JP2007293597A - Analysis device, retrieval device and measurement device, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an analysis device for extracting the featured values of three-dimensional data or image data, and for preparing an index for structuring and managing data by analysis using the extracted featured values. <P>SOLUTION: This analysis device is provided with a storage part 4 for storing object data obtained by integrating the shapes of processing objects into data; a data analysis part 2 for extracting the featured values of the shapes from the object data, and for classifying the object data into data of every feature section of the shapes to be specified according to the featured values; and a management structure generation part 3 for specifying the inter-data hierarchical structure of every feature section on the basis of the classification result of the data analyzing part 2, and for generating index information where the data of every feature section are hierarchically referenced according to the hierarchial structure. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an analysis device for analyzing images and three-dimensional coordinate data, a search device for searching for three-dimensional data using the analysis device, and three-dimensional measurement using the three-dimensional data searched by the search device. The present invention relates to a measurement device and a program for causing a computer to function as these devices.

  In recent years, progress in laser surveying technology has made it possible to acquire highly accurate elevation data, and attempts have been made to create highly accurate three-dimensional maps. In addition, techniques related to the use of a three-dimensional map, such as grasping the current topography and soil volume calculation, are also disclosed in the literature.

  For example, in the method for distinguishing a characteristic terrain disclosed in Patent Document 1, in detecting a characteristic terrain such as a collapsed land or a river based on an aerial photograph, a candidate for a characteristic terrain is used by using color information of the aerial photograph. The region (singular region) to be is extracted, or the singular region is classified based on the three-dimensional information and gradient information of the singular region. As a result, the characteristic terrain can be discriminated efficiently and with high accuracy. The three-dimensional information includes, for example, information representing altitude expressed by TIN (Trianglated Irregular Network) or DTM (Digital Terrain Model) method.

  Patent Document 2 discloses a technology related to a creation planning support system that acquires three-dimensional coordinate data at random points and calculates soil volume using three-dimensional polygon data created from random point cloud data. ing.

JP 2003-140544 A JP 2001-140257 A

  In general, a wide range of three-dimensional data has a large amount of data, and a wide range of three-dimensional maps and the like measured at high density have a huge amount of data. For this reason, there is a problem that a lot of processing time is required when using a three-dimensional map in a business support system or the like.

  Thus, in order to utilize a three-dimensional map, a technique for visualizing three-dimensional data has attracted attention, and a data management and display method for drawing large-capacity three-dimensional data at high speed has been proposed. As a data management method, a method of dividing a two-dimensional area into block areas (for example, 1 km square) and classifying and managing data such as points and lines into blocks is widely known.

  In this method, the standard is uncertain as to which block (or both blocks) should be classified into data across a plurality of blocks. Note that if data is not classified into either block, it is necessary to divide the data at boundaries, which complicates data editing and takes time.

  Moreover, although the system which assumed the surveying and disaster prevention field like patent document 2 is disclosed, the management for searching and extracting the data which belong to a required range from high-capacity three-dimensional data at high speed. Does not have a structure. For this reason, at the time of data acquisition in area calculation or the like, the number of target data for determining whether or not the data is required increases, and the search time becomes enormous.

  In particular, in areas such as disaster prevention, civil engineering, and surveying, there are areas that are more important than other areas such as roads and river basins, and areas such as area and volume are measured in such highly important areas. Often done.

  Further, when data management is performed by dividing a two-dimensional area into block areas, the data is classified regardless of the highly important part. For this reason, when a search area is designated, an efficient search is not always performed.

  The present invention has been made to solve the above-described problems, and is an index for extracting feature quantities of three-dimensional data and image data, and structuring and managing the data by analysis using the extracted feature quantities. It is an object of the present invention to obtain an analysis apparatus capable of creating a computer program and a program for causing a computer to function as the analysis apparatus.

  Another object of the present invention is to obtain a search device capable of efficiently performing a high-speed spatial search using the index and a program for causing a computer to function as the search device.

  Another object of the present invention is to obtain a measuring apparatus capable of performing three-dimensional measurement using three-dimensional polygon data with high accuracy and high speed, and a program for causing a computer to function as the measuring apparatus.

  The analysis apparatus according to the present invention includes a storage unit that stores target data obtained by converting a shape of a processing target into data, and extracts a feature amount of the shape from the target data, for each feature portion of the shape specified by the feature amount. Data analysis unit that classifies the data into the data of the data, and a hierarchical structure between the data of each feature unit based on the classification result of the data analysis unit, and index information that refers to the data of each feature unit hierarchically according to this hierarchical structure And a management structure generation unit for generating.

  According to the present invention, the feature quantity of the terrain is extracted from the three-dimensional data obtained by converting the terrain into data as the shape to be processed, and the data for each feature portion of the terrain specified by the feature quantity, such as a river or a road, for example. Then, the three-dimensional data is classified, the hierarchical structure between the data for each feature is defined based on the classification result, and index information for hierarchically referring to the data for each feature according to the hierarchical structure is generated. Therefore, it is possible to refer hierarchically to data suitable for river management, road management, etc. For example, high-speed search of 3D data (polygon data) existing in a river basin for a river including mainstream and tributaries. There is an effect that can be done.

Embodiment 1 FIG.
In the first embodiment, for example, 3D coordinates of vertexes such as 3D polygon data and 3D data in which the connection order of the vertices is determined are analyzed, and an index for structuring and managing the 3D data is generated. To do.

  FIG. 1 is a block diagram showing a configuration of an analysis apparatus according to Embodiment 1 of the present invention. As shown in the figure, the analysis device 1 according to the first embodiment includes a data analysis unit 2, a management structure generation unit 3, and a storage unit 4. The data analysis unit 2 includes a feature amount extraction unit 5 and a grouping unit 6. The management structure generation unit 3 includes a structuring unit 7 and an index generation unit 8. The index generation unit 8 includes a group index generation unit 9 and a three-dimensional data index generation unit 10.

  The data analysis unit 2 analyzes a plurality of types of feature amounts extracted from images and three-dimensional data (hereinafter referred to as processing target data as appropriate), and converts the three-dimensional polygon data in the processing target data into related objects. Each expressed 3D polygon data is classified into a plurality of groups. The feature quantity extraction means 5 of the data analysis unit 2 extracts feature quantities such as colors and shapes from the processing target data. The grouping unit 6 of the data analysis unit 2 classifies the three-dimensional polygon data into a plurality of groups using the feature amounts extracted by the feature amount extraction unit 5.

  The management structure generation unit 3 uses the feature amounts and grouping results extracted by the data analysis unit 2 to structure the data based on the relevance of the object represented by the three-dimensional polygon data. Create a data structure (index for groups and 3D data) to search and extract at high speed. In the structuring unit 7 of the management structure generating unit 3, a hierarchical structure in which a plurality of types of groups are structured by grouping adjacent groups and related groups using the results of grouping by the grouping unit 6 of the data analysis unit 2. Create information that specifies

  The index generation unit 8 of the management structure generation unit 3 generates an index for high-speed extraction of data related to graphic elements such as vertices between groups and three-dimensional data grouped by the grouping unit 6. The group index generation unit 9 of the index generation unit 8 generates an index of data related to the part that becomes the boundary of the group generated by the grouping unit 6. The three-dimensional data index generation means 10 of the index generation unit 8 generates an index related to graphic elements such as polygons and ridge lines in the three-dimensional data.

  The storage unit 4 analyzes the analysis results extracted from the processing target data by the data analysis unit 2 in addition to the analysis target data such as three-dimensional data of topography and features (roads, buildings, etc.) and images such as aerial photographs. Memorize etc. Here, as the three-dimensional data, there are polygon data having vertices having three-dimensional coordinates and a connection order of the vertices, and those expressed by triangles (including notation by the TIN method), quadrangles, and polygonal surfaces. is there. In the following description, polygon data expressed by triangles is used.

  The above-described feature amount extraction unit 5, grouping unit 6, structuring unit 7, group index generation unit 9 and three-dimensional data index generation unit 10 constitute, for example, an analysis processing program according to the gist of the present invention. It can be realized as a specific means in which software and hardware cooperate by causing an arithmetic processing unit of a computer to execute and controlling its operation.

  The storage unit 4 can be configured on a storage area such as a hard disk device mounted on the computer, a storage medium that can be played back by a drive device, or an external storage device that can perform data communication.

  In the following description, the configuration of the computer itself that embodies the analysis device 1 of the present invention and the basic functions thereof can be easily recognized by those skilled in the art based on the common general technical knowledge in the technical field. The detailed description is omitted because it is not directly related to the essence of the present invention.

Next, the operation will be described.
First, the feature amount extraction unit 5 of the data analysis unit 2 extracts a feature amount from an image or three-dimensional data stored in the storage unit 4. At this time, the feature quantity extraction unit 5 continuously performs identification and area segmentation of terrain and features in addition to general feature quantities (color information, curvature, etc.) related to images and three-dimensional shapes. The feature quantity considering the characteristic (global characteristic) of a simple polygon is extracted.
Here, an example of the feature amount extracted from the three-dimensional data is shown below.
(1a) Angle formed by two surfaces on both sides of the edge (2a) Approximate Gaussian curvature (3a) Polygon density (4a) Length of each side of the polygon of interest (5a) Adjacent to the polygon of interest Existence range of vertices of polygons (6a) Characteristics of area of polygons adjacent to target polygon (7a) Difference between maximum and minimum altitude coordinates of vertexes of target polygon (8a) Parallelism of nearby feature ridges (polygon ridges representing shape features)

Note that (1a) the angle formed by the two surfaces of the surfaces on both sides of the edge is a feature amount used when extracting a characteristic ridge line in a three-dimensional shape. (2a) The approximate Gaussian curvature is a feature amount used when determining the complexity of the shape. Assuming that the angle formed by the two surfaces with respect to the surfaces on both sides of the edge is a, the approximate Gaussian curvature K at the vertex V can be obtained by the following equation (1).
K = a / S (1)
However, the angle a formed by the two surfaces with respect to both surfaces of the edge is a = 2π− (the sum of the angles of the corners of the surface gathered at V), and S = (the sum of the areas gathered at V) / 3. . The sum of the areas gathered at V is the sum of the areas of the polygons having the vertex V in common, and the angle of the corner of the surface gathered at V is the angle at the vertex V between the polygons having the vertex V in common. is there.

(3a) The polygon density is a feature amount for examining the degree of detail of the shape, and is obtained by the number of polygons existing within a radius R from the target polygon, for example.
(4a) The length of each side of the focused polygon is a feature amount for examining the shape characteristics of the focused polygon. By using this feature amount, for example, the ratio of the minimum length side to the maximum length side in the sides constituting the polygon can be obtained.

  (5a) The existence range of the vertices of the polygons adjacent to the target polygon is used to investigate changes in the region characteristics of the feature and the shape characteristics within the same feature in the vicinity of the target polygon. This is a feature value to be used. With this feature amount, for example, for each polygon adjacent to the target polygon, the distance from the vertex farthest from the currently focused polygon can be obtained.

  (6a) The characteristics of the area of the polygon adjacent to the target polygon are the same as the region characteristics of the feature or the same location in the vicinity of the target polygon as in the purpose of the feature amount shown in (5a). This is a feature amount for examining changes in shape characteristics in an object. Examples of the feature amount include a difference between the area of the target polygon and the area of each adjacent polygon, and the ratio between the area of the target polygon and the area of the adjacent polygon.

  (7a) The difference between the maximum value and the minimum value of the elevation coordinates of the vertices in the polygon of interest is a feature quantity for examining the shape characteristics of the polygon of interest in the three-dimensional data of the topography and features. For topography and features, information in the height direction is useful, and the difference between the maximum value and the minimum value of the elevation value is obtained as a feature value, particularly for the vertices constituting the polygon.

  (8a) The parallelism of feature ridgelines (polygon ridgelines representing shape features) in the vicinity is the same as the ridgeline or road boundary line among the feature ridgelines in the three-dimensional data of topography and features. This is a feature value for checking. For example, for a line segment that is continuous for a certain length or longer, the distance from a feature ridge line existing in the vicinity is calculated at regular intervals along the line segment.

  When the analysis target is image data, the feature amount extraction unit 5 extracts edges, color information, luminance, and the like as feature amounts from the image.

  The feature amount extracted from the processing target data is not limited to the above. Further, in extracting the feature quantities shown in (1a) to (8a) from the three-dimensional data, the calculation method of each feature quantity is not limited to the above formula (1) or the contents thereof.

  In the feature quantity extraction unit 5, when the feature quantity is extracted from the processing target data read from the storage unit 4, the feature quantity is stored in the storage unit 4 in association with each extracted data or is output to the grouping unit 6. At least one of the processes is performed.

  When the grouping unit 6 reads the feature amount from the storage unit 4 or inputs the feature amount from the feature amount extraction unit 5, the grouping unit 6 classifies the three-dimensional polygon data into a plurality of groups using the feature amount. Hereinafter, a specific example of the operation in the case where the three-dimensional data of the topography or the feature is the analysis target will be described.

  First, the grouping unit 6 extracts feature ridge lines using the above-described feature amounts (1a), (2a), and (3a) regarding the three-dimensional data among the feature amounts extracted by the feature amount extraction unit 5. To do. As a result, it is possible to find a global ridgeline that can be a boundary between regions having different feature quantities, such as road and river boundary lines, from the three-dimensional data.

  In the grouping means 6, when data defining a ridge line that is a characteristic ridge line is extracted from the three-dimensional data, a flag indicating that it is a characteristic ridge line is assigned to this data and stored in the storage unit 4. In addition, a flag indicating that the vertex is located on the feature ridge line is assigned to the vertex of the polygon located on the feature ridge line, and is stored in the storage unit 4.

  In areas corresponding to rivers, embankments, etc., the shape is often expressed in detail by high-density polygons, compared to plain areas where there is little change in shape. In plain areas with few undulations, polygons belonging to the plain areas are expressed in a similar shape, but the shape of the polygon may change at the boundary between areas with different characteristics, such as plain areas and mountain areas. Many.

  Therefore, the grouping unit 6 appropriately corrects the feature ridge lines using the feature quantities other than the above (1a), (2a), and (3a) regarding the image and the three-dimensional data, and the above (4a) to (8a). Using the feature amount, the shape and area of nearby polygons are analyzed globally, and it is determined whether the currently focused polygon and the neighboring polygon have the same feature or area characteristics. Group polygons near the edge.

  In this grouping, the grouping means 6 labels the same value to polygons belonging to the same group. In the grouping, color information extracted from the image may be used. The grouping means 6 stores the grouped result in the storage unit 4 and outputs it to the structuring unit 7 of the management structure generating unit 3.

  The structuring unit 7 of the management structure generating unit 3 uses the result of grouping by the grouping unit 6 to create a hierarchical structure in which adjacent groups and related groups are grouped, and coordinates of the circumscribed rectangular area of each layer Calculate the value.

Here, a specific operation example of the structuring unit 7 will be described.
The topographic region of the three-dimensional data to be analyzed shown on the right side of FIG. 2 is divided by, for example, four map figures 11, and area 1 is an arbitrary area in map figure 11. Further, it is assumed that the three-dimensional polygon data in the area 1 is divided into five groups A to E by the grouping means 6 as shown on the left side of FIG.

  In the structuring unit 7, for such an area 1, the average elevation of the vertices belonging to a certain group, the minimum and maximum values of the elevation, the total perimeter of the boundary line of the group, the parallelism and intersection of the characteristic ridge lines Continuous polygon characteristics, such as whether or not there are other feature ridges that exist within a certain range from a certain feature ridge, and if they intersect, the length of successive feature ridges (Global characteristics) is calculated to obtain the relationship between groups.

  Based on the relationship between the groups, for example, it has a minimum elevation compared to the vicinity shown in the topographic cross section on the right side of FIG. 3 (the cross section taken along the line aa in the area 1 shown on the left side of FIG. 3). The region belonging to the group A corresponding to the portion surrounded by the characteristic ridgeline 12 having parallelism is estimated as a river (see the left side of FIG. 3). In addition, the upstream and downstream of the river is estimated from the altitude value of the vertex in group A, and the soil area (groups B and D) and the dike area (groups C and E) are adjacent due to the symmetry of the group adjacent to group A. (See FIG. 3).

  From the above estimation results, the structuring unit 7 regards the groups A to E as a structure of a river basin including a river and performs structuring. For example, XY coordinates are applied to a rectangular area that is area 1, the area in area 1 is scanned in the X-axis direction and the Y-axis direction, and the adjacency relationship between the groups is checked in the left-right and up-down directions, and groups A to E and The other parts (areas belonging to groups F and G shown in FIG. 2) are structured.

  FIG. 4 is a diagram showing an example of structuring the topographic region data shown in FIG. In FIG. 4, names such as “river” and “soil region (right)” are names given when the features of each group are estimated based on an altitude average of vertices belonging to the group. The right and left indicate the left and right of the direction from the upstream to the downstream of the river. A to E shown on the right of the name are labels given when the polygons in the area 1 are divided into groups, and correspond to the groups A to E shown in FIG.

  In the structuring unit 7, the group in the area 1 shown in FIG. 2 is further grouped in the order of the group D, group E, group G in order of the river (group A) on the right side of the river (group A). The left side is further grouped in the order of group B, group C, and group F in the order closer to the river. In this way, the groups B to E in the area 1 are structured as groups related to the group A. That is, in FIG. 4, the group A for the river is the highest, the groups B and D are in the lowest hierarchy, the groups C and E are in the lower hierarchy, and the groups F and G are in the lowest.

  Group A is given the minimum coordinates (corresponding to point A in FIG. 2) and the maximum coordinates (corresponding to point A in FIG. 2) of the circumscribed rectangular regions of groups B to G, which are lower layers. That is, the maximum coordinate value and the minimum coordinate value in the rectangular area (frame of area 1) circumscribing the groups B to G are set as information regarding the group A.

In the above, the case where all the groups existing in the area 1 are related groups is shown. However, when there are groups that are not related to each other, these are distinguished and structured.
FIG. 5 is a diagram illustrating data structuring when, for example, in the area 1, in addition to the groups A to E, a group P (road) and a group Q (sidewalk) that are less relevant to rivers exist. Note that the level of relevance is determined by using feature amounts relating to images and three-dimensional data.

  In the example of FIG. 5, the group P related to the road is set at the highest level separately from the group A of the river, and the group Q related to the sidewalk is set as a lower hierarchy. Also for the group P, the coordinates (maximum coordinates, minimum coordinates) of the circumscribed rectangular area of the group Q which is the lower hierarchy are set. Further, the name “walk (right)” is the name given when the features of the group Q are estimated, and the right indicates the left and right regarding the direction from the upstream to the downstream of the river as in FIG. . Q is a label given when the polygons in area 1 are divided into groups.

The structuring result obtained in this way is managed in association with the hierarchical structure of the block when the target terrain area is divided into block areas by a map or the like.
6 is a diagram showing an example of data structuring for the terrain area to be analyzed shown on the right side of FIG. In FIG. 6A, the terrain area to be analyzed shown on the right side of FIG. 2 is divided into predetermined analysis areas, divided into blocks (blocks 1 to 4) for each map, and block 2 is further smaller. The area is divided into areas (areas 1 to 4). In FIG. 6, the size of blocks and areas and the division positions are arbitrary.

  FIG. 6 (b) shows an example of data structuring in which the structuring results for each analysis area in FIG. 6 (a) are hierarchically organized, and assigned to the terrain area to be analyzed and each block. In addition, the minimum and maximum coordinates of each circumscribed rectangular area are set in association with the data of each layer. The structured data of the areas 1 to 4 constituting the structured data of the block 2 is data structured by grouping the features in the areas as shown in FIGS.

  The circumscribed rectangular area of the target area is an area corresponding to a frame surrounding blocks 1 to 4 in FIG. 6A, and each circumscribed rectangular area of blocks 1 to 4 surrounds blocks 1 to 4, respectively. This is an area corresponding to a frame.

  The structuring unit 7 of the management structure generating unit 3 obtains the result of data structuring as shown in FIG. 6 (the hierarchical structure of the area when the target terrain area is divided into a plurality of blocks, and an arbitrary With respect to the area, a hierarchical structure created based on the relevance and adjacency between groups and the coordinates of the circumscribed rectangular area of each hierarchy are created and stored in the storage unit 4.

  Next, the index generation unit 8 of the management structure generation unit 3 generates an index for extracting a desired polygon or vertex at high speed between the groups generated by the grouping unit 6 or in the three-dimensional polygon data. In this index generation, the group index generation unit 9 of the index generation unit 8 generates an index based on the data of the portion that becomes the boundary of the group for the group generated by the grouping unit 6.

  FIG. 7 is a diagram for explaining a process of creating a line group based on characteristic ridge lines from the grouping result shown in FIG. First, the group index generation unit 9 acquires information about feature ridge lines and groups from the storage unit 4, and the feature ridge lines that serve as boundary lines between groups for each group are grouped into a plurality of line groups (hereinafter also referred to as component lines as appropriate). Divide. Specifically, among the feature ridge lines that are the boundary lines of the group, the constituent lines whose angles are equal to or less than the threshold are grouped together as the same line group while sequentially obtaining the angles formed by the adjacent feature ridge lines. If the angle is greater than or equal to the threshold, it is set as another line group.

  For example, let us consider a case where the characteristic ridgelines 1 to 4 and the vertices a1 to a5 connecting them are acquired as the information about the characteristic ridgelines that are the boundary lines of the group A shown in FIG. In this case, the group index generation unit 9 sequentially obtains the angles formed by the adjacent feature ridge lines inside the group A with respect to the vertices, and collects the feature ridge lines whose angles are equal to or greater than the threshold as the same line group. When the angle is less than the threshold value, the line group is set as another line group.

  In the example of FIG. 7A, an angle formed by the feature ridge line 1 and the feature ridge line 2 inside the group A with respect to the vertex a2 (indicated by an arrow in the figure), and the feature ridge line 2 and the feature ridge line 3 are groups with respect to the vertex a3. Since the angle formed inside A is equal to or greater than a threshold value that is a classification criterion for the same line group, the angle is classified into the same line group. On the other hand, the angle formed by the feature ridge line 3 and the feature ridge line 4 on the inner side of the group A with respect to the vertex a4 is less than the above threshold value, and thus is classified into a line group different from the feature ridge line 3 side. As a result, the characteristic ridge lines constituting the boundary line of the group A are divided into line groups assigned with line group numbers 1 to 8 as shown in FIG. 7B.

  Here, for example, the boundary line X shown in FIG. 7C is a boundary line common to the group A and the group B. In the case of such a boundary line, in setting a line group in group B as described above, the same number as the line group number already assigned in group A is set. That is, for the boundary line X, the line group numbers 1 to 3 are given by the line grouping in the group A. Therefore, in the line grouping in the group B, the line group numbers 14 to 16 are not used. As shown in d), the line group numbers 1 to 3 are set as they are. As a result, the line group creation result for group B is as shown in FIG.

Next, the group index generation means 9 uses the result of dividing the feature ridge lines into constituent lines, and for each group, an index based on the constituent lines (hereinafter referred to as a group index), and polygons and ridge lines that serve as group boundaries. Create a list of
FIG. 8 is a diagram showing an example of an index related to group A in FIG. “Shared group 1” and “Shared group 2” in FIG. 8 indicate groups existing on both sides of the configuration line (groups adjacent via the configuration line). In the case of FIG. 2, the shared groups of configuration line 1 (line group with line group number 1) are group A and group B. In FIG. 8, the “configuration line” is a configuration line constituting the group A. The line group numbers are written in the clockwise or counterclockwise order with respect to the closed area as a group.

  The “polygon list reference destination of shared group 1” is the reference position in the polygon list of the polygon belonging to “shared group 1” among the polygons having the constituent lines as ridge lines. Similarly, “polygon list reference destination of shared group 2” is a reference position in the polygon list of polygons belonging to “shared group 2” among the polygons having the constituent lines as ridge lines.

  For example, when the vicinity of the component line 1 of the group A is as shown in FIG. 9, referring to the “polygon list reference destination of the shared group 1” with respect to the “component line 1” of the group index, As the polygons belonging to group A, the positions related to the polygon 101, the polygon 86, and the polygon 97 in the polygon list can be referred to.

  In the “polygon list”, which is a reference destination, polygons having ridgelines that serve as group boundaries are managed, and reference positions for detailed information about polygons including vertex coordinates and the like are set. By referring to the reference position from the “polygon list”, detailed information such as vertex coordinates can be acquired. The index relating to the detailed information of the polygon will be described later in the description of the processing by the three-dimensional data index generation means 10.

  The “ridge line list reference destination” indicates the reference position of the ridge line belonging to the component line in the ridge line list. In the “ridge line list” as a reference destination, a list of ridge lines serving as group boundaries is managed, and reference positions of detailed information about the ridge lines (vertex coordinates of both ends of the ridge line) are set. The index related to the detailed information on the ridge line will be described later in the description of the processing by the three-dimensional data index generation unit 10.

“Coordinates of the end point 1 of the constituent line” and “coordinates of the end point 2 of the constituent line” store the coordinates of the vertex located at the connecting portion of the constituent lines. In the configuration line 1 shown in FIG. 9, the end points are P (vertex on the polygon 101) and Q (vertex on the polygon 97).
The group index, polygon list, and ridge line list created as described above are stored in the storage unit 4 by the group index generation means 9.

The three-dimensional data index generation means 10 generates an index related to graphic elements such as polygons, ridge lines, and vertices with respect to the three-dimensional polygon data.
FIG. 10A is a diagram illustrating an example of an index for managing polygons. In FIG. 10A, “polygon” is referred to from the polygon list shown in FIG. 8 generated by the group index generation means 9. “Ridge line 1”, “Ridge line 2”, and “Ridge line 3” in this index indicate ridge lines constituting the polygon, and vertex coordinates described later from “Ridge line 1”, “Ridge line 2”, and “Ridge line 3”, etc. Detailed information related to the ridgeline of can be referred to. In the example of FIG. 10A, the number of ridge lines is three, and the case of a triangular polygon is shown.

The order of the ridge lines described in “ridge line 1”, “ridge line 2”, and “ridge line 3” is regular clockwise (or counterclockwise) with respect to the polygon. When the “polygon” is a polygon on the constituent line of the group, the ridge lines on the constituent line are stored in the order of “ridge line 1”, “ridge line 2”, and “ridge line 3”. For example, in a polygon having two constituent lines, “ridge line 1” and “ridge line 2” are ridge lines on the constituent lines.
“Vertex X coordinate”, “vertex Y coordinate”, and “vertex Z coordinate” are the coordinates of the vertices constituting the polygon. The “adjacent polygon of ridge line 1” is a polygon adjacent to the polygon indicated by “ridge line 1”, and the reference destination of this polygon is set. Similarly, for “adjacent polygons of ridgeline 2” and “adjacent polygons of ridgeline 3”, reference destinations of polygons adjacent to the polygons indicated by “ridgeline 2” and “ridgeline 3” are respectively set.

Next, the index regarding a ridgeline is demonstrated.
FIG. 10B is a diagram illustrating an example of an index for managing a ridge line. In FIG. 10B, the “ridge line” is referred to from the edge line list generated by the group index generation unit 9 and the “ridge line 1” to “ridge line 3” of the indexes related to the polygon shown in FIG. “Shared polygon 1” and “Shared polygon 2” indicate reference positions to indices related to the polygons on both sides of the ridgeline (polygons adjacent via the ridgeline). “Vertex 1” and “Vertex 2” are two vertices at both ends of the ridge line.
The index created as described above is stored in the storage unit 4 by the three-dimensional data index generation means 10.

  In the above-described example, three-dimensional polygon data is targeted. However, in the case of point cloud data obtained by laser surveying or the like, polygon data is created by Voronoi division or other methods, and this data is stored in the storage unit 4. In the same manner, processing by the data analysis unit 2 and the management structure generation unit 3 can be performed.

  In addition, as an example of the feature amount related to the three-dimensional data, an example in which a feature edge line is extracted using three types of “angle between two surfaces of an edge”, “approximate Gaussian curvature”, and “polygon density” is shown. However, the feature quantity used for extracting the feature ridge line is not limited to the above three types. The feature ridge line may be obtained using another feature amount that can be calculated based on the three-dimensional data.

  Furthermore, the items constituting the index are not limited to those shown in FIG. 8, FIG. 10 (a), and FIG. 10 (b). That is, other items may be added, and a plurality of items may be created by dividing a plurality of items collected in one index in the index shown in FIG. The data structure is not particularly limited, such as a table or a list.

  As described above, according to the first embodiment, since feature quantities considering global three-dimensional data characteristics such as area characteristics of adjacent polygons and vertex existing ranges of adjacent polygons are extracted, a plurality of polygons are extracted. When classifying into groups, it is possible to classify according to the characteristics of features and regions. For this reason, it is possible to perform analysis in accordance with applications such as river management and road management, and it is possible to create an index that can realize high-speed search according to the application.

  In the first embodiment, when the polygons are classified into a plurality of groups, the feature ridge lines are extracted from the three-dimensional data, and are classified by analysis using the extracted feature ridge lines and other feature amounts. It is possible to avoid the generation of unnecessary processing of dividing and managing. For this reason, even when the number of polygons in the original three-dimensional data is enormous, the number of polygons does not increase further, and can be managed efficiently.

Embodiment 2. FIG.
In the second embodiment, a search device that performs high-speed spatial search such as neighborhood search using an index created based on the analysis result of the three-dimensional data shown in the first embodiment will be described.

  FIG. 11 is a block diagram showing a configuration of a search device according to Embodiment 2 of the present invention. As shown in FIG. 11, the search device 13 according to the second embodiment is additionally provided with a search unit 14 in addition to the configuration of FIG. 1 shown in the first embodiment. As in the first embodiment, the data analysis unit 2 extracts a plurality of types of feature values from an image or three-dimensional data, analyzes the extracted feature values, and associates the three-dimensional polygon data in the processing target data with related data. Classify into several groups. The data analysis unit 2 includes a feature amount extraction unit 5 and a grouping unit 6 as in FIG.

  The management structure generator 3 uses the feature amounts and grouping results extracted by the data analyzer 2 to structure the 3D polygon data to be analyzed into data having a hierarchical structure, and to extract the data at high speed Create data structures (indexes for groups and 3D data). The management structure generation unit 3 includes a structuring unit 7 and an index generation unit 8 as in FIG. 1, and the index generation unit 8 includes a group index generation unit 9 and a three-dimensional data index generation unit 10.

  The storage unit 4 includes, in addition to analysis results extracted from three-dimensional data of topography and features (roads, buildings, etc.), images such as aerial photographs, images and three-dimensional data, search result data (polygon, Vertex coordinates, vertex connection order) are stored. The three-dimensional data is polygon data having vertices having three-dimensional coordinates and a connection order of the vertices. In the following description, an example in which polygon data represented by triangles (including TIN) is used, but polygon data may be represented by quadrilateral or polygonal surfaces.

  The search unit 14 searches the space represented by the processing target data using the index related to the three-dimensional data such as vertices and ridgelines and the index related to the group in which a plurality of polygon data are collected, which is created by the management structure generation unit 3. I do. The neighborhood search means 15 of the search unit 14 searches for data existing in the vicinity of the input point or the like. The closed area search means 16 of the search unit 14 searches for data existing in the specified closed area or data intersecting with the closed area.

  The data search unit 2, the management structure generation unit 3, the neighborhood search unit 15 and the closed region search unit 16 of the search unit 14, for example, apply a search processing program according to the gist of the present invention to the arithmetic processing unit of the computer constituting the search unit 13. By executing the program and controlling its operation, it can be realized as a specific means in which software and hardware cooperate.

Next, the operation will be described.
First, the feature amount extraction unit 5 of the data analysis unit 2 extracts a feature amount from an image or three-dimensional data stored in the storage unit 4. At this time, the feature quantity extraction unit 5 continuously performs identification and area segmentation of terrain and features in addition to general feature quantities (color information, curvature, etc.) related to images and three-dimensional shapes. The feature quantity considering the characteristic (global characteristic) of a simple polygon is extracted. The specific operation is the same as that in the first embodiment.

  The grouping unit 6 of the data analysis unit 2 divides the three-dimensional polygon data into a plurality of groups using a plurality of types of feature amounts extracted by the feature amount extraction unit 5. The specific operation is the same as that in the first embodiment.

  The structuring unit 7 of the management structure generating unit 3 uses the result of grouping by the grouping unit 6 of the data analyzing unit 2 to create a hierarchical structure in which adjacent groups and related groups are grouped, and The coordinate value of the circumscribed rectangular area is calculated. The specific operation is the same as that in the first embodiment.

Further, the group index generation unit 9 of the index generation unit 8 generates an index based on the data of the part that becomes the boundary of the group for the group generated by the grouping unit 6. The three-dimensional data index generation means 10 of the index generation unit 8 generates an index related to a graphic element such as a polygon or a ridge line with respect to the three-dimensional data.
Specific operations by these means 9 and 10 are the same as those in the first embodiment. An index structured in this way is constructed and stored in the storage unit 4.

The search unit 14 performs a spatial search using the index stored in the storage unit 4.
First, the proximity search unit 15 of the search unit 14 reads an index from the storage unit 4 and searches for data existing in the vicinity of the designated data in the terrain area to be analyzed.
For example, when a user inputs the coordinates of a certain point using an input device (not shown) connected to the search device 13, it is specified by the vertex of the polygon closest to the point specified by the input coordinate value or the input coordinate value Search for vertices and the like that exist within a certain range from the point to be searched. When the three-dimensional data of the terrain area is a search target, the search device 13 according to Embodiment 2 can search at least the following data.
(1b) Polygon to which the designated point belongs (2b) Polygon adjacent to the polygon including the designated point (3b) N vertices (4b) within a certain range from the designated point in order closest to the designated point (for example, within a circle of radius R ) Existing polygons and vertices

First, (1b) a process of searching for a polygon to which a designated point belongs and (2b) a polygon adjacent to a polygon including the designated point will be specifically described.
FIG. 12 is a diagram for explaining a search operation for a designated point. The block 1 to block 4 shown in the left side view of FIG. 12 have a hierarchical structure as shown in FIG. 6 of the first embodiment, and the data related to the area 2 in the block 2 is the same as that of the first embodiment. It is assumed that the data structure as shown in FIG.

  Here, when the user designates the coordinate value of the point c using an input device (not shown), the neighborhood search means 15 of the search unit 14 immediately reads out the index related to the topographic area shown in the left side view of FIG. Then, using this index, the coordinates (minimum coordinates, maximum coordinates) of the circumscribed rectangular areas of the blocks 1 to 4 in the left side view of FIG. 12 are extracted and compared with the coordinate values of the point c.

  Thereby, for example, when the block 2 is extracted as a block having the coordinate value of the circumscribed rectangular area close to the coordinate value of the point c, the neighborhood search means 15 uses the index information related to the block 2 to search the lower hierarchy of the block 2 Are sequentially identified, and area 2 is identified as the area to which point c belongs. Subsequently, the neighborhood search means 15 uses the index information related to area 2 to sequentially trace the lower layers of area 2 and specify that the group to which point c belongs is group A. In this way, the group to which the designated point c belongs is specified.

Processing for searching for (1b) a polygon to which a designated point belongs and (2b) a polygon adjacent to a polygon including the designated point will be described in more detail.
FIG. 13 is a diagram for explaining a polygon search process to which a designated point belongs, and shows a case where a point P in a polygon U belonging to a group F is designated. The group F is adjacent to the group G via the component line 1, and there are polygons V and W with the component line 1 as a common ridge line, and these and the polygon U are adjacent to each other.

  In FIG. 13, when the coordinates of the point P (which may be two-dimensional or three-dimensional) are designated, the neighborhood searching means 15 refers to the index stored in the storage unit 4 and, as described above, the block or area The coordinate value of the circumscribed rectangular area is compared with the coordinate value of the designated point P to identify a block in the upper hierarchy close to the designated point P, and the area that is the lower hierarchy of the block is traced in order, and the group to which the designated point P belongs Specify F.

  The neighborhood search means 15 identifies the constituent line closest to the designated point P using the index information related to the group F in the index read from the storage unit 4, and the polygon having this constituent line as a common ridge line is shown in FIG. The polygons closest to the designated point P are searched by specifying with reference to the “polygon list reference destination” as shown.

  In the example of FIG. 13, the component line 1 is specified as the component line closest to the designated point P, and the polygon closest to the designated point P among the polygons belonging to the component line 1 (polygon having the component line 1 as an edge line) is searched. Thus, the polygon W (polygon adjacent to the polygon including the designated point P) is specified as the polygon.

Subsequently, when the polygon W closest to the designated point P is specified, the neighborhood search means 15 refers to the polygon related index if the designated point P does not exist on this polygon (or within the polygon). Among polygons that do not belong to line 1 (polygons that do not have component line 1 as an edge), adjacent polygons are specified in order, and the polygon to which the designated point P belongs is specified.
In the example of FIG. 13, the polygon U is specified as a polygon adjacent to the polygon W, and the designated point P exists in the polygon U. Therefore, the neighborhood search unit 15 ends the search process. As a result, data relating to the polygon U is output as a polygon as a search result.

  On the other hand, when the point Q shown in FIG. 13 is input as the designated point, the group F is specified as the group to which the designated point Q belongs, and the constituent line 1 is the closest to the designated point Q with respect to the group F. Is identified. Similarly, by specifying the polygons belonging to the component line 1 in order, the polygon V is specified as the polygon closest to the designated point Q. Since this polygon V includes the designated point Q, data relating to the polygon V is output as a search result.

  Further, (2b) as a process of searching for a polygon adjacent to the polygon including the designated point, the polygon including the designated point P is specified by referring to the polygon-related index as in the case where the point P is designated in FIG. In order to do so, the polygons adjacent to the polygon may be identified in order. Further, as in the case where the point Q is designated, the polygon V including the designated point Q is identified first, and the polygons adjacent to the polygon V can be identified sequentially with reference to the polygon-related index.

Next, (3b) a process of searching for N vertices in the order closest to the designated point will be described.
In the process of searching for N vertices in order from the specified point, after the polygon to which the specified point belongs is specified by the above search operation, the operation of specifying adjacent polygons in order is repeated a plurality of times, and the polygons including the specified point are A plurality of polygons are specified, and N vertices are specified in ascending order of distance between the vertexes of each polygon and the designated point.

  FIG. 14 is a diagram for explaining a process of searching for N vertices in the order closest to the designated point, and shows a case where a point P in the polygon U belonging to the group F is designated. The group F is adjacent to the group G via the component line 1, and there are polygons V and W with the component line 1 as a common ridge line, and the polygon V, polygon W, polygon X and polygon U are adjacent to each other. ing. Further, the polygon V and the polygon W are adjacent to the polygon Y and the polygon Z belonging to the group G via the component line 1, respectively.

  When a point near the boundary of the group is designated as in the point P shown in FIG. 14, the neighborhood search means 15 includes the boundary in addition to the polygon belonging to the group including the designated point (group F in the example of FIG. 14). Polygons belonging to an adjacent group (group G in the example of FIG. 14) via a line (configuration line 1) are also specified.

  In the example of FIG. 14, after the polygon U is specified as the polygon in which the designated point P exists, the neighborhood searching means 15 first determines the polygons V, W, and V belonging to the group F that is the same group as the polygon adjacent to the polygon U. X is specified. Subsequently, since the polygon W has a ridge line on the component line 1, the polygon Y in the group G is specified by referring to the polygons that are adjacent via the ridge line and referring to the polygon index. Similarly for the polygon V having a ridge line on the component line 1, the adjacent polygon Z belonging to the group G is specified.

  Thereafter, the neighborhood searching means 15 specifies N vertices in ascending order of the distance between each vertex of the polygons V to Z and the designated point P. When N vertices are obtained from the polygons V to Z around the polygon U including the designated point P, the search is terminated. If the number of vertices is less than N, a polygon further adjacent to the polygons identified so far is specified, and the above process is repeated until the number of vertices reaches a predetermined number of N.

Next, (4b) a search process for polygons and vertices existing within a certain range from the designated point will be described.
In the process of searching for polygons and vertices that exist within a certain range (for example, within a circle with a radius R) from the specified point, after identifying the polygon that includes the specified point, refer to the polygon adjacent to that polygon for the polygon index. The process of searching for a polygon adjacent to the polygon ends when the distance between the apex of the specified polygon and the specified point exceeds the radius R.

  For example, the search processes (1b) to (3b) described above are executed, and for all the polygons identified so far, the vertexes of the polygons adjacent to each of the polygons are defined by circles having a radius R from the designated point. If it exists outside the range, the search process is terminated, and polygons and vertices within a circle having a radius R from the specified point are output as search results.

  It should be noted that a polygon existing within a certain range from the designated point is retrieved by the above-described retrieval process, and the distance between the vertex of the polygon and the designated point is compared with the radius R afterwards to bring the polygon from the designated point within the certain range. You may make it search the vertex which exists.

  When the search result data (for example, three-dimensional data of the specified polygon, vertex coordinates, vertex connection order) is obtained by the above operation, the neighborhood search unit 15 stores the search result in the storage unit 4.

The closed area search means 16 of the search unit 14 searches for data existing in the closed area specified in the three-dimensional data (terrain area) to be searched or data crossing the closed area.
Hereinafter, a case where a group constituent line exists in the closed region and a case where a group constituent line does not exist in the closed region (a case where the entire closed region is included in the group) will be described.

  FIG. 15 is a flowchart showing the flow of search processing when a group constituent line exists in the closed region. FIG. 16 is a diagram for explaining a search process when a group constituent line exists in the closed region, where the constituent line 1 that is a boundary line between the group F and the group G exists in the closed region. Show. The operation will be described along FIG. 15 while using FIG. 16 as appropriate.

  First, when a user inputs vertex coordinate values constituting a closed region as shown in FIG. 16 using an input device (not shown), the closed region search means 16 uses the group index shown in the first embodiment. The group 4 is read out from the storage unit 4, and the group index is referred to to check in which group each vertex constituting the closed region exists (step ST1). In the example of FIG. 16, it is obtained that the vertices a to d constituting the closed region belong to the group F or the group G.

  Next, the closed region search means 16 examines the places where adjacent vertices are in another group with respect to the vertices a to d constituting the closed region, identifies the constituent line where the closed region and the group intersect, Polygon information that intersects with the closed region among the polygons belonging to the line is acquired (step ST2). In the case of FIG. 16, since the groups to which the vertices belong are different between the vertex a and the vertex b and between the vertex c and the vertex d, the line segment ab and the line segment cd intersect at points p and q, respectively. The constituent line 1 is specified as the line.

  Further, the closed region search means 16 refers to the group index, polygon list, and indexes related to polygons and ridge lines, and among the polygons belonging to the component line 1, polygons F1, polygon F2, polygon F3, Polygon G1, polygon G2, and polygon G3 are specified, and their polygon information (polygon data such as vertex coordinates) is acquired.

  In step ST3, the closed region search unit 16 specifies a ridge line that is not a constituent line and a polygon that is adjacent via the ridge line with respect to the polygon specified in step ST2. For example, in the case of the polygon F1, information regarding the ridge line L1 (indicated by a thick line in FIG. 16) is acquired as a ridge line that is not on the component line among the ridge lines of the polygon F1. Further, polygon information of the polygon F4 is acquired as a polygon adjacent to the ridgeline L1. The same processing is performed for polygon F2, polygon F3, polygon G1, polygon G2, and polygon G3. The polygon information about the polygons F1 to F6 and the polygons G1 to G5 is acquired by the processing so far.

  The closed region searching means 16 determines whether or not all the vertices in one polygon are outside the closed region for each of the polygons F1 to F6 and G1 to G5 (step ST4). At this time, if all the vertices are outside the closed region, the process proceeds to step ST5A. If at least one vertex is a polygon within the closed region, the process proceeds to step ST5B.

  In the example of FIG. 16, since the polygon F4 has a vertex also in the closed region, the process proceeds to step ST5B. In step ST5B, a polygon adjacent to a polygon having a vertex in the closed region is specified. In the case of the polygon F4, the polygon F7 is specified as a polygon adjacent to the polygon F4, and the polygon information is acquired.

  In the same manner as the polygon F7, the closed region searching means 16 searches for polygons that intersect the closed region while tracing the polygons F1 to F3, F5, F6, G1 to G5 belonging to the constituent line 1 in the closed region in order. Identify and get polygon information. The process ends when all the adjacent polygons have been acquired for all the specified polygons.

  On the other hand, in the case of the polygon F6, since all the vertices are outside the closed region, the process proceeds to step ST5A. In step ST5A, polygons whose vertices are outside the closed region are not regarded as search results. In the case of the polygon F6, since all the vertices are outside the closed region, the polygon adjacent to this is not searched, and the polygon F6 is excluded from the search result.

Next, a case where there is no group constituent line in the closed region will be described.
FIG. 17 is a flowchart showing the flow of search processing when there is no group constituent line in the closed region. FIG. 18 is a diagram for explaining search processing when a group constituent line does not exist in the closed region. The closed region exists in the group F adjacent to the group G via the constituent line 1, The case where it does not cross the line 1 is shown. The operation will be described along FIG. 17 while using FIG. 18 as appropriate.

  First, when a user inputs vertex coordinate values constituting a closed region as shown in FIG. 18 using an input device (not shown), the closed region search means 16 uses the group index shown in the first embodiment. The group 4 is read out from the storage unit 4, and the group index is referred to to check in which group each vertex constituting the closed region exists (step ST1). In the example of FIG. 18, a result that all of the vertices a to d configuring the closed region belong to the group F is obtained.

  The closed region search means 16 specifies the nearest group of constituent lines for each vertex a to d that constitutes the closed region (step ST2a). In the example of FIG. 18, information on the component line 4 is acquired for the vertices a and b, the component line 1 is acquired for the vertex c, and the component line 3 is acquired for the vertex d.

  In step ST3a, the closed region search means 16 specifies the polygon closest to the vertex constituting the closed region among the polygons belonging to the constituent lines 1, 3, and 4 specified in step ST2a, and acquires polygon information. In the example of FIG. 18, information on the polygon F1, the polygon F2, the polygon F3, and the polygon F4 is acquired for each of the vertices a to d.

  Subsequently, the closed region search unit 16 refers to the polygon-related index, specifies the polygons adjacent to the polygons F1 to F4, and acquires polygon information (step ST4a). In the example of FIG. 18, polygons F5 to F10 adjacent to the polygons F1 to F4 are specified. Since the vertices a to d constituting the closed region are all in the same group F, the adjacent polygons are not searched for via the ridge line on the component line 1 in the search for the adjacent polygons. For example, since the ridgeline L1a of the polygon F3 (indicated by a bold line in FIG. 18) is on the component line 1, polygon information of the polygon G1 belonging to the group G is not acquired as a polygon adjacent to the ridgeline L1a.

  Thereafter, the closed region search means 16 determines whether or not all the vertices in one polygon are outside the closed region for each polygon specified in step ST4a (step ST5a). At this time, if all the vertices are outside the closed region, the process proceeds to step ST6A. If at least one vertex is a polygon within the closed region, the process proceeds to step ST6B.

  In the example of FIG. 18, the closed region search unit 16 specifies polygons that intersect the closed region while sequentially tracing the polygons F1 to F3 and F5 to F9, and acquires polygon information. The process ends when all the adjacent polygons have been acquired for all the specified polygons.

  On the other hand, in the case of polygon F4 and polygon F10, since all the vertices are outside the closed region, the process proceeds to step ST6A. In step ST6A, polygons whose vertices are outside the closed region are not regarded as search results. In the case of the polygon F4 and the polygon F10, since all the vertices are outside the closed region, the search for the polygons adjacent thereto is not performed, and the polygon F4 and the polygon F10 are excluded from the search results.

  In this way, the search device 13 according to the second embodiment identifies polygons included in the closed region designated by the user and polygons that intersect the closed region, and acquires polygon information. Further, if data related to the vertices and ridgelines of the polygon is stored in the storage unit 4 as a search result, these are read out and the intersection determination with the closed area is performed, so that the vertices and ridgelines included in the closed area described above are closed. Data specifying the ridge line that intersects the area can be acquired afterwards.

  In the above description, when searching for polygons and vertices that exist within a certain range from the designated point, the neighborhood search unit 15 has shown an example of searching within an area defined within a circle of radius R from the designated point. However, the figure used for the search range is not limited to a circle. Instead of giving the radius of the circle, the minimum and maximum coordinates of the rectangle may be given.

  As described above, according to the second embodiment, vertices designated as a search range (vertices constituting a closed region and neighborhood search are performed using a result structured based on the feature amount of three-dimensional data. Specify the group to which the first vertex) belongs. Since the search is started by using the polygon existing on the group composition line close to the vertex of the search range as a clue, the determination can be performed in order from the data in the vicinity of the search range, and the search can be performed efficiently. