of the desired portion was selected (represented by an evaluation "1" in the middle right of FIG. 5 ) or not (represented by an evaluation "0" in the middle right of FIG. 5 ), and the calculation unit 110 also records this evaluation result in the correspondence table 210 of memory 150.

[0061] The structural conditions of the first core 11a and the second core 11b used for the selection in step ST140 may include, for example, at least one of the conditions that the distance from the center of the common cladding 12 is within a predetermined range and the area of ​​each contour is within a predetermined range, thereby enabling the contours of the first core 11a and the second core 11b to be distinguished from bright spots caused by the common cladding 12 or noise. Furthermore, the structural conditions of the cores may include the center-to-center distance between the first core 11a and the second core 11b, or the angle formed by the line segment connecting the center of the common cladding 12 to each of the first core 11a and the second core 11b, being within a predetermined range. This reduces errors in misidentifying brightness variations in the common cladding 12 as the contours of the first core 11a and the second core 11b.

[0062] In step ST150, it is determined whether the operations from step ST110 to step ST140 have been performed for all brightness threshold values ​​previously stored in memory 150, and ultimately, the correspondence table 210b shown in the middle right of Figure 5 is obtained.

[0063] Next, in step ST160, the feature vector V ｃｏｒｅ，ｎAmong these, each feature vector with an evaluation result of "1" becomes a feature vector candidate used to determine the brightness threshold value in step ST170. For example, if the feature vector is defined as a (3 x M)-dimensional position vector including the features of M cores in step ST120 as described above, the feature vector including each of the feature values ​​of the first core 11a and the second core 11b is a six-dimensional position vector. M is an integer equal to or greater than 2. Therefore, feature points whose positions are specified by feature vector candidates including the features of the first core 11a and the second core 11b are normally defined as points in a six-dimensional coordinate system. In the lower right-hand side of FIG. 5 , feature points P1 to P4 whose positions are respectively specified by the feature vector candidates are shown as points in a three-dimensional coordinate system for simplification. In calculating the match score for each feature vector candidate, for example, when feature point P4 is set as the target feature point, the distances from feature point P4 to the remaining peripheral feature points P1 to P3 are calculated. The match score S for feature point P4 is calculated as follows: ｃｏｒｅ is given a value obtained by adding the reciprocals of the obtained distances. In this case, instead of the reciprocal, for example, the square of the reciprocal may be used as a decreasing function. In this case, in order for each component of the feature vector to contribute to the processing result, it is preferable that the units of the values ​​of the vector components are approximately the same. More specifically, the difference in non-zero values ​​between vector components should be within a range of 10 times the vector components. The match score S ｃｏｒｅ The feature point for which the value is maximum means that the degree of positional agreement with the other feature points is the greatest, that is, the feature point is the feature point that is closest to the other feature points. This corresponds to the fact that when the brightness threshold value for extracting each of the contours of the first core 11 a and the second core 11 b is changed, the change in the feature amount of the contour is small.

[0064] More specifically, for each of the contours of the first core 11a and the second core 11b in the MCF 1, a feature vector V ｃｏｒｅ，ｎis expressed as (a11, a12, a13, a21, a22, a23), for example. The match score S of the feature point P4 whose position is specified by this feature vector candidate is ｃｏｒｅ is given by the sum of the reciprocals of the distances from feature points P1 to P3, the positions of which are specified by the other candidate feature vectors. In the example shown on the lower right side of FIG. 5, only the vector components of the feature amounts relating to one core are displayed. As an example, the match score S for feature point P4 is ｃｏｒｅ When calculating the distance L1 from feature point P4, whose position is specified by feature vector candidates (a11, a12, a13, a21, a22, a23), to feature point P1, whose position is specified by feature vector candidates (b11, b12, b13, b21, b22, b23), is calculated as ((a11-b11) 2 +(a12-b12) 2 +(a13-b13) 2 +(a21-b21) 2 +(a22-b22) 2 +(a23-b23) 2 )) 1 / 2 Similarly, the distance from the feature point P4 to the feature point P2 is given by L2, and the distance from the feature point P4 to the feature point P3 is given by L3. Therefore, the matching score S of the feature point P4 ｃｌａｄ is (1 / L1+1 / L2+1 / L3).

