Information processing device, information processing method, and computer program

Abstract

To automatically generate a dish shape drawing efficiently while suppressing variations without requiring mastery skills, regarding dishes.SOLUTION: An information processing apparatus for generating a dish shape drawing that represents a shape of a dish using at least a contour line of a cross section of a dish and a ridge line on a surface of the dish includes a cross section acquisition unit, a differential value acquisition unit, a specific point extraction unit, and a drawing generation unit. The cross section acquisition unit acquires cross section data representing a contour line of a cross section of a dish. The differential value acquisition unit acquires a first-level differential value and a second-level differential value in each point on the contour line. The specific point extraction unit extracts a specific point which is a concave point or a convex point on the contour line on the basis of the first-level differential value and the second-level differential value. The drawing generation unit sets, as a ridge line, a horizontal line passing through the specific point, to generate a dish shape drawing based on the ridge line and the contour line.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The technology disclosed in this specification relates to an information processing device or the like for generating a drawing representing the shape of an object. [Background technology]

[0002] Measurement drawings are made for pottery and other vessels excavated from archaeological sites. Measurement drawings summarize the shape and manufacturing method of the vessel, and are essential for excavation reports. Measurement drawings express the shape of the vessel using at least the outline of the vessel's cross section and the ridges on its surface.

[0003] Creating a measurement map is a manual process that involves placing a ruler or other tool on the object and tracing the shape of the object onto graph paper. Furthermore, when creating a measurement map, it is necessary to draw ridgelines at appropriate locations on the object. Therefore, creating a measurement map requires skilled techniques, is time-consuming and labor-intensive, and is prone to variations in the quality of the work depending on the creator.

[0004] Conventionally, a system has been proposed for creating a survey map of remains using measured values obtained by an electro-optical surveying instrument (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-65763 Summary of the Invention [Problem to be solved by the invention]

[0006] The above-mentioned system for creating measured maps is only intended for archaeological remains, and cannot be applied to creating measured maps of objects. There is no known technology for automatically creating measured maps of objects. As such, there is a demand for technology that can automatically create measured maps of objects efficiently and with reduced variation, without requiring skilled techniques.

[0007] Furthermore, this issue is not limited to the creation of measured drawings of archaeological artifacts excavated from ruins, but is also a common issue when creating drawings that represent the shapes of other types of artifacts, such as works of art.

[0008] This specification discloses a technique that can solve the above-mentioned problems. [Means for solving the problem]

[0009] The technology disclosed in this specification can be realized, for example, in the following forms.

[0010] (1) The information processing device disclosed in this specification is an information processing device for generating a shape drawing of an object that represents the shape of the object using at least the outline of a cross section of the object and the ridge lines on the surface of the object, and includes a cross section acquisition unit, a differential value acquisition unit, a specific point extraction unit, and a drawing generation unit. The cross section acquisition unit acquires cross section data that indicates the outline of the cross section of the object. The differential value acquisition unit acquires first-order differential values and second-order differential values at each point on the outline. The specific point extraction unit extracts specific points that are concave or convex points on the outline based on the first-order differential values and the second-order differential values. The drawing generation unit sets a horizontal line that passes through the specific points as the ridge line, and generates the object shape drawing based on the ridge line and the outline.

[0011] In this way, the information processing device can extract specific points that are concave or convex on the contour line based on the first-order and second-order differential values at each point on the contour line of the cross section of the object, and set horizontal lines that pass through the specific points as ridge lines, thereby drawing ridge lines in appropriate positions. Therefore, the information processing device can automatically create object shape drawings with ridge lines drawn in appropriate positions efficiently and with reduced variation, without requiring skilled techniques, for objects.

[0012] (2) In the information processing device, the differential value acquisition unit may acquire the first-order differential value and the second-order differential value for two intersecting directions, and the specific point extraction unit may extract the specific point based on the first-order differential value and the second-order differential value for each of the two directions. This information processing device can automatically create an object shape drawing in which ridgelines are drawn at more appropriate positions.

[0013] (3) In the information processing device, the specific point extraction unit may be configured to extract, as the specific point, a point whose absolute value of the first-order differential value is greater than a predetermined threshold and whose absolute value of the second-order differential value is less than a predetermined threshold in at least one direction. This information processing device can automatically create an object shape drawing in which ridgelines are drawn at more appropriate positions.

[0014] (4) In the information processing device, the two directions may be the horizontal direction and the vertical direction, and the specific point extraction unit may extract, as the specific point, a point for which a concavity-convexity index value, obtained by subtracting the absolute value of the second-order differential value in the vertical direction, the absolute value of the first-order differential value in the horizontal direction, and the absolute value of the second-order differential value in the horizontal direction from the absolute value of the first-order differential value in the vertical direction, is greater than a predetermined threshold. Points for which the thus-defined concavity-convexity index value CV is relatively large are likely to be concave or convex points on a contour line. Therefore, this information processing device can automatically generate an object shape drawing in which ridgelines are drawn at more appropriate positions.

[0015] (5) The information processing device may further include a specific point selection unit that selects, from the extracted specific points, the specific point to be used for setting the ridge line. By selecting a more appropriate specific point as the starting point of the ridge line, this information processing device can automatically create an object shape drawing in which the ridge line is drawn in a more appropriate position.

[0016] (6) In the information processing device, the cross-section acquisition unit may acquire a plurality of cross-section data showing the contour lines of different cross-sections of the object, the information processing device may further include a representative cross-section selection unit that selects one of the plurality of cross-section data as representative cross-section data, and the drawing generation unit may generate the object shape drawing using the representative cross-section that is the cross-section represented by the representative cross-section data. This information processing device can automatically create an object shape drawing showing a representative cross-section that appropriately shows the characteristics of the object.

[0017] (7) In the information processing device, the representative cross-section selection unit may be configured to select one of the cross-section data belonging to the second group as the representative cross-section data when the plurality of cross-section data are grouped into three groups, namely, a first group, a second group, and a third group, in descending order of the number of the specific points. According to this information processing device, it is possible to automatically create an object shape drawing showing a representative cross-section that more appropriately represents the characteristics of the object.

[0018] (8) In the information processing device, the second group may be a group consisting of the cross-sectional data in which the number of the specific points matches the median value among the plurality of cross-sectional data. According to this information processing device, it is possible to automatically create an object shape drawing showing a representative cross-section that more appropriately expresses the characteristics of the object.

[0019] (9) In the information processing device, the representative cross-section selection unit may perform the grouping for each of the specific points on the outer contour line of the object and the specific points on the inner contour line of the object, and select one of the cross-section data belonging to the second group for both the outer contour line and the inner contour line as the representative cross-section data. This information processing device can automatically create an object shape drawing showing a representative cross-section that more appropriately represents the characteristics of the object.

[0020] (10) In the information processing device, the representative cross-section selection unit may perform clustering to classify the plurality of cross-section data into a plurality of clusters using a shape index value representing the shape of the cross-section, and perform the grouping on the cross-section data belonging to the largest cluster. This information processing device makes it possible to more efficiently and automatically create an object shape drawing showing a representative cross-section that more appropriately represents the characteristics of the object.

[0021] (11) In the information processing device, the representative cross-section selection unit may be configured to select, from the cross-section data belonging to the second group, one piece of cross-section data having a shape index value representing the shape of the cross-section closest to the center point of the distribution as the representative cross-section data. According to this information processing device, it is possible to automatically create an object shape drawing showing a representative cross-section that more appropriately represents the characteristics of the object.

[0022] (12) In the information processing device, the object may be an archaeological object, and the object shape drawing may be a measured drawing of the archaeological object. This information processing device can automatically create a measured drawing of an archaeological object, with ridgelines drawn in appropriate positions, efficiently and with reduced variation, without requiring skilled techniques.

[0023] The technology disclosed in this specification can be realized in various forms, such as an information processing device, an information processing method, a computer program that realizes those methods, a non-transitory recording medium on which that computer program is recorded, etc. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a block diagram showing a schematic configuration of an information processing device 100 according to an embodiment of the present invention. [Figure 2] An explanatory diagram showing an example of a surveyed drawing SD for VE of archaeological artifacts [Figure 3] Flowchart showing the measurement drawing generation process [Figure 4] FIG. 1 is an explanatory diagram showing an overview of a measured drawing generation process according to an embodiment of the present invention; [Figure 5] FIG. 1 is an explanatory diagram showing an overview of a measured drawing generation process according to an embodiment of the present invention; [Figure 6] FIG. 1 is an explanatory diagram showing an overview of a measured drawing generation process according to an embodiment of the present invention; [Figure 7] Flowchart showing specific point extraction processing [Figure 8] An explanatory diagram showing an overview of how to obtain the first derivative value DV1 [Figure 9] An explanatory diagram showing an overview of how to obtain the second derivative value DV2 DETAILED DESCRIPTION OF THE INVENTION

[0025] A. Implementation: A-1. Configuration of information processing device 100: FIG. 1 is a block diagram showing a schematic configuration of an information processing device 100 according to this embodiment. The information processing device 100 according to this embodiment is a device for generating an object shape drawing. In this specification, an object shape drawing is a drawing that represents the shape of an object using at least the outline of the cross section of the object and the ridge lines on the surface of the object. In this embodiment, as shown in FIG. 2, an example will be described in which the information processing device 100 is applied to generate a measured drawing SD, which is a drawing that represents the shape of an object VE (e.g., earthenware) as an archaeological specimen excavated from a ruin, using the outline CO of the cross section CS of the object VE and the ridge lines RL on the surface of the object VE. The measured drawing SD is an example of an object shape drawing.

[0026] As shown in Figure 2, in the measured drawing SD, for example, the portion to the right of the center line CE (hereinafter referred to as the "inner portion") is composed of the overall outline CO of the cross section CS and the ridge line RL of the inner surface of the vessel VE, while the portion to the left of the center line CE (hereinafter referred to as the "outer portion") is composed of the outer edge line of the vessel VE (the outer outline CO of the cross section CS) and the ridge line RL of the outer surface of the vessel VE. Furthermore, the vessel VE as an archaeological specimen includes containers (such as bowls, high-rises, bowls, and plates) shown in the upper part of Figure 2, and lids shown in the lower part of Figure 2.

[0027] The information processing device 100 (FIG. 1) is configured by a computer such as a personal computer, a server, a tablet terminal, or a smartphone. The information processing device 100 includes a control unit 110, a storage unit 130, a display unit 152, an operation input unit 156, and an interface unit 158. These units are connected to each other via a bus 190 so as to be able to communicate with each other.

[0028] The display unit 152 of the information processing device 100 is configured, for example, by a liquid crystal display or an organic EL display, and displays various images and information. The operation input unit 156 is configured, for example, by a keyboard, a mouse, buttons, a microphone, and a trackpad, and receives operations and instructions from the administrator. The display unit 152 may also function as the operation input unit 156 by being equipped with a touch panel. The interface unit 158 is configured, for example, by a LAN interface, a USB interface, or the like, and communicates with other devices via wired or wireless connections. The information processing device 100 may also be equipped with a speaker.

[0029] The storage unit 130 of the information processing device 100 is configured with, for example, a ROM, RAM, and hard disk drive (HDD), and is used to store various programs and data, and as a work area when various programs are executed, and as a temporary storage area for data. For example, the storage unit 130 stores a drawing generation program CP, which is a computer program for executing the measured drawing generation process described below. The drawing generation program CP is provided in a state stored in a computer-readable recording medium (not shown), such as a CD-ROM, DVD-ROM, or USB memory, or is provided in a state where it can be obtained from an external device (a server or other terminal device on a network) via the interface unit 158, and is stored in the storage unit 130 in a state where it can be operated on the information processing device 100.

