Measuring instrument, surface evaluation index calculation method and program

Abstract

A measuring instrument comprises: a single illumination means (1) capable of radiating illumination light to an object to be measured; a two-dimensional photoelectric conversion means (3) disposed in a position in which light regularly reflected from the object to be measured (100) from the illumination light radiated from the illumination means (1) to the object to be measured can be received; a shape distribution acquisition means (4) for acquiring a surface shape distribution of the object to be measured, on the basis of an electrical signal obtained by the two-dimensional photoelectric conversion means (3); a luminance distribution acquisition means (4) for acquiring a two-dimensional luminance distribution of the object to be measured, on the basis of the electrical signal obtained by the two-dimensional photoelectric conversion means (4); and a calculation means (4) for calculating a surface evaluation index for the object to be measured using at least either the surface shape distribution (101) of the object to be measured acquired by the luminance distribution acquisition means, or the two-dimensional luminance distribution (102) of the object to be measured acquired by the luminance distribution acquisition means.

Description

[Technical Field]

[0001] The present invention relates to a measuring instrument capable of determining an evaluation index for the surface of, for example, a textured automobile interior part, a method for calculating a surface evaluation index, and a program. [Background technology]

[0002] Automotive interior parts that have been given surface treatments such as graining are subject to quality control using color, gloss, etc. as control indices. However, in order to control texture that cannot be quantified by color or gloss alone, there has been a recent trend to measure two-dimensional gloss information and height information (same as shape information) of the interior part surface and create indices that match the inspector's visual inspection.

[0003] Patent Document 1 discloses a surface roughness and contour shape measuring device that measures the surface roughness and contour shape (height information) of an object by moving a pickup with a stylus along the surface of the object to be measured, converting the displacement of the stylus into an electrical signal, and reading it with a computer.

[0004] Specifically, by moving the pickup and the object being measured relatively, it is possible to measure the contour shape in two directions, i.e., the XY plane. By performing calculations on the acquired three-dimensional height information (topography), it is possible to calculate surface roughness indices Ra, Sa, etc., as specified in ISO-4287, ISO-25178, etc.

[0005] However, in the technology described in Patent Document 1, A) Since only one-dimensional information is acquired while the pickup is moving, it takes time to measure two-dimensional information. B) Two-dimensional gloss distribution cannot be obtained, so visual correlation cannot be guaranteed. There are challenges such as these.

[0006] For measurement objects with deep unevenness, such as those with a textured finish, there is a high visual correlation between the change in gloss due to the unevenness, and if information about the unevenness can be obtained from the two-dimensional gloss distribution and surface shape distribution, it is possible to visualize and quantify information corresponding to the unevenness of the measurement object.

[0007] Thus, the device described in Patent Document 1 is insufficient for quality control with high visual correlation according to the unevenness of an object to be measured that has been subjected to a textured finish or the like.

[0008] Meanwhile, Patent Document 2 proposes an apparatus capable of acquiring the surface profile distribution and two-dimensional glossiness of a measurement target, such as an automobile interior part. This apparatus measures the surface profile distribution based on the "photometric stereo method." To acquire the surface profile distribution, four light sources (2) are arranged in directions perpendicular to the left and right and front and rear directions, and each illuminates the sample (10) at a 45° angle from the normal direction of the sample. The illumination light is received by a light receiver (4) arranged normal to the sample, and the surface inclination of the sample is estimated from the four pieces of information corresponding to each light source. However, this estimation is based on the assumption that the light reflection characteristics of the sample are cosine (Lambertian) characteristics.

[0009] To obtain two-dimensional glossiness, illumination light from another light source (6) is reflected by a beam splitter (18) to illuminate the sample (10) from a perpendicular direction, and the illumination light is received by a light receiver (4). [Prior art documents] [Patent documents]

[0010] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-118911 [Patent Document 2] US Patent Publication No. 2015 / 0369595 Summary of the Invention [Problem to be solved by the invention]

[0011] However, although the device described in Patent Document 2 can acquire the surface shape distribution and two-dimensional gloss, the following problems arise. 1. When the measurement target has a large difference in intensity between specularly reflected light and diffusely reflected light, processing to ensure the dynamic range on the sensor side is necessary, which may result in a poor signal-to-noise ratio and reduced accuracy of the surface evaluation index. 2. Surface shape distribution measurement and two-dimensional gloss measurement are performed using different illumination optical systems. Due to the presence of multiple light sources, individual differences can cause variations in the illuminance on the measurement object, which can reduce the accuracy of the reconstruction of the surface shape distribution and the accuracy of the surface evaluation index.

[0012] The present invention has been made in consideration of the above technical background, and aims to provide a measuring instrument, a method for calculating a surface evaluation index, and a program that can determine the surface evaluation index of a measurement object with high accuracy. [Means for solving the problem]