[0065] In step 170, the brightness threshold candidate corresponding to the feature vector candidate of the feature point with the largest matching score among the feature vector candidates is selected as the brightness threshold value for extracting the contours of the first core 11 a and the second core 11 b. This makes it possible to automatically set the brightness threshold value that is optimal for identifying each contour of the first core 11 a and the second core 11 b.

[0066] Once the optimum brightness threshold value for extracting the outline of the common cladding 12 and the optimum brightness threshold value for extracting the outlines of the first core 11 a and the second core 11 b are set as described above, the display process of step 60 in FIG. 3 is performed. That is, the determined brightness threshold value includes the feature vector V ｃｌａｄ and the feature vector Vｃｏｒｅ are associated with each other, the feature vector V ｃｌａｄ The structural data and the feature vector V ｃｏｒｅ Image data is generated based on the structure data.

[0067] Specifically, as shown in FIG. 6, the control unit 100 reads the feature vector V ｃｌａｄ The structural data and the feature vector V ｃｏｒｅ The controller 100 reads out the structural data of the end face image 4 and instructs the rendering unit 130 to generate image data. The image data generated by the rendering unit 130 includes, for example, the end face image 4, contour data 410, center of gravity data 420, and numerical data 430 stored in the memory 150. The contour data 410 includes two-dimensional contour data 411 of the common cladding 12 and two-dimensional contour data 412 of the first core 11a and the second core 11b. The centroid data 420 includes two-dimensional centroid data 421 of the common cladding 12 and two-dimensional centroid data 422 of the first core 11a and the second core 11b. The controller 100 instructs the rendering unit 130 to display on the monitor 140 an image in which the contour data 410 is superimposed on the end face image 4, an image in which the center of gravity data 420 is superimposed on the end face image 4, or only the numerical data 430.

[0068] Note that the upper part of Fig. 7 shows an example of an end face image 4 to be processed. The middle part of Fig. 7 shows an example of an image in which contour data 410 is superimposed on the end face image 4. Furthermore, the lower part of Fig. 7 shows an example of an image in which center of gravity data 420 is superimposed on the end face image 4. Even in a case where the brightness of the region corresponding to the common cladding 12 is low and the brightness of the regions corresponding to the first core 11a and the second core 11b is high, as in the end face image 4 to be processed shown in the upper part of Fig. 7, and further where the brightness distribution differs between the first core 11a and the second core 11b, the core position measurement method of the present disclosure can stably and mechanically calculate the relative positions of the first core 11a and the second core 11b with respect to the common cladding 12.

[0069] Furthermore, the lower part of Figure 1 shows an example of an apparatus for observing the end face of one MCF, but the core position measurement method of the present disclosure can also be applied to a fusion splicing apparatus that observes the end faces of two opposing MCFs, measures their respective core positions, and rotates the MCFs appropriately to connect them so that their core positions match, as disclosed in Patent Document 1.

[0070] DESCRIPTION OF SYMBOLS 1...MCF 11a...First core 11b...Second core 12...Common cladding 12a...End face 13...Resin coating 2...Core position measuring device 21...Camera 22...Stage 23a, 23b...Illumination device 24...Mirror 100...Control unit 110...Calculation unit 120...Drive unit 130...Picture unit 140...Monitor 150...Memory 4, 4a, 4b...End face image 41a, 41b...Common cladding image 42a, 42b...First core image 43a, 43b...Second core image 44a, 44b...Line image 46b...Blurring 47b...Bright spot 5a, 5b...Brightness distribution 51a, 51b, 52a, 52b...Brightness threshold 53a, 53b...Common cladding center 54a, 54b...First core center 55a, 55b...second core center 200...brightness threshold value table 210, 210a, 210b...correspondence table 410...contour data 411, 412...two-dimensional contour data 420...centroid data 421, 422...two-dimensional centroid data 430...numerical data AX...fiber axis

Claims

1. 1. A core position measuring method for measuring, from an end face image of a multi-core optical fiber having a plurality of cores and a common cladding surrounding the plurality of cores, relative positions of the plurality of cores with respect to the common cladding, which has a different brightness from that of the plurality of cores in the end face image, comprising: a first step of associating one or more contours extracted from the end face image with each of a plurality of brightness threshold candidate values ​​prepared in advance, using one of the brightness threshold candidate values, and calculating one or more feature vectors that are in one-to-one correspondence with the one or more contours corresponding to the one brightness threshold candidate value; a second step of generating a first set of one or more first feature vector candidates, which are feature vectors that satisfy the structural condition of the common cladding, from among the one or more feature vectors calculated in the first step; a third step of calculating, for each of the one or more first feature vector candidates belonging to the first set, a distance from one first target feature point whose position is specified by one first feature vector candidate to first peripheral feature points whose positions are specified by the remaining first feature vector candidates, and selecting, from the plurality of brightness threshold value candidates, a brightness threshold value candidate corresponding to the first feature vector candidate that maximizes the degree of coincidence between the position of the first target feature point and the first peripheral feature point, as a first brightness threshold value for extracting the contour of the common cladding; a fourth step of generating a second set of second feature vector candidates, which are feature vectors that satisfy the structural conditions of the plurality of cores, from the one or more feature vectors calculated in the first step; a fifth step of calculating, for each of the second feature amount vector candidates belonging to the second set, a distance from one second target feature point whose position is specified by one second feature amount vector candidate to second peripheral feature points whose positions are specified by the remaining second feature amount vector candidates, and selecting, from the plurality of brightness threshold value candidates, a brightness threshold value candidate corresponding to the second feature amount vector candidate that maximizes the degree of coincidence between the position of the second target feature point and the second peripheral feature point, as a second brightness threshold value for extracting contours of the plurality of cores; Equipped with each of the one or more feature vectors calculated in the first step is a position vector whose components are represented by a plurality of structure data quantified as feature amounts of corresponding contours of the one or more contours; wherein, when m is an integer of 2 or more and the number of components of the feature amount vector is m, each of the first target feature point, the first surrounding feature point, the second target feature point, and the second surrounding feature point is defined as a point in an m-dimensional spatial coordinate system having an origin that is a starting point of the one or more feature amount vectors, the degree of match of the position of the first target feature point is evaluated based on a match score obtained by adding a decreasing function of the distance to each of the first peripheral feature points calculated for the first target feature point; the degree of match of the position of the second target feature point is evaluated based on a match score obtained by adding a decreasing function of the distance to each of the second peripheral feature points calculated for the second target feature point; measuring relative positions of the plurality of cores with respect to the common cladding based on the structure data of the first feature quantity vector candidate corresponding to the first brightness threshold selected in the third step and the structure data of the second feature quantity vector candidate corresponding to the second brightness threshold selected in the fifth step; Core location measurement method.

2. the plurality of brightness threshold candidates are prepared as a plurality of first brightness threshold candidates for contour extraction of the common cladding and a plurality of second brightness threshold candidates for contour extraction of the plurality of cores; The first step a sixth step of calculating a feature vector to be selected in the second step for each of the plurality of first brightness threshold candidates; a seventh step of calculating a feature vector to be selected in the fourth step for each of the plurality of second brightness threshold candidates; Including, The core position measurement method according to claim 1 .

3. displaying at least one of the numerical value of the structure data of the first feature quantity vector candidate corresponding to the first brightness threshold value, image data generated based on the structure data of the first feature quantity vector candidate corresponding to the first brightness threshold value, the numerical value of the structure data of the second feature quantity vector candidate corresponding to the second brightness threshold value, and image data generated based on the structure data of the second feature quantity vector candidate corresponding to the second brightness threshold value; The core position measuring method according to claim 1 or 2.

4. the plurality of brightness threshold candidate ranges from maximum brightness to minimum brightness of the end face image; The core position measuring method according to claim 1 or 2.

5. the plurality of structure data include data relating to at least one of a center of gravity position and a size of the corresponding contour on the end face image; The core position measuring method according to claim 1 or 2.

6. a calculation unit that performs the core position measurement method according to claim 1 or 2; and a memory that stores the plurality of brightness threshold value candidates in advance and a correspondence table calculated by the calculation unit; Equipped with the correspondence tables include a first correspondence table indicating a correspondence relationship between the first feature quantity vector candidates and brightness threshold candidates corresponding to the first feature quantity vector candidates, and a second correspondence table indicating a correspondence relationship between the second feature quantity vector candidates and brightness threshold candidates corresponding to the second feature quantity vector candidates. Core position measuring device.

7. 1. A core position measuring device for measuring, from an end face image of a multi-core optical fiber having a plurality of cores and a common cladding surrounding the plurality of cores, relative positions of the plurality of cores with respect to the common cladding, which has a brightness different from that of the plurality of cores in the end face image, a memory storing a plurality of brightness threshold candidates; a calculation unit that selects a first brightness threshold value for extracting the outline of the common cladding and a second brightness threshold value for extracting the outlines of the plurality of cores; Equipped with The calculation unit a first step of associating one or more contours extracted from the end face image using one brightness threshold candidate for each of a plurality of brightness threshold candidate read from the memory, and calculating one or more feature vectors that are in one-to-one correspondence with the one or more contours corresponding to the one brightness threshold candidate; a second step of storing in the memory a first correspondence table indicating correspondence between one or more first feature vector candidates, which are feature vectors that satisfy the structural condition of the common cladding, among the one or more feature vectors calculated in the first step, and brightness threshold value candidates corresponding to the one or more first feature vector candidates, among the plurality of brightness threshold value candidates; a third step of calculating, for each of the one or more first feature amount vector candidates in the first correspondence table, a distance from one first target feature point whose position is specified by the one first feature amount vector candidate to each of first peripheral feature points whose positions are specified by the remaining first feature amount vector candidates, and selecting, from the plurality of brightness threshold value candidates, a brightness threshold value candidate corresponding to the first feature amount vector candidate which maximizes the degree of coincidence between the position of the first target feature point and the first peripheral feature point; a fourth step of storing in the memory a second correspondence table indicating correspondence between second feature vector candidates, which are feature vectors that satisfy the structural conditions of the plurality of cores among the one or more feature vectors calculated in the first step, and brightness threshold value candidates that correspond to the second feature vector candidates among the plurality of brightness threshold value candidates; a fifth step of calculating, for each of the second feature amount vector candidates in the second correspondence table, a distance from one second target feature point whose position is specified by one second feature amount vector candidate to each of the second peripheral feature points whose positions are specified by the remaining second feature amount vector candidates, and selecting, from the plurality of brightness threshold value candidates, a brightness threshold value candidate corresponding to the second feature amount vector candidate which maximizes the degree of coincidence between the position of the second target feature point and the second peripheral feature point, as the second brightness threshold value; and each of the one or more feature vectors calculated in the first step is a position vector whose components are represented by a plurality of structural data quantified as feature amounts of the corresponding contour; wherein, when m is an integer of 2 or more and the number of components of the feature amount vector is m, each of the first target feature point, the first surrounding feature point, the second target feature point, and the second surrounding feature point is defined as a point in an m-dimensional spatial coordinate system having an origin that is a starting point of the one or more feature amount vectors, the degree of match of the position of the first target feature point is evaluated based on a match score obtained by adding a decreasing function of the distance to each of the first peripheral feature points calculated for the first target feature point; the degree of match of the position of the second target feature point is evaluated based on a match score obtained by adding a decreasing function of the distance to each of the second peripheral feature points calculated for the second target feature point; The calculation unit measuring relative positions of the plurality of cores with respect to the common cladding based on the structure data constituting the first feature quantity vector candidate corresponding to the first brightness threshold value selected in the third step and the structure data constituting the second feature quantity vector candidate corresponding to the second brightness threshold value selected in the fifth step; Core position measuring device.

8. the plurality of brightness threshold candidate values ​​are stored in the memory, the plurality of first brightness threshold candidate values ​​for contour extraction of the common cladding, and the plurality of second brightness threshold candidate values ​​for contour extraction of the plurality of cores; The calculation unit, as the first step, a sixth step of calculating a feature vector to be selected in the second step for each of the plurality of first brightness threshold candidates; a seventh step of calculating a feature vector to be selected in the fourth step for each of the plurality of second brightness threshold candidates; implement, The core position measuring device according to claim 7.

9. a monitor on which at least one of the numerical values ​​of the structure data of the first feature quantity vector candidate corresponding to the first brightness threshold value, image data generated based on the structure data of the first feature quantity vector candidate corresponding to the first brightness threshold value, the numerical values ​​of the structure data of the second feature quantity vector candidate corresponding to the second brightness threshold value, and image data generated based on the structure data of the second feature quantity vector candidate corresponding to the second brightness threshold value is displayed; The core position measuring device according to claim 7 or 8.

10. the plurality of brightness threshold candidate ranges from maximum brightness to minimum brightness of the end face image; The core position measuring device according to claim 7 or 8.

11. the plurality of structure data include data relating to at least one of a center of gravity position and a size of the corresponding contour on the end face image; The core position measuring device according to claim 7 or 8.