[0030] Furthermore, the 3D scan data SCD, cross-sectional data CSD, shape index value SV, and measured drawing data SDD are stored in advance or during the measured drawing generation process described later in the storage unit 130 of the information processing device 100. The contents of these will be described in conjunction with the description of the measured drawing generation process described later.

[0031] The control unit 110 of the information processing device 100 is configured with, for example, a CPU or the like, and controls the operation of the information processing device 100 by executing a computer program read from the storage unit 130. For example, the control unit 110 functions as a drawing generation unit 120 for executing a measured drawing generation process described below by reading and executing a drawing generation program CP from the storage unit 130. The drawing generation unit 120 includes a cross-section acquisition unit 121, a shape index value calculation unit 123, a differential value acquisition unit 124, a specific point extraction unit 125, a specific point selection unit 126, and a representative cross-section selection unit 127. The functions of each of these units will be described in conjunction with the description of the measured drawing generation process described below.

[0032] A-2. Measurement drawing generation process: Next, a description will be given of the measured map generation process executed by the information processing device 100 of this embodiment. Fig. 3 is a flowchart showing the measured map generation process. Figs. 4 to 6 are explanatory diagrams showing an overview of the measured map generation process in this embodiment.

[0033] As described above with reference to Figure 2, the measured drawing generation process is a process for generating a measured drawing SD representing the shape of an object VE, which is an archaeological specimen excavated from a ruin, using the outline CO of the cross section CS of the object VE and the ridge line RL on the surface of the object VE. Column A of Figure 4 shows an example of an object VE that is the target of the measured drawing generation process.

[0034] When generating a measured drawing SD, it is necessary to set a cross section CS (a representative cross section CSr, described later) that properly reflects the characteristics of the object VE and to draw ridge lines RL at appropriate positions. Therefore, in the measured drawing generation process, various processes are executed, including a process for setting the representative cross section CSr and a process for drawing ridge lines RL on the representative cross section CSr, and as a result, the measured drawing SD is automatically generated. The measured drawing generation process is started, for example, in response to an instruction to start the process being input by the administrator via the operation input unit 156 of the information processing device 100.

[0035] First, the drawing generation unit 120 (FIG. 1) of the information processing device 100 acquires 3D scan data SCD of the object VE (S110). The 3D scan data SCD is 3D data (mesh data) generated by 3D scanning the object VE using a 3D scanner. The 3D scan data SCD of the object VE is acquired from an external device via the interface unit 158, for example. The acquired 3D scan data SCD is stored in the storage unit 130. Note that if the information processing device 100 has a 3D scanning function, the information processing device 100 may acquire the 3D scan data SCD of the object VE using the function.

[0036] Next, a process (S120-S196) for setting a representative cross section CSr that appropriately represents the characteristics of the object VE is executed. First, the cross section acquisition unit 121 (FIG. 1) of the information processing device 100 acquires multiple cross section data CSD representing the shapes of different cross sections CS of the object VE based on the 3D scan data SCD (S120). More specifically, as shown in column A of FIG. 4, the cross section acquisition unit 121 identifies a virtual central axis (vertical axis) Ov of the object VE, sets multiple virtual vertical planes VP extending radially from the virtual central axis Ov, and acquires multiple cross section data CSD representing the shapes of the cross sections CS of the object VE on each virtual vertical plane VP. The acquired cross section data CSD are stored in the storage unit 130. Note that the virtual central axis Ov can be set by any method. For example, a vertical line passing through the center of gravity of a figure obtained by projecting the object VE in the vertical direction can be set as the virtual central axis Ov. The virtual central axis Ov corresponds to the center line CE (FIG. 2) in the measured drawing SD. Furthermore, the plurality of virtual vertical planes VP are set, for example, at a constant pitch (rotation angle). In this embodiment, the number of virtual vertical planes VP to be set, i.e., the number of pieces of cross-sectional data CSD to be acquired, is 1000. Column B of Fig. 4 shows an example of the plurality of pieces of cross-sectional data CSD that have been acquired.

[0037] Next, the cross-section acquisition unit 121 extracts the contour line CO of the cross-section CS represented by each cross-section data CSD (S130). More specifically, the cross-section acquisition unit 121 binarizes each cross-section data CSD and performs well-known image processing for contour extraction on the binarized cross-section data CSD, thereby extracting the contour line CO of the cross-section CS. Column B of FIG. 4 shows an example of the contour line CO extracted for the cross-section CS represented by each cross-section data CSD. Hereinafter, data indicating the contour line CO of the cross-section CS will also be referred to as cross-section data CSD.

[0038] Next, the shape index value calculation unit 123 of the information processing device 100 calculates a shape index value SV based on the contour line CO of each cross section CS (S140). The shape index value SV is an index value that represents the shape of the cross section CS.

[0039] More specifically, the shape index value calculation unit 123 performs an elliptic Fourier transform on the contour CO of each cross section CS. The elliptic Fourier transform is a process for quantitatively describing the contour, in which the contour is regarded as a periodic function, a Fourier transform of the function is performed, and the contour is described using the coefficients. Specifically, the shape index value calculation unit 123 performs an elliptic Fourier transform on the contour CO of the cross section CS using the following equations (1) and (2), thereby obtaining four descriptors a n , b n , c n , d n The order n of the elliptic Fourier transform is set appropriately, but in this embodiment, the order n is set to 128. Therefore, 4×128 dimensional variables are found for each cross section CS.

number

number

[0040] Next, the shape index value calculation unit 123 performs dimension reduction by principal component analysis on the 4 × 128-dimensional variables for each cross section CS to calculate a shape index value SV, which is data of a predetermined number of dimensions (20 dimensions in this embodiment). The shape index value SV calculated in this way becomes an index value representing the shape of the cross section CS. The calculated shape index value SV is stored in the storage unit 130.

[0041] Next, the representative cross-section selection unit 127 (FIG. 1) of the information processing device 100 performs clustering of the multiple cross-sectional data CSD using the shape index value SV to identify the largest cluster CLx (S150). Clustering of the multiple cross-sectional data CSD can be performed using any known clustering method, but in this embodiment, DBSCAN, a density-based clustering algorithm, is used. Based on the clustering results, the representative cross-section selection unit 127 identifies the cluster CL to which the largest number of cross-sectional data CSD belongs as the largest cluster CLx. The cross-sections CS represented by the cross-sectional data CSD belonging to the largest cluster CLx can be said to be typical cross-sections CS in the object VE, and are candidates for the representative cross-section CSr. Column C in FIG. 4 shows an example of the largest cluster CLx identified by clustering of the cross-sectional data CSD.

[0042] Next, the specific point extraction unit 125 (FIG. 1) of the information processing device 100 executes a specific point extraction process for extracting a specific point SP on the contour line CO of the cross section CS for each piece of cross section data CSD belonging to the largest cluster CLx (S160). Here, the specific point SP is a concave or convex point on the contour line CO of the cross section CS, and is a candidate for the starting point of the edge line RL.

[0043] FIG. 7 is a flowchart showing the specific point extraction process. First, the specific point extraction unit 125 identifies an outer contour line COo and an inner contour line COi for a cross section CS represented by each cross-section data CSD (S310). More specifically, the specific point extraction unit 125 identifies, among the contour lines CO of the cross section CS, a line that starts from a point on the virtual central axis Ov (section A in FIG. 4) on the outer side of the container and extends along the outer periphery to the outer edge of the vessel VE as the outer contour line COo, and a line that starts from a point on the virtual central axis Ov on the inner side of the container and extends along the inner periphery to the outer edge of the vessel VE as the inner contour line COi. Section D in FIG. 5 shows a portion of the outer contour line COo and inner contour line COi identified for a given cross section CS. The subsequent processing in the specific point extraction process is performed separately for each of the outer contour line COo and inner contour line COi.

[0044] Next, the differential value acquisition unit 124 (FIG. 1) of the information processing device 100 acquires a first-order differential value DV1 and a second-order differential value DV2 at each point P on the contour CO (outer contour COo and inner contour COi) of the cross section CS (S320). FIG. 8 is an explanatory diagram showing an outline of a method for acquiring the first-order differential value DV1, and FIG. 9 is an explanatory diagram showing an outline of a method for acquiring the second-order differential value DV2. For each point P (each pixel) on the contour CO, the differential value acquisition unit 124 acquires the amount of deviation in the y direction (vertical direction) when the point P is shifted by one pixel in the x direction (horizontal direction) as the first-order differential value DV1, and acquires the amount of deviation in the y direction when the point P is shifted by one pixel in the x direction as the second-order differential value DV2. As shown in Figures 8 and 9, the first-order differential value DV1 corresponds to the slope of the tangent TL to the contour line CO at each point P on the contour line CO, and the second-order differential value DV2 corresponds to the rate of change of the slope at each point P on the contour line CO (the slope of the tangent TL to the curve COd that represents the first-order differential value DV1).

[0045] In this embodiment, a first-order differential value DV1 and a second-order differential value DV2 are obtained for each of the x and y directions. While Fig. 8 and Fig. 9 show how to obtain the first-order differential value DV1 and the second-order differential value DV2 for the x direction, similar first-order differential values DV1 and DV2 for the y direction are obtained for the contour line CO when the relationship between the x and y axes is inverted. Hereinafter, the first-order differential value DV1 and the second-order differential value DV2 for the x direction will be referred to as the x-direction first-order differential value DV1x and the x-direction second-order differential value DV2x, respectively, and the first-order differential value DV1 and the second-order differential value DV2 for the y direction will be referred to as the y-direction first-order differential value DV1y and the y-direction second-order differential value DV2y, respectively.

[0046] Next, the specific point extraction unit 125 calculates a concavity-convexity index value CV at each point P on the contour CO (S330). The concavity-convexity index value CV is an index value that indicates the degree of concavity or convexity at each point P on the contour CO. In this embodiment, the concavity-convexity index value CV is calculated using the following equation (3). That is, the concavity-convexity index value CV is a value obtained by subtracting the absolute value |DV2y| of the y-direction second-order differential value DV2y, the absolute value |DV1x| of the x-direction first-order differential value DV1x, and the absolute value |DV2x| of the x-direction second-order differential value DV2x from the absolute value |DV1y| of the y-direction first-order differential value DV1y. In other words, the concavity-convexity index value CV can be said to be an index value that indicates the magnitude of the absolute value of the y-direction first-order differential value DV1y and the smallness of the absolute value of the y-direction second-order differential value DV2y.

number

[0047] Next, the specific point extraction unit 125 extracts a point P whose concavity / convexity index value CV exceeds a predetermined threshold Th (that is, satisfies the relationship CV > Th) as a specific point candidate SPp (S340). As described above, since the concavity / convexity index value CV is an index value representing the magnitude of the absolute value of the first-order differential value DV1y in the y direction and the smallness of the absolute value of the second-order differential value DV2y in the y direction, the extracted specific point candidate SPp is a point P where the absolute value of the first-order differential value DV1y in the y direction is relatively large and the absolute value of the second-order differential value DV2y in the y direction is relatively small. A point P where the absolute value of the first-order differential value DV1y in the y direction is relatively large and the absolute value of the second-order differential value DV2y in the y direction is relatively small is a point on the contour line CO where the ratio of the change amount in the horizontal direction to the change amount in the vertical direction is relatively large and the change rate of the change amount is relatively small, and it can be said that there is a high probability that it is a concave or convex point on the contour line CO. In this embodiment, the specific point extraction unit 125 normalizes the concavity / convexity index value CV to a range of 0 or more and 1 or less, and compares the normalized concavity / convexity index value CV with a preset threshold Th (0 < Th < 1) to execute the extraction of the specific point candidate SPp.

[0048] Next, the specific point selection unit 126 (FIG. 1) of the information processing apparatus 100 selects a specific point SP from the extracted specific point candidates SPp (S350). The specific point selection unit 126 deletes (filters) the specific point candidates SPp according to at least one of the following rules, for example, and selects the specific point candidate SPp that remains undeleted as the specific point SP. (1) Rule 1: When the distance (for example, the straight-line distance) between the specific point candidates SPp is equal to or less than a predetermined threshold, one of the specific point candidates SPp (for example, the specific point candidate SPp closer to the outer edge of the article VE) is deleted. (2) Rule 2: When the distance (for example, the straight-line distance) from the specific point candidate SPp to the outer edge of the article VE is equal to or less than a predetermined threshold, the specific point candidate SPp is deleted. (3) Rule 3: When a horizontal line is drawn from the specific point candidate SPp toward the virtual central axis Ov (center line CE) of the article VE and the horizontal line intersects the contour line CO before reaching the virtual central axis Ov, the specific point candidate SPp is deleted.

[0049] By performing the above steps, the specific point extraction process (S160 in FIG. 3) is completed. The specific point extraction process extracts specific points SP, which are highly likely to be concave or convex points, from each of the outer contour line COo and the inner contour line COi. Hereinafter, the specific points SP extracted from the outer contour line COo will be referred to as outer specific points SPo, and the specific points SP extracted from the inner contour line COi will be referred to as inner specific points SPi. Column D in FIG. 5 shows examples of specific points SP (outer specific points SPo and inner specific points SPi) extracted from the contour line CO (outer contour line COo and inner contour line COi) of a certain cross-section CS.

[0050] Next, the representative cross-section selecting unit 127 extracts, from the plurality of cross-section data CSD, cross-section data CSD in which the number of outer specific points SPo matches the median of the entire plurality of cross-section data CSD and the number of inner specific points SPi matches the median of the entire plurality of cross-section data CSD (S170 in FIG. 3). That is, the representative cross-section selecting unit 127 extracts cross-section data CSD in which the number of outer specific points SPo and the number of inner specific points SPi are typical numbers for the object VE. This process corresponds to the process of extracting cross-section data CSD belonging to the second group when the plurality of cross-section data CSD is grouped into three groups, a first group, a second group, and a third group, in descending order of the number of specific points SP.

[0051] The representative cross section selection unit 127 branches the process according to the number N of cross section data CSD extracted in S170 (S180). Specifically, when the number N of extracted cross section data CSD is 1 (N=1), the representative cross section selection unit 127 selects the only extracted cross section data CSD as the representative cross section data CSDr (S192). On the other hand, when the number N of extracted cross section data CSD is 2 or more (N≧2), the representative cross section selection unit 127 selects one cross section data CSD whose shape index value SV is closest to the center of distribution from the extracted multiple cross section data CSD as the representative cross section data CSDr (S194). This process is performed, for example, by identifying a medoid (a point in a cluster that has the smallest sum of dissimilarities with other points in the cluster). Furthermore, if the number N of extracted cross-sectional data CSD is 0 (N=0), the representative cross-sectional data selection unit 127 selects one cross-sectional data CSD whose shape index value SV is closest to the center of distribution as the representative cross-sectional data CSDr from all the cross-sectional data CSD belonging to the largest cluster CLx, as in S194 (S196). The representative cross-sectional data CSr represented by the representative cross-sectional data CSDr selected in this manner has a shape of the entire cross-section that is typical for the object VE, and the number of specific points SP is also typical for the object VE, so it can be said to be a cross-section that appropriately represents the characteristics of the object VE. Column E in Figure 5 shows an example of the selected representative cross-sectional data CSDr.

[0052] Next, the drawing generation unit 120 (FIG. 1) of the information processing device 100 generates a measured drawing SD by drawing ridge lines RL and the like using the representative cross-section data CSDr (S200). More specifically, as shown in columns E and F of FIG. 5, the drawing generation unit 120 identifies the nadir BP, which is the lowest point on the outer contour line COo, and converts the portion of the outer contour line COo from the nadir BP to the imaginary center axis Ov (center line CE) into a horizontal line (solid line). At this time, if a specific point SP exists in the converted portion, the specific point SP is deleted. Note that, as shown in column G of FIG. 6, this process is not performed if the target vessel VE is a "lid" rather than the above-mentioned "container."

[0053] Furthermore, as shown in column H of Figure 6, if the target vessel VE has a so-called protruding shape and the nadir BP on the outer contour line COo is located at a location other than the leg LP, a quasi-nadir BP1 is identified, for example, as follows, and the same processing is performed using the quasi-nadir BP1 instead of the nadir BP. (1) Identify the intermediate line CCL between the outer contour line COo and the inner contour line COi. (2) The lowest point on the median line CCL is identified as the intermediate bottom point CBP1. (3) The intersection of the outer contour COo and a straight line L1 that passes through the intermediate nadir point CBP1 and connects the outer contour COo and the inner contour COi is identified as a quasi-nadir point BP1.

[0054] Next, the drawing generation unit 120 draws a ridge line RL passing through the specific point SP. More specifically, as shown in column F of FIG. 5, the drawing generation unit 120 draws the ridge line RL, which is a horizontal chain line, from the specific point SP (external specific point SPo) on the outer contour line COo to the center line CE. This generates an outer portion (the portion to the left of the center line CE) of the measured drawing SD composed of the outer contour line COo and the ridge line RL. The drawing generation unit 120 also draws a ridge line RL, which is a horizontal chain line, from the specific point SP (inner specific point SPi) on the inner contour line COi to the center line CE. This generates an inner portion (the portion to the right of the center line CE) of the measured drawing SD composed of the contour line CO of the cross section CS and the ridge line RL. Note that the specific point SP itself is not actually drawn on the measured drawing SD. Also, as shown in column G of Figure 6, when the vessel VE is a "lid," when the specific point SP coincides with the point on the outermost edge (horizontally outward) of the outer contour line COo, the ridge line RL (RLx) drawn from the specific point SP is a solid line.

[0055] Next, the drawing generating unit 120 completes the measured drawing SD by combining the outer and inner parts of the generated measured drawing SD and then drawing a center line CE, a bottom line BL, a top line HL, and a scale SC. The measured drawing data SDD representing the completed measured drawing SD is stored in the memory unit 130. The drawing generating unit 120 may also display the measured drawing SD on the display unit 152 or print it on a printing device via the interface unit 158.

[0056] A-3. Advantages of this embodiment: As described above, the information processing device 100 of this embodiment is an apparatus for generating an object shape drawing (measured drawing SD) that represents the shape of an object VE using at least the contour line CO of the cross section CS of the object VE and the ridge line RL on the surface of the object VE, and includes a cross section acquisition unit 121, a differential value acquisition unit 124, a specific point extraction unit 125, and a drawing generation unit 120. The cross section acquisition unit 121 acquires cross section data CSD that represents the contour line CO of the cross section CS of the object VE. The differential value acquisition unit 124 acquires a first-order differential value DV1 and a second-order differential value DV2 at each point on the contour line CO. The specific point extraction unit 125 extracts specific points SP, which are concave or convex points on the contour line CO, based on the first-order differential value DV1 and the second-order differential value DV2. The drawing generation unit 120 sets a horizontal line passing through the specific points SP as the ridge line RL and generates a measured drawing SD, which is an object shape drawing, based on the ridge line RL and the contour line CO.

[0057] In this way, the information processing device 100 of this embodiment can draw the ridge line RL at an appropriate position by extracting specific points SP, which are concave or convex points on the contour line CO, based on the first-order differential value DV1 and the second-order differential value DV2 at each point on the contour line CO of the cross section CS of the object VE, and setting a horizontal line passing through the specific points SP as the ridge line RL. Therefore, the information processing device 100 of this embodiment can automatically create a measured drawing SD of the object VE, in which the ridge line RL is drawn at an appropriate position, efficiently and with reduced variation, without requiring skilled techniques.

[0058] Furthermore, in the information processing device 100 of this embodiment, the differential value acquisition unit 124 acquires a first-order differential value DV1 and a second-order differential value DV2 for two intersecting directions, and the specific point extraction unit 125 extracts a specific point SP based on the first-order differential value DV1 and the second-order differential value DV2 for each of the two directions. Therefore, the information processing device 100 of this embodiment can automatically create a measured map SD on which the ridge line RL is drawn at a more appropriate position.

[0059] In the information processing device 100 of this embodiment, the two directions are the horizontal direction (x direction) and the vertical direction (y direction), and the specific point extraction unit 125 extracts, as specific points SP, points for which the unevenness index value CV, obtained by subtracting the absolute value |DV2y| of the second-order differential value DV2 in the y direction, the absolute value |DV1x| of the first-order differential value DV1 in the x direction, and the absolute value |DV2x| of the second-order differential value DV2 in the x direction from the absolute value |DV1y| of the first-order differential value DV1 in the y direction, is greater than a predetermined threshold value Th. Points with a relatively large unevenness index value CV thus defined are points that are likely to be concave or convex points on the contour line CO. Therefore, the information processing device 100 of this embodiment can automatically create a measured map SD in which the ridge line RL is drawn at a more appropriate position.

[0060] Moreover, the information processing device 100 of this embodiment further includes a specific point selection unit 126 that selects a specific point SP to be used for setting a ridge line RL from the extracted specific points SP (specific point candidate SPp). According to the information processing device 100 of this embodiment, by selecting a specific point SP that is more appropriate as the starting point of the ridge line RL, it is possible to automatically create a measured drawing SD on which the ridge line RL is drawn at a more appropriate position.

[0061] In the information processing device 100 of this embodiment, the cross-section acquisition unit 121 acquires multiple cross-section data CSD showing the contour lines CO of different cross-sections CS of the object VE, the information processing device 100 further includes a representative cross-section selection unit 127 that selects one of the multiple cross-section data CSD as representative cross-section data CSDr, and the drawing generation unit 120 generates a measured drawing SD for the representative cross-section CSr, which is the cross-section CS represented by the representative cross-section data CSDr. According to the information processing device 100 of this embodiment, it is possible to automatically create a measured drawing SD showing the representative cross-section CSr that appropriately represents the characteristics of the object VE.

[0062] Furthermore, in the information processing device 100 of this embodiment, the representative cross-section selection unit 127 divides the multiple cross-section data CSD into three groups, a first group, a second group, and a third group, in descending order of the number of specific points SP, and selects one of the cross-section data CSD belonging to the second group as the representative cross-section data CSDr. According to the information processing device 100 of this embodiment, it is possible to automatically create a measured drawing SD showing the representative cross-section CSr that more appropriately represents the characteristics of the object VE.

[0063] In the information processing device 100 of this embodiment, the second group is a group configured of the cross-sectional data CSD in which the number of specific points SP matches the median value among the multiple cross-sectional data CSD. According to the information processing device 100 of this embodiment, it is possible to automatically create a measured drawing SD showing a representative cross-section CSr that more appropriately represents the characteristics of the object VE.

[0064] Furthermore, in the information processing device 100 of this embodiment, the representative cross-section selection unit 127 performs grouping for each of the specific points SP (external specific points SPo) on the outer contour line CO of the object VE and the specific points SP (inner specific points SPi) on the inner contour line CO of the object VE, and selects one of the cross-section data CSD belonging to the second group for both the outer contour line CO and the inner contour line CO as the representative cross-section data CSDr. According to the information processing device 100 of this embodiment, it is possible to automatically create a measured drawing SD showing the representative cross-section CSr that more appropriately represents the characteristics of the object VE.

[0065] Furthermore, in the information processing device 100 of this embodiment, the representative cross section selection unit 127 performs clustering to classify multiple cross section data CSD into multiple clusters using the shape index value SV that represents the shape of the cross section CS, and performs grouping on the cross section data CSD that belongs to the largest cluster CLx. According to the information processing device 100 of this embodiment, it is possible to more efficiently and automatically create a measured drawing SD that shows the representative cross section CSr that more appropriately expresses the characteristics of the object VE.

[0066] Furthermore, in the information processing device 100 of this embodiment, the representative cross section selection unit 127 selects, from the cross section data CSD belonging to the second group, one cross section data CSD whose shape index value SV representing the shape of the cross section CS is closest to the center point of the distribution, as the representative cross section data CSDr. According to the information processing device 100 of this embodiment, it is possible to automatically create a measured drawing SD showing the representative cross section CSr that more appropriately represents the characteristics of the object VE.

[0067] B. Variations: The technology disclosed in this specification is not limited to the above-described embodiments, and can be modified into various forms without departing from the spirit thereof, for example, the following modifications are also possible.

[0068] The configuration of the information processing device 100 in the above embodiment is merely an example and can be modified in various ways. In addition, in the above embodiment, part of the configuration realized by hardware may be replaced by software, and conversely, part of the configuration realized by software may be replaced by hardware.

[0069] The contents of the measured drawing generation process in the above embodiment are merely an example and can be modified in various ways. For example, in the above embodiment, a 20-dimensional variable is used as the shape index value SV, but the number of dimensions of the shape index value SV (i.e., the order of the elliptic Fourier transform or the number of dimensions reduced by principal component analysis) can be changed as desired. Furthermore, a method other than principal component analysis (e.g., SVD or t-SNE) may be used as a dimension reduction method. Furthermore, when calculating the shape index value SV, other contour description processes may be adopted instead of the elliptic Fourier transform. Furthermore, the clustering process (S150) may be omitted. Furthermore, the clustering process may be performed multiple times. Furthermore, each piece of data and information may be stored in an external storage device connected via the interface unit 158.

[0070] In the above embodiment, the index value defined by the above-described formula (3) is used as the unevenness index value CV used to extract the specific point candidate SPp. However, the method of calculating the unevenness index value CV can be changed as desired as long as it is calculated based on the first-order differential value DV1 and the second-order differential value DV2. For example, the unevenness index value CV may be calculated by subtracting the absolute value |DV2y| of the y-direction second-order differential value DV2y and the absolute value |DV2x| of the x-direction second-order differential value DV2x from the sum of the absolute value |DV1y| of the y-direction first-order differential value DV1y and the absolute value |DV1x| of the x-direction first-order differential value DV1x. Alternatively, the unevenness index value CV may be calculated by using only the differential value in the y direction, for example, by subtracting the absolute value |DV2y| of the y-direction second-order differential value DV2y from the absolute value |DV1y| of the y-direction first-order differential value DV1y. Conversely, the unevenness index value CV may be calculated using only the x-direction differential value, for example, by subtracting the absolute value |DV2x| of the x-direction second-order differential value DV2x from the absolute value |DV1x| of the x-direction first-order differential value DV1x. The unevenness index value CV may also be calculated by taking the product, quotient, logarithm, etc. of the first-order differential value DV1 and the second-order differential value DV2 in the x and / or y directions. The unevenness index value CV may also be calculated using the first-order differential value DV1 and the second-order differential value DV2 in two directions that intersect each other, rather than just two directions that are orthogonal to each other.

[0071] Alternatively, an index value for the first-order differential value DV1 and an index value for the second-order differential value DV2 may be calculated separately and compared with separately set thresholds to extract a specific point candidate SPp. For example, a point P where the absolute value |DV1y| of the y-direction first-order differential value DV1y is greater than a threshold and the absolute value |DV2y| of the y-direction second-order differential value DV2y is smaller than another threshold may be extracted as a specific point candidate SPp. Alternatively, a point P where the sum of the absolute value |DV1y| of the y-direction first-order differential value DV1y and the absolute value |DV1x| of the x-direction first-order differential value DV1x is greater than a threshold and the sum of the absolute value |DV2y| of the y-direction second-order differential value DV2y and the absolute value |DV2x| of the x-direction second-order differential value DV2x is smaller than another threshold may be extracted as a specific point candidate SPp.

[0072] The method of selecting a specific point SP from the specific point candidates SPp in the above embodiment is merely an example and can be modified in various ways. For example, one of the above three rules (rules 1 to 3) for deleting (narrowing down) the specific point candidates SPp may be deleted, or another rule may be added.

[0073] In the above embodiment, cross-sectional data CSD in which the number of outer specific points SPo matches the overall median and the number of inner specific points SPi matches the overall median are extracted as candidates for representative cross-sectional data CSDr from among the multiple cross-sectional data CSD, but this extraction method is merely an example and various modifications are possible. For example, cross-sectional data CSD in which the number of outer specific points SPo is somewhat close to the overall median (e.g., the difference from the median is within 10% of the median) and the number of inner specific points SPi is somewhat close to the overall median (same) may be extracted as candidates for representative cross-sectional data CSDr from among the multiple cross-sectional data CSD. Alternatively, cross-sectional data CSD in which the number of outer specific points SPo matches the overall average and the number of inner specific points SPi matches the overall average may be extracted as candidates for representative cross-sectional data CSDr from among the multiple cross-sectional data CSD.

[0074] Furthermore, in the above embodiment, an example of applying the information processing device 100 to generate a measured drawing SD of an object VE as an archaeological specimen has been described, but the technology disclosed in this specification is not limited to object VE as an archaeological specimen, and is similarly applicable to generating an object shape drawing that represents the shape of an object using the outline of the cross section of the object and the ridge lines on the surface of the object, for objects in general, including other types of objects such as works of art. [Explanation of symbols]

[0075] 100: Information processing device 110: Control unit 120: Drawing generation unit 121: Cross section acquisition unit 123: Shape index value calculation unit 124: Differential value acquisition unit 125: Specific point extraction unit 126: Specific point selection unit 127: Representative cross section selection unit 130: Memory unit 152: Display unit 156: Operation input unit 158: Interface unit 190: Bus BL: Base line BP1: Quasi-nadir point BP: Nadir point CCL: Median line CE: Center line CL: Cluster CLx: Maximum cluster CO: Contour line COd: Curve COi: Inner contour line COo: Outer contour line CP: Drawing generation program CS: Cross section CSD: Cross section data CSDr: Representative cross section data CSr: Representative cross section CV: Concave-convex index value DV1: First-order differential value DV2: Second-order differential value HL: Peak line LP: Leg Ov: Virtual center axis RL: Ridge line SCD: 3D scan data SDD: Measured drawing data SP: Specific point SPi: Inner specific point SPo: Outer specific point SPp: Specific point candidate SV: Shape index value TL: Tangent VE: Object VP: Virtual vertical plane

Claims

1. 1. An information processing device for generating a shape drawing of an object, the shape of which is represented by at least a contour line of a cross section of the object and a ridge line on a surface of the object, a cross-section acquisition unit that acquires cross-section data indicating the contour line of the cross-section of the object; a differential value acquisition unit that acquires a first-order differential value and a second-order differential value at each point on the contour line; a specific point extraction unit that extracts specific points that are concave or convex points on the contour line based on the first-order differential value and the second-order differential value; a drawing generation unit that sets a horizontal line passing through the specific point as the edge line and generates a drawing of the object shape based on the edge line and the contour line; An information processing device comprising:

2. 2. The information processing device according to claim 1, the differential value acquisition unit acquires the first-order differential value and the second-order differential value for two directions intersecting each other; the specific point extraction unit extracts the specific point based on the first-order differential value and the second-order differential value for each of the two directions; Information processing device.

3. 3. The information processing device according to claim 2, the specific point extraction unit extracts, in at least one direction, a point whose absolute value of the first-order differential value is greater than a predetermined threshold and whose absolute value of the second-order differential value is smaller than a predetermined threshold, as the specific point; Information processing device.

4. 4. The information processing device according to claim 2, the two directions being horizontal and vertical; the specific point extraction unit extracts, as the specific point, a point having an unevenness index value, which is obtained by subtracting the absolute value of the second-order differential value in the vertical direction, the absolute value of the first-order differential value in the horizontal direction, and the absolute value of the second-order differential value in the horizontal direction from the absolute value of the first-order differential value in the vertical direction, and which is greater than a predetermined threshold. Information processing device.

5. The information processing device according to any one of claims 1 to 4, further comprising: a specific point selection unit that selects the specific points to be used for setting the edge lines from the extracted specific points; Information processing device.

6. 6. The information processing device according to claim 1, the cross-section acquisition unit acquires a plurality of cross-section data indicating the contour lines of the cross-sections different from each other of the object, the information processing device further includes a representative cross-section selection unit that selects one of the plurality of cross-section data as representative cross-section data; the drawing generation unit generates the object shape drawing for the representative cross section, which is the cross section represented by the representative cross section data. Information processing device.

7. 7. The information processing device according to claim 6, the representative cross section selection unit performs grouping to divide the plurality of cross section data into three groups, a first group, a second group, and a third group, in descending order of the number of specific points, and selects one of the cross section data belonging to the second group as the representative cross section data; Information processing device.

8. 8. The information processing device according to claim 7, the second group is a group configured of the cross-sectional data in which the number of the specific points matches a median value among the plurality of cross-sectional data; Information processing device.

9. 9. The information processing device according to claim 7 or claim 8, the representative cross section selection unit performs the grouping for each of the specific points on the contour line of the outside of the container and the specific points on the contour line of the inside of the container, and selects one of the cross section data belonging to the second group for both the outer contour line and the inner contour line as the representative cross section data; Information processing device.

10. 10. The information processing device according to claim 7, the representative cross-section selection unit performs clustering to classify the plurality of cross-section data into a plurality of clusters using a shape index value that represents the shape of the cross-section, and performs the grouping on the cross-section data that belongs to the largest cluster. Information processing device.

11. 11. The information processing device according to claim 7, the representative cross-section selection unit selects, from the cross-section data belonging to the second group, one piece of cross-section data having a shape index value representing a shape of the cross-section closest to a center point of distribution as the representative cross-section data; Information processing device.

12. 12. The information processing device according to claim 1, The said objects are archaeological materials, The object shape drawing is a measurement drawing of the archaeological material. Information processing device.

13. 1. An information processing method for generating a shape drawing of an object, which represents the shape of the object using at least a contour line of a cross section of the object and a ridge line on a surface of the object, comprising: acquiring cross-sectional data indicating the contour line of the cross-section of the object; obtaining a first-order differential value and a second-order differential value at each point on the contour line; extracting specific points that are concave or convex points on the contour line based on the first-order differential value and the second-order differential value; a step of setting a horizontal line passing through the specific point as the edge line, and generating the object shape drawing based on the edge line and the contour line; An information processing method comprising:

14. A computer program for generating a drawing of an object shape that represents the shape of an object using at least a cross-sectional outline of the object and a ridge line on the surface of the object, On the computer, A process of acquiring cross-sectional data indicating the contour line of the cross-section of the object; A process of obtaining a first-order differential value and a second-order differential value at each point on the contour line; a process of extracting specific points that are concave or convex points on the contour line based on the first-order differential value and the second-order differential value; a process of setting a horizontal line passing through the specific point as the edge line, and generating the object shape drawing based on the edge line and the contour line; Execute Computer program.

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