[0013] The above object can be achieved by the following means: (1) a single illumination means capable of irradiating an illumination light onto an object to be measured; a two-dimensional photoelectric conversion means disposed at a position capable of receiving specularly reflected light from the measurement object of illumination light irradiated onto the measurement object from the illumination means; a shape distribution acquisition means for acquiring a surface shape distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a luminance distribution acquisition means for acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a calculation means for calculating a surface evaluation index of the measurement object using at least one of the surface shape distribution of the measurement object acquired by the shape distribution acquisition means and the two-dimensional luminance distribution of the measurement object acquired by the luminance distribution acquisition means; Equipped with 、 The calculation means performs a first region division to divide the surface shape distribution into a plurality of closed regions using a watershed segmentation method according to the height of the surface shape distribution before calculating the surface evaluation index. Measuring instrument. (2) a shape distribution acquisition means for acquiring a surface shape distribution of the measurement object based on an electrical signal obtained by a two-dimensional photoelectric conversion means disposed at a position capable of receiving specularly reflected light from the measurement object of illumination light irradiated onto the measurement object from a single illumination means; a luminance distribution acquisition means for acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a calculation means for calculating a surface evaluation index of the measurement object using at least one of the surface shape distribution of the measurement object acquired by the shape distribution acquisition means and the two-dimensional luminance distribution of the measurement object acquired by the luminance distribution acquisition means; Equipped with 、 The calculation means performs a first region division to divide the surface shape distribution into a plurality of closed regions using a watershed segmentation method according to the height of the surface shape distribution before calculating the surface evaluation index. Measuring instrument. (3) A measuring instrument according to paragraph 1 or 2, wherein the shape distribution acquisition means acquires the surface shape distribution of the object by calculation based on the electrical signal obtained by the two-dimensional photoelectric conversion means when the object is irradiated with illumination light of a plurality of sinusoidal wave patterns with different phases in a time-division manner by the illumination means and the specularly reflected light is received by the two-dimensional photoelectric conversion means. (4) A measuring instrument according to paragraph 1 or 2, wherein the luminance distribution acquisition means acquires the luminance distribution of the object to be measured by calculation based on the electrical signal obtained by the two-dimensional photoelectric conversion means when the object to be measured is irradiated with illumination light of a constant value pattern by the illumination means and the specularly reflected light is received by the two-dimensional photoelectric conversion means. (5) The measuring instrument according to the above item 1 or 2, wherein the spatial resolution of the two-dimensional photoelectric conversion means is 100 μm or less. (6) The surface of the measurement object is textured, 3. The measuring instrument according to claim 1 or 2, wherein the surface evaluation index is an index relating to a feature quantity of a textured surface. ( 7 The calculation means performs low-frequency filtering on the surface shape distribution to remove high-frequency components before executing the first region division. 1 or 2 A measuring instrument as described in ( 8) The calculation means deletes coordinate information representing closed areas adjacent to an edge of the image from an image representing a plurality of closed areas obtained as a result of the first area division. 1 or 2 A measuring instrument as described in ( 9 The calculation means performs Otsu's binarization on the surface shape distribution before calculating the surface evaluation index, thereby obtaining coordinate information of the uneven portions of the measurement object. of 3. The measuring device according to claim 1 or 2. ( 10 ) The calculation means, before calculating the surface evaluation index, ,before performing Otsu's binarization of the surface shape distribution to perform a second region division to acquire coordinate information of the uneven portions of the measurement object; 3. The measuring instrument according to claim 1 or 2, wherein the surface evaluation index is calculated using one or more of the image obtained as a result of the first region division, the image obtained as a result of the second region division, the surface shape distribution, and the two-dimensional luminance distribution. ( 11 The surface evaluation index is the number of closed areas in the image obtained by the first area division. 1 or 2 A measuring instrument as described in ( 12 The surface evaluation index is the average area of ​​the closed area within the image obtained by the first region division. 1 or 2 A measuring instrument as described in ( 13 The surface evaluation index is the average value of heights in the coordinate set within the surface shape distribution, calculated from coordinate information representing a closed area within the image obtained by the first area division. 1 or 2 A measuring instrument as described in ( 14 ) The calculation means, before calculating the surface evaluation index, ,before performing Otsu's binarization of the surface shape distribution to perform a second region division to acquire coordinate information of the uneven portions of the measurement object; 3. The measuring instrument according to claim 1 or 2, wherein the surface evaluation index is expressed as an average area of ​​a set of coordinates within a closed region, which is calculated from a plurality of closed regions within the image obtained by the first region division and coordinate information representing convex or concave portions obtained by the second region division. ( 15 The surface evaluation index is the average value of brightness in a set of coordinates in the two-dimensional luminance distribution, which is obtained from a set of coordinates representing the convex or concave portions obtained by the second region division. 9 A measuring instrument as described in ( 16 The surface evaluation index is obtained by calculating the contrast between the index values ​​calculated for the convex and concave portions obtained by the second region division. 9 A measuring instrument as described in ( 17 The surface evaluation index is the index obtained by calculating the average value of the absolute value of the deviation from a reference surface in the surface shape distribution or the two-dimensional luminance distribution from the convex portion or the coordinate set representing the convex portion obtained by the second region division. 9 A measuring instrument as described in ( 18 The surface evaluation index is the index obtained by calculating the square root of the average value of the squares of deviations from a reference surface in a coordinate set representing a convex portion or a concave portion obtained by the second region division within the surface shape distribution or the two-dimensional luminance distribution. 9 A measuring instrument as described in ( 19 The surface evaluation index is the average value of wavelength components included in a set of coordinates in the surface shape distribution, which is obtained from a set of coordinates representing convex or concave portions obtained by the second area division. 9 A measuring instrument as described in ( 20 The surface evaluation index is the value obtained by calculating the square root of the average value of the squares of the deviations of the surface shape distribution from a reference plane in the horizontal or vertical direction within the surface shape distribution or the two-dimensional luminance distribution from a coordinate set representing the convex or concave portions obtained by the second region division.9 A measuring instrument as described in ( 21 The surface evaluation index is the average value of the radius of curvature of the convex portion obtained from the coordinate set representing the convex portion obtained by the second region division. 9 A measuring instrument as described in ( 22 3. The measuring instrument according to claim 1 or 2, wherein the surface evaluation index represents the directionality of the streak surface of the unevenness in the surface shape distribution. ( 23 The surface evaluation index is the average value of the local gradient in the coordinate set within the surface shape distribution, which is calculated from the coordinate set representing the convex portion obtained by the second region division. 9 A measuring instrument as described in ( 24 ) a shape distribution acquisition means of the measuring instrument according to the preceding paragraph 1 or 2 executes a shape distribution measurement step of acquiring a surface shape distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a luminance distribution acquisition means for acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; The calculation means executes a step of calculating a surface evaluation index of the measurement object using at least one of the surface shape distribution of the measurement object acquired in the shape distribution measurement step and the two-dimensional luminance distribution of the measurement object acquired in the luminance distribution measurement step. death, The calculation means performs a first region division to divide the surface shape distribution into a plurality of closed regions using a watershed segmentation method according to the height of the surface shape distribution before calculating the surface evaluation index. Method for calculating surface evaluation index. ( 25 ) acquiring a surface shape distribution of the measurement object based on an electrical signal obtained by a two-dimensional photoelectric conversion means disposed at a position capable of receiving specularly reflected light from the measurement object of illumination light irradiated onto the measurement object from a single illumination means; acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a calculation step of calculating a surface evaluation index of the measurement object using at least one of the acquired surface shape distribution and two-dimensional luminance distribution of the measurement object; Before calculating the surface evaluation index, a first region division step is performed in which the surface shape distribution is divided into a plurality of closed regions according to their heights using a watershed segmentation method; A program that causes a computer to execute the following. [Effects of the Invention]

[0014] According to the measuring instrument and the method for calculating a surface evaluation index of this invention, the two-dimensional conversion element is positioned at a position where it can receive the specularly reflected light from the object to be measured of the illumination light irradiated onto the object from the illumination means, so even if the object to be measured has a large difference in intensity between the specularly reflected light and the diffusely reflected light, processing to ensure a dynamic range on the two-dimensional photoelectric conversion unit is not required, so a high SN can be maintained and a highly accurate surface evaluation index can be obtained. Moreover, because the surface shape distribution can be obtained using a single illumination means, there is no variation in illuminance on the object to be measured, so the surface shape distribution can be obtained stably and the reproducibility of the calculated index can be expected to be ensured.

[0015] Furthermore, according to the program of the present invention, a computer can be made to execute the steps of: acquiring a surface shape distribution of the object to be measured based on an electrical signal obtained by a two-dimensional photoelectric conversion means arranged at a position where it can receive specularly reflected light from the object to be measured of illumination light irradiated onto the object from a single illumination means; acquiring a two-dimensional luminance distribution of the object to be measured based on the electrical signal obtained by the two-dimensional photoelectric conversion means; and calculating a surface evaluation index of the object to be measured using at least one of the acquired surface shape distribution and two-dimensional luminance distribution. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a block diagram showing the configuration of a measuring device according to an embodiment of the present invention. [Figure 2] FIG. 2 is a perspective view showing the appearance of the measuring device. [Figure 3] 10A and 10B are diagrams for explaining a flow up to calculation of a surface evaluation index using at least one of a surface shape distribution and a two-dimensional luminance distribution of a measurement object. [Figure 4] FIG. 10 is a diagram showing an image in which lines are drawn on the surface shape distribution to divide it into cells. [Figure 5]FIG. 1 is a diagram illustrating a watershed segmentation method. [Figure 6] FIG. 10 is a diagram for explaining a surface evaluation index 2. [Figure 7] FIG. 10 is a diagram for explaining surface evaluation indices 5 and 6. [Figure 8] FIG. 10 is a diagram for explaining surface evaluation indexes 7 to 9. [Figure 9] 1(a) and 1(b) are explanatory diagrams relating to surface evaluation indexes 10 to 13. [Figure 10] 10(a) and 10(b) are explanatory diagrams relating to surface evaluation indexes 14 to 17. [Figure 11] FIG. 2 is a diagram for explaining a surface evaluation index 18. [Figure 12] FIG. 2 is an explanatory diagram relating to surface evaluation indexes 20 to 23. [Figure 13] FIG. 10 is an explanatory diagram relating to the surface evaluation indexes 20 to 23. [Figure 14] FIG. 2 is a diagram for explaining a surface evaluation index 28. [Figure 15] FIG. 2 is a diagram for explaining a surface evaluation index 29. [Figure 16] FIG. 10 is a diagram for explaining a problem that occurs when HV segmentation is performed by binarization. [Figure 17] These are schematic diagrams of HV segmentation performed with ternary or higher values, where (a) shows the draft angle when removing the grain from the mold, and (b) shows the change from the concave to the convex portion. [Figure 18] 10A and 10B are explanatory diagrams of expansion and contraction processes when opening processing is performed as noise processing in HV segmentation. [Figure 19] FIG. 1 is an explanatory diagram of Voronoi division. [Figure 20] FIG. 10 is a diagram for explaining post-processing for deleting surrounding cells in cell segmentation. [Figure 21] FIG. 10 is an explanatory diagram illustrating a case where a surface shape distribution is subjected to a blurring filter process in order to improve the accuracy of segmentation. [Figure 22]FIG. 10 is an explanatory diagram of the application of Wolf pruning when watershed segmentation is used as cell segmentation. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0018] FIG. 1 is a block diagram showing the configuration of a measuring device according to an embodiment of the present invention.

[0019] The measuring instrument shown in Figure 1 comprises a single illumination display device 1, an objective lens 2, a two-dimensional photoelectric conversion unit 3 which is a two-dimensional imaging element such as a CCD sensor, a calculation unit 4, and a calculation result display unit 5 which is composed of a liquid crystal display device or the like.

[0020] The illumination display device 1 displays an illumination pattern, and irradiates illumination light L1 from the displayed illumination pattern onto a measurement site 100a of an object to be measured (also simply referred to as a sample) 100.

[0021] The two-dimensional photoelectric conversion unit 3 has a large number of pixels, and receives reflected light L2 from the sample 100 for each pixel via the objective lens 2, converts it into image data, and outputs it.

[0022] In this embodiment, the lighting display device 1 and the two-dimensional photoelectric conversion unit 3 are positioned such that the two-dimensional photoelectric conversion unit 3 can receive reflected light of the specular reflection component, which is specular reflection from the surface of the measurement site 100a. In other words, they are positioned such that the angle between the normal to the measurement site 100a of the sample 100 and the normal to the lighting display device 1 is approximately the same as the angle between the normal to the measurement site 100a of the sample 100 and the two-dimensional photoelectric conversion unit 3. It is also desirable that the surface of the measurement site 100a of the sample 100 (sample surface) and the two-dimensional photoelectric conversion unit 3 are in a conjugate relationship.

[0023] The image data, which is an electrical signal output from the two-dimensional photoelectric conversion unit 3, is converted into a digital signal through an IV conversion circuit and an AD conversion circuit (not shown) as necessary, and sent to the calculation unit 4. Note that the conversion of the image data output from the two-dimensional photoelectric conversion unit 3 into a digital signal may also be performed by the calculation unit 4.

[0024] The calculation unit 4 uses the transmitted image data to acquire the surface shape distribution and two-dimensional luminance distribution of the sample 100 by means of a CPU or the like. The acquisition method will be described later. The calculation unit 4 further calculates a surface evaluation index (hereinafter simply referred to as an index) of the sample 100 using one or both of the acquired surface shape distribution and two-dimensional luminance distribution of the sample 100.

[0025] The calculation unit 4 may be a dedicated device or may be configured with a personal computer. Furthermore, the image data output from the two-dimensional photoelectric conversion unit 3 and processed into a digital signal may be sent to the calculation unit 4 via a network. In this case, even if the calculation unit 4 is located at a location distant from the measurement site, the index can be calculated.

[0026] Furthermore, if the measurement target is an automobile interior part, the size of the grain on a molded product produced by texturing is thought to be less than 200 μm for the smallest and about 2000 μm for the largest for a typical shape. It is preferable that the measuring device be able to spatially distinguish the unevenness of the texturing.

[0027] From the above, it is desirable that the spatial resolution of the two-dimensional photoelectric conversion unit 3 is 100 μm or less in consideration of the Nyquist theorem.

[0028] 2 is a perspective view showing the appearance of a measuring device according to one embodiment of the present invention. In this embodiment, the measuring device is configured as a portable, handy type.

[0029] Specifically, an illumination display device 1, an objective lens 2, a two-dimensional photoelectric conversion unit 3, and a calculation unit 4 are housed in a housing 8. The top surface of the housing 8 is provided with a handle 82 for carrying and a calculation result display unit 5 for displaying calculation results, and the bottom surface of the housing 8 is formed with an opening 81 for irradiating a measurement site 100a of the sample 100 with illumination light and capturing reflected light from the measurement site.

[0030] 2 is used by gripping the grip portion 82 and positioning the opening 81 on the bottom surface at the measurement site 100a of the sample 100. In this state, illumination light is irradiated onto the sample 100 from the illumination display device 1 housed inside the housing 8, and the reflected light is received by the two-dimensional photoelectric conversion unit 3. The surface shape distribution and two-dimensional luminance distribution are obtained by the calculation unit 5 using the image data output from the two-dimensional photoelectric conversion unit 3, and an index is calculated using at least one of these distributions, with the calculation result displayed on the calculation result display unit 5.

[0031] With such a measuring instrument, the surface shape distribution and two-dimensional luminance distribution can be acquired regardless of location by simply carrying the housing, and the indices can be calculated. Note that a benchtop measuring instrument may be used instead of a handheld type measuring instrument. [Example 1]: Example of image processing algorithm and calculation example of surface evaluation index Next, the flow of calculating the surface evaluation index using at least one of the surface shape distribution and the two-dimensional brightness distribution of the sample 100 will be described in the order of (1) image processing algorithm and (2) indexing. However, it is assumed that the two-dimensional brightness distribution can be obtained under constant exposure conditions.

[0032] Image processing algorithms The following describes the image processing algorithm up to the point just before calculating the index representing the characteristics of the sample 100 using the surface shape distribution and the two-dimensional brightness distribution.

[0033] The overall flow of the process is shown in Figure 3. It is assumed that two pieces of information, a surface shape distribution 101 and a two-dimensional brightness distribution 102 of a sample 100, are already stored at the start of the process.

[0034] First, cell-segmentation is performed on the surface topography distribution 101. This process involves applying noise reduction to the surface topography distribution 101, and then "segmenting" the surface topography distribution 101 into regions according to the surface roughness of the sample 100. This is achieved by connecting areas of locally low values ​​of the roughness of the surface topography distribution 101. Conceptually, as shown in image 104 in Figure 4, this corresponds to drawing a white line 103 on the surface topography distribution 101 and assigning a label value to each segmented region, as in image 105 segmented into cells (hereinafter referred to as cell labels 105). Here, each segmented region is referred to as a cell 105a.

[0035] This method can be realized by using an algorithm called watershed segmentation, as shown in Figure 5. In this algorithm, the input image is first inverted, and the distribution of the image is considered as peaks and valleys. Water is poured from above until a certain water level is reached, and the pixels where different puddles meet are used as dividing lines and boundaries to create cells. Furthermore, by carefully adjusting the water level, it is possible to deal with cases where the surface shape distribution 101 contains outliers. Additionally, noise reduction methods include (1) applying a blur filter and (2) wolf pruning, but these are not limited to these.

[0036] Next, HV segmentation is performed on the surface shape distribution 101, as indicated by reference numeral 106 in FIG. 3. This refers to processing of the surface shape distribution 101 to perform noise processing and classify its height into two or more categories using a certain threshold. As a result, an image 106 (hereinafter also referred to as HV mask 106) containing information on relatively high parts (hills) and low parts (valleys) of the surface shape distribution 101 can be obtained. The threshold may be determined manually when performing HV segmentation, or may be calculated automatically. One example of a processing method is the well-known Otsu binarization process. Noise processing techniques include, but are not limited to, blur filtering and expansion / contraction processing before performing HV segmentation.

[0037] At least one of the four pieces of information obtained by the above processing, namely, cell-labels 105, HV mask 106, surface shape distribution 101, and two-dimensional brightness distribution 102, is used to calculate an index representing the feature amount of the sample 100.

[0038] · Indexing Here, a method for calculating an index for the sample 100 using at least one of the above four pieces of information will be described. Index 1 [unit is pieces] This index 1 is the number of cells 105a in the size of the surface shape distribution 101 and the two-dimensional brightness distribution 102. It is used when the samples being compared are of the same size. It can be calculated by counting the number of types of label values ​​assigned to each cell 105a. Index 2 [unit: pieces / mm 2 ] As shown in Figure 6, it represents the number of cells 105a per unit area. After calculating index 1, it is divided by the size of the surface shape distribution 101 (SizeX × SizeY) to calculate a value that is not affected by the size of the surface shape distribution / two-dimensional gloss. Index 3 [unit: mm 2 ] It represents the average area of ​​the cells 105a. It can be calculated by counting the number of times the same label value assigned to the Cell-labels 105 appears for each cell 105a and averaging them. Index 4 [unit: μm] This represents the average height of the cells 105a. Within the coordinate set corresponding to one cell 105a, the maximum and minimum values ​​of the surface shape distribution 101 are extracted, and the difference between them is calculated. This calculation is repeated for each cell 105a, and the results are averaged to calculate the average height. Index 5 [unit: mm 2 ] This is the average area of ​​the parts of each cell 105a that are determined to be Hills. As an image, as shown in the right diagram of Figure 7, it can be calculated by calculating the area of ​​Hills (light-colored parts) for each cell 105a and averaging them. Index 6 [unit: mm 2 ] This is the average area of ​​the valley part of each cell 105a. As an image, as shown in the right diagram of Figure 7, it can be calculated by calculating the area of ​​the valley (dark part) for each cell 105a and averaging them. ·Indicator 7 8, it is the average brightness value in a relatively high part (Hill) of the height of the sample 100. It can be calculated from the product of the two-dimensional brightness distribution 102 and the H-Vmask 106. ·Indicator 8 As shown in Figure 8, this is the average brightness value in a relatively low part (valley) in the height of the sample 100. It can be calculated from the element product of the image obtained by inverting the two-dimensional brightness distribution 102 and the H-Vmask 106. ·Indicator 9 As shown in Figure 8, it is calculated by calculating the contrast between index 7 and index 8. Index 10: Sa_TopoHill [unit: μm] It represents the average "roughness" of the hill region in the surface shape distribution 101. It can be calculated by averaging the absolute value of the height deviation of the surface shape distribution 101 with respect to the reference surface of the hill region. Index 11: Sa_TopoValley [unit: μm] It represents the average "roughness" of a valley area in terms of surface shape distribution. It can be calculated by averaging the absolute value of the deviation in height of the surface shape distribution with respect to the reference plane of the valley area. Index 12: Sa_RefHill [unit: μm] It represents the average absolute value of deviation in the reference plane of the region that is a Hill for the two-dimensional luminance distribution 101. · Index 13: Sa_RefValley [unit: μm] It represents the average absolute value of deviation in the reference plane of a valley area for the two-dimensional luminance distribution 101.

[0039] However, there are two methods for calculating indicators 10 and 12, depending on how the reference plane is taken: (a) A method in which a reference plane is set using the entire area of ​​the hill, and the index is calculated using the deviation from that reference plane (see Figure 9(a)). (b) A method in which a unique reference plane is set for each hill region, and the index is calculated for each region and averaged (see Figure 9(b)). At the HV segmentation stage, you can switch between methods (a) and (b) depending on whether you want to treat each Hill collectively or not. Comparing methods (a) and (b), (b) treats each Hill individually, so it is expected that calculations will be more accurate.

[0040] In the case of the Valleys indicated by indicators 11 and 13, as in the case of the Hills shown in Figures 9(a) and (b), the reference plane may be set using the entire Valley area, or a unique reference plane may be set for each Valley area. Index 14: Sq_TopoHill [unit: μm] It represents the average "roughness" of the hill region with respect to the surface shape distribution 101. It can be calculated by taking the root mean square of the deviation of the height of the surface shape distribution with respect to the reference surface of the hill region. Index 15: Sq_TopoValley [unit: μm] It represents the average "roughness" of a valley area in terms of surface shape distribution. It can be calculated by taking the root mean square of the deviation of the height of the surface shape distribution from the reference plane of the valley area. Index 16: Sq_RefHill [unit: μm] This represents the average "roughness" of a hill area in a two-dimensional brightness distribution. It can be calculated by taking the root mean square of the deviation of the height of the surface shape distribution in relation to the reference surface of the hill area. Index 17: Sq_RefValley [unit: μm] This represents the average "roughness" of a valley area in a two-dimensional brightness distribution. It can be calculated by taking the root mean square of the deviation of the height of the surface shape distribution in the valley area relative to the reference plane.

[0041] However, there are two methods for calculating indicators 14 and 16, depending on how the reference plane is taken: (a) A method in which a reference plane is set using the entire area of ​​the hill, and the index is calculated using the deviation from that reference plane (see Figure 10(a)). (b) A method in which a unique reference plane is set for each hill region, and the index is calculated for each region and averaged (see Figure 10(b)). At the HV segmentation stage, you can switch between methods (a) and (b) depending on whether you want to treat each Hill collectively or not. Comparing methods (a) and (b), (b) treats each Hill individually, so it is expected that calculations will be more accurate.

[0042] In the case of the Valleys indicated by indicators 15 and 17, as in the case of the Hills shown in Figures 10(a) and (b), the reference plane may be set using the entire Valley area, or a unique reference plane may be set for each Valley area. Index 18: RsM_TopoHill [unit: mm] As shown in Figure 11, this represents the average period of the hill portion of the surface shape distribution 101. For a certain hill, the intersection points between the reference surface and the surface shape distribution 101 are counted, and each time the count reaches an even number, they are averaged as one period. This can be calculated by repeating this process for all hills and averaging them. The same applies to the index 19 for the valley shown next. Index 19: RsM_TopoValley [unit: mm] This represents the average period of the valley portion of the surface shape distribution 101. For a certain valley, the intersections between the reference surface and the surface shape distribution 101 are counted, and each time the count reaches an even number, they are averaged as one period. This process is repeated for all valleys, and the average is calculated.

[0043] The following four indices 20 to 23 are calculated in the X and Y directions shown in FIG. 12, and these indices can quantify the directionality of roughness in the Hill / Valley region in the surface shape distribution 101. · Index 20: RaX_TopoHill [unit: μm] This represents the "roughness" in the X direction of the hill portion of the surface shape distribution 101. When there is a hill portion (area 1, 2, 3, 4, ...) in the surface shape distribution 101 as shown in the lower diagram of Fig. 13, it is calculated using the formula for calculating RaX_TopoHill in Fig. 13. · Index 21:RaY_TopoHill [unit: μm] This represents the "roughness" in the Y direction of the hill portion of the surface shape distribution 101. When there is a hill portion (area 1, 2, 3, 4, ...) in the surface shape distribution 101 as shown in the lower diagram of Fig. 13, it is calculated using the formula for RaY_TopoHill in Fig. 13. Indicator 22: RaX_TopoValley [unit: μm] This represents the "roughness" in the X direction of the Valley portion of the surface shape distribution 101. It can be obtained in the same way by replacing the Hill portion in FIG. 13 with the Valley portion. Indicator 23:RaY_TopoValley [unit: μm] This represents the "roughness" in the Y direction of the Valley portion of the surface shape distribution 101. It can be obtained in the same way by replacing the Hill portion in FIG. 13 with the Valley portion. · Index 24: RaX_RefHill [unit: μm] This represents the "roughness" in the X direction of the hill portion of the two-dimensional luminance distribution 102. By replacing the hill portion in FIG. 13 with the hill portion of the two-dimensional luminance distribution 102, the same calculation as in FIG. 13 can be performed. Index 25:RaY_RefHill [unit: μm] This represents the "roughness" in the Y direction of the hill portion of the two-dimensional luminance distribution 102. By replacing the hill portion in FIG. 13 with the hill portion of the two-dimensional luminance distribution 102, the same calculation as in FIG. 13 can be performed. Indicator 26: RaX_TopoValley [unit: μm] This represents the "roughness" in the X direction of the valley portion of the two-dimensional luminance distribution 102. By replacing the hill portion in FIG. 13 with the valley portion of the two-dimensional luminance distribution 102, the same calculation as in FIG. 13 can be performed. · Index 27:RaY_TopoValley [unit: μm] This represents the "roughness" in the Y direction of the valley portion of the two-dimensional luminance distribution 102. By replacing the hill portion in FIG. 13 with the valley portion of the two-dimensional luminance distribution 102, the same calculation as in FIG. 13 can be performed. Index 28: SpChill [unit: mm -1 ] As shown in FIG. 14, this represents the average value of the curvature of the hill portion in the surface shape distribution 101. The larger the value, the sharper the shape. At the coordinates of the apex of each hill, the curvature of the surface shape distribution from the neighboring pixels is calculated using the formula S in FIG. pc It is calculated as follows. Index 29: Std [unit: °] This is an index that represents the directionality and its angle in the surface shape distribution 101, and is an index that represents the directionality of the streak plane of the unevenness in the surface shape distribution 101. As shown in Fig. 15, the strength of directionality at a certain angle can be obtained by integrating the spatial frequency distribution of the surface shape distribution 101 in the radial direction from the origin. The Std parameter indicates the angle in descending order of the peaks in the direction plot. Index 30: Sdq [unit: μm / mm] This is the average value of the local gradient of the curvature of the hill portion of the surface shape distribution 101. The larger this value, the finer the mesh. It can be calculated by performing a differential operation for each hill of the surface shape distribution 101 and averaging. (3) Method for obtaining surface shape distribution and two-dimensional brightness distribution First, we will explain the method for acquiring the surface shape distribution. This method is a well-known method generally known as "Phase Measuring Deflectmetry (PMD)." Multiple sinusoidal wave patterns with different phases are displayed in a time-division manner on the illumination display device 1, and the reflected light is received by the two-dimensional photoelectric conversion unit 3, and the light reception results are recorded. Since the reflected light contains information about the tilt (curvature) of the sample 100, it can be converted into height information by performing spatial integration calculations on the sample surface using the distributions of the multiple received reflected lights.

[0044] Next, we will explain how to obtain the two-dimensional luminance distribution. The illumination display device 1 displays a pattern in which all pixels have a constant value (an all-white image), and the reflected light is received by the two-dimensional photoelectric conversion unit 3, and the received light results are recorded.

[0045] In this way, it is possible to obtain the surface shape distribution and two-dimensional luminance distribution of the sample 100. However, by adjusting the light intensity of the illumination display device 1 before recording so that both distributions do not exceed the dynamic range of brightness that can be recorded by the two-dimensional photoelectric conversion unit 3, it is expected that highly accurate values ​​can be calculated when calculating the index.

[0046] As described above, the index calculated by this embodiment has the following features: the two-dimensional photoelectric conversion unit 3 receives the specularly reflected light of the illumination light irradiated by the lighting display device 1, and the surface shape distribution 101 and the two-dimensional luminance distribution 102 are obtained from the electrical signal obtained by the two-dimensional photoelectric conversion unit 3; and by calculating the index using at least one of these surface shape distribution 101 and two-dimensional luminance distribution 102, the following effects can be expected. (1) Even if the sample 100 has a large difference in intensity between specularly reflected light and diffusely reflected light, both the surface shape distribution 101 and the two-dimensional luminance distribution 102 are acquired based on the reception of specularly reflected light, so there is no need for processing to ensure dynamic range on the two-dimensional photoelectric conversion unit 3 side. As a result, a high SN can be maintained, and it becomes possible to calculate the index with high accuracy. (2) Because the surface shape distribution 101 can be acquired using the illumination display device 1, which is a single light source, there is no variation in the illuminance of the object being measured, and the surface shape distribution can be acquired stably. This is expected to ensure the reproducibility of the calculated index. If the accuracy of the surface shape distribution is poor, the height information cannot be accurately reflected in the index. (3) The lighting display device 1 has a single light source, and compared to Patent Document 2, the lighting system including the light source is common, so exposure adjustment is required at least once, and index calculation is efficient. In this way, in this embodiment, it is possible to calculate the above index more accurately and simply than in the prior art. [Example 2]: HV segmentation method Another possible method of HV segmentation is to use ternary or quaternary values ​​by setting two or more thresholds. In the case of binarization, as shown in Figure 16, there is a high possibility that areas that are not actually hills, such as the slope between hill and valley, will be included in the calculation when calculating the index. For this reason, by assigning three or more values ​​to the surface shape distribution 101, such as "hill" and "valley" as well as "else" which represents other areas, it is thought that the results for indices related to two-dimensional gloss will be closer to what the human eye sees.

[0047] Furthermore, by calculating and analyzing the "else" region, it is possible to obtain information on the boundary region between the actual convex and concave portions of the textured portion in the surface shape distribution 101. It is possible to know the draft angle when removing the texture from the mold (see Figure 17(a)) and the state of change from the concave portion to the convex portion (see Figures 17(b) and (c)). [Example 3]: Noise processing method 1 in HV segmentation Opening is an effective noise treatment method for HV segmentation. This process divides regions by performing the following sequence: erosion → erosion → dilation → dilation... Dilation refers to the process of expanding a binarized region by one or two times, as shown in the upper diagram of Figure 18. By performing dilation, adjacent areas can be connected. Erosion refers to the process of shrinking a binarized region by one or two times, as shown in the lower diagram of Figure 18. This can erase binarized regions of approximately one pixel or separate areas that were unintentionally extracted as being stuck together. This allows the removal of small or thin patterns, making it effective when the surface shape distribution 101 before the creation of the HV mask 106 contains high-frequency noise (chips, dust, dirt, scratches, etc.). [Example 4]: Noise processing method 2 in HV segmentation When high frequency noise is superimposed on the surface shape distribution 101 itself, the noise processing method of the third embodiment described above may be used, but blur-filtering using a Gaussian kernel or the like is also effective. [Example 5]: Cell-Segmentation Implementation Method 1 Cell-Segmentation can also be achieved by searching for local minimum values ​​(local minima) of the surface shape distribution and connecting them. [Example 6]: Cell-Segmentation Implementation Method 2 This can be achieved by using Voronoi tessellation. As shown in Figure 19, Voronoi tessellation is a diagram in which multiple points placed at any position in a space are divided into regions based on which points are closest to each other. The division pattern depends only on the positions of the points before division (it is possible to weight the points).

[0048] Cell-labels 105 are obtained by marking the surface shape distribution 101 at positions where the unevenness is locally maximum. [Example 7]: Post-processing of cell segmentation (addressing the issue of missing cells 105a at the image edge) The cell-labels 105 obtained by Cell-Segmentation are cut off at the edges of the image as shown in the left diagram of Figure 20, so the accuracy of the calculated index value differs from the actual value. Therefore, this problem can be addressed by adding post-processing to delete the surrounding cells 105a as shown in the right diagram of Figure 20. The resulting image can then be treated as new cell-labels 105 and the index value can be calculated. [Example 8]: Noise processing before cell segmentation 1 To improve segmentation accuracy, the surface shape distribution 101 (left side of Figure 21) can be passed through a blur filter using a Gaussian kernel or similar to remove high-frequency noise, as shown in the right side of Figure 21. For example, if watershed segmentation is used for cell segmentation, over-segmentation can occur due to high-frequency noise. Because segmentation accuracy is directly linked to the accuracy of the index, this process can be performed as needed. [Example 9]: Noise processing before cell segmentation 2 When watershed-segmentation is used as cell-segmentation, "wolf pruning" can be used to prevent over-segmentation. This method removes areas below a certain threshold for the height of peaks and the depth of valleys in the surface shape distribution. For example, the threshold can be set as a percentage of the maximum height (Sz) of the surface shape distribution. The left figure in Figure 22 shows the surface shape distribution without "wolf pruning," the middle figure shows the surface shape distribution with "wolf pruning 5% Sz" performed, and the right figure shows the surface shape distribution with "wolf pruning 10% Sz" performed. [Example 10]: Calculation of index when exposure conditions are not constant The values ​​of the indices 7 and 8 vary depending on the exposure conditions when the two-dimensional luminance distribution 102 is acquired. If constant exposure conditions cannot be guaranteed, stable index calculation is possible by calculating the indices based on the above definitions and then dividing the index value by the exposure time.

[0049] Depending on the sample 100, the amount of specularly reflected light may be relatively low compared to the diffusely reflected light. In such cases, it is necessary to increase the exposure amount (exposure time) to improve the SN ratio, but since the reflection characteristics change when the sample 100 is changed, this index cannot be compared under the same conditions. This effect can be canceled out by dividing by the exposure time. Furthermore, if the exposure amount is low and the SN ratio is low for some reason, this effect can be similarly suppressed.

[0050] Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment. For example, although the surface of the measurement object 100 is embossed, other processing may be used, or the surface may not be processed at all.

[0051] This application claims priority from Japanese Patent Application No. 2021-106980, filed on June 28, 2021, the disclosure of which is incorporated herein by reference in its entirety. [Industrial Applicability]

[0052] The present invention can be used, for example, to determine an evaluation index for the surface of a textured automobile interior part. [Explanation of symbols]

[0053] 1 Lighting display device 2 objective lenses 3 Two-dimensional photoelectric conversion section 4 Arithmetic section 5 Calculation result display section 100 Measurement object (sample) 100a Measured area 101 Surface shape distribution 102 Two-dimensional luminance distribution 103 line 104 Images with lines 105 Cell-divided images 105a Cell 106 Image with Hill and Valley information in surface shape distribution

Claims

1. a single illumination means capable of irradiating an illumination light onto the measurement object; a two-dimensional photoelectric conversion means disposed at a position capable of receiving specularly reflected light from the measurement object of illumination light irradiated onto the measurement object from the illumination means; a shape distribution acquisition means for acquiring a surface shape distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a luminance distribution acquisition means for acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a calculation means for calculating a surface evaluation index of the measurement object using at least one of the surface shape distribution of the measurement object acquired by the shape distribution acquisition means and the two-dimensional luminance distribution of the measurement object acquired by the luminance distribution acquisition means; Equipped with The calculation means is a measuring instrument that performs a first region division to divide the surface shape distribution into a plurality of closed regions according to their heights using a watershed segmentation method before calculating the surface evaluation index.

2. a shape distribution acquisition means for acquiring a surface shape distribution of the measurement object based on an electrical signal obtained by a two-dimensional photoelectric conversion means disposed at a position capable of receiving specularly reflected light from the measurement object of illumination light irradiated onto the measurement object from a single illumination means; a luminance distribution acquisition means for acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a calculation means for calculating a surface evaluation index of the measurement object using at least one of the surface shape distribution of the measurement object acquired by the shape distribution acquisition means and the two-dimensional luminance distribution of the measurement object acquired by the luminance distribution acquisition means; Equipped with The calculation means is a measuring instrument that performs a first region division to divide the surface shape distribution into a plurality of closed regions according to their heights using a watershed segmentation method before calculating the surface evaluation index.

3. 3. The measuring instrument according to claim 1, wherein the shape distribution acquisition means acquires the surface shape distribution of the object by calculation based on the electrical signal obtained by the two-dimensional photoelectric conversion means when the object is irradiated with illumination light of a plurality of sinusoidal wave patterns with different phases in a time-division manner by the illumination means and the specular reflected light is received by the two-dimensional photoelectric conversion means.

4. 3. The measuring instrument according to claim 1, wherein the luminance distribution acquisition means acquires the luminance distribution of the object to be measured by calculation based on the electrical signal obtained by the two-dimensional photoelectric conversion means when the object to be measured is irradiated with illumination light of a constant value pattern by the illumination means and the specular reflected light is received by the two-dimensional photoelectric conversion means.

5. 3. The measuring instrument according to claim 1, wherein the spatial resolution of said two-dimensional photoelectric conversion means is 100 [mu]m or less.

6. The measurement object has a textured surface, 3. The measuring instrument according to claim 1, wherein the surface evaluation index is an index relating to a feature quantity of a textured surface.

7. 3. The measuring instrument according to claim 1, wherein the calculation means performs low-frequency filtering on the surface shape distribution to remove high-frequency components before executing the first region division.

8. 3. The measuring instrument according to claim 1, wherein the calculation means deletes coordinate information representing closed areas adjacent to an edge of the image from an image representing a plurality of closed areas obtained as a result of the first area division.

9. 3. The measuring instrument according to claim 1, wherein the calculation means performs a second region division to obtain coordinate information of the uneven portions of the measurement object by performing Otsu's binarization on the surface shape distribution before calculating the surface evaluation index.

10. the calculation means performs Otsu's binarization of the surface shape distribution before calculating the surface evaluation index, thereby performing a second region division to acquire coordinate information of uneven portions of the measurement object; 3. The measuring instrument according to claim 1, wherein the surface evaluation index is calculated using one or more of the image obtained as a result of the first region division, the image obtained as a result of the second region division, the surface shape distribution, and the two-dimensional luminance distribution.

11. 3. The measuring instrument according to claim 1, wherein the surface evaluation index is expressed by the number of closed regions within the image obtained by the first region division.

12. 3. The measuring instrument according to claim 1, wherein the surface evaluation index is expressed as an average area of ​​closed regions within the image obtained by the first region division.

13. 3. The measuring instrument according to claim 1, wherein the surface evaluation index is expressed as an average value of heights in a set of coordinates within the surface shape distribution, the average value being calculated from coordinate information representing a closed area within the image obtained by the first area division.

14. the calculation means performs Otsu's binarization of the surface shape distribution before calculating the surface evaluation index, thereby performing a second region division to acquire coordinate information of uneven portions of the measurement object; 3. The measuring instrument according to claim 1, wherein the surface evaluation index is expressed as an average area of ​​a set of coordinates within a closed region, the coordinates being calculated from a plurality of closed regions within the image obtained by the first region division and coordinate information representing convex or concave portions obtained by the second region division.

15. 10. The measuring instrument according to claim 9, wherein the surface evaluation index is expressed as an average value of brightness in a set of coordinates within the two-dimensional luminance distribution, the set of coordinates being obtained from the set of coordinates representing the convex or concave portions obtained by the second region division.

16. 10. The measuring instrument according to claim 9, wherein the surface evaluation index is obtained by calculating a contrast between index values ​​calculated for each of the convex portions and the concave portions obtained by the second region division.

17. The measuring instrument according to claim 9, wherein the surface evaluation index includes an index obtained by calculating an average value of absolute values ​​of deviations from a reference surface in a convex portion or a coordinate set representing the convex portion obtained by the second region division within the surface shape distribution or the two-dimensional luminance distribution.

18. 10. The measuring instrument according to claim 9, wherein the surface evaluation index includes an index obtained by calculating the square root of the average value of the squares of deviations from a reference surface in a coordinate set representing a convex portion or a concave portion obtained by the second area division within the surface shape distribution or the two-dimensional luminance distribution.

19. 10. The measuring instrument according to claim 9, wherein the surface evaluation index is expressed as an average value of wavelength components included in a set of coordinates in the surface shape distribution, the set of coordinates being obtained from the set of coordinates representing the convex or concave portions obtained by the second area division.

20. 10. The measuring instrument according to claim 9, wherein the surface evaluation index is obtained by calculating the square root of the average value of the squares of deviations of the surface shape distribution from a reference plane in the horizontal or vertical direction within the surface shape distribution or the two-dimensional luminance distribution, from a coordinate set representing the convex or concave portions obtained by the second region division.

21. 10. The measuring instrument according to claim 9, wherein the surface evaluation index is expressed as an average value of the radii of curvature of the convex portions obtained from a set of coordinates representing the convex portions obtained by the second area division.

22. The measuring instrument according to claim 1 or 2, wherein the surface evaluation index represents a directionality of streaks of irregularities in the surface shape distribution.

23. 10. The measuring instrument according to claim 9, wherein the surface evaluation index is expressed as an average value of local gradients in a set of coordinates within the surface shape distribution, the average value being calculated from the set of coordinates representing the convex portions obtained by the second region division.

24. a shape distribution acquisition means for acquiring a surface shape distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a luminance distribution acquisition means for acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; the calculation means executes a step of calculating a surface evaluation index of the measurement object using at least one of the surface shape distribution of the measurement object acquired in the shape distribution measurement step and the two-dimensional luminance distribution of the measurement object acquired in the luminance distribution measurement step, A method for calculating a surface evaluation index, in which the calculation means performs a first region division to divide the surface shape distribution into multiple closed regions using a watershed segmentation method according to their heights before calculating the surface evaluation index.

25. a step of acquiring a surface shape distribution of the measurement object based on an electrical signal obtained by a two-dimensional photoelectric conversion means disposed at a position capable of receiving specularly reflected light from the measurement object of illumination light irradiated onto the measurement object from a single illumination means; acquiring a two-dimensional luminance distribution of the measurement object based on the electrical signal obtained by the two-dimensional photoelectric conversion means; a calculation step of calculating a surface evaluation index of the measurement object using at least one of the acquired surface shape distribution and two-dimensional luminance distribution of the measurement object; performing a first region segmentation step of dividing the surface shape distribution into a plurality of closed regions using a watershed segmentation method according to heights of the surface shape distribution before calculating the surface evaluation index; A program that causes a computer to execute the following.

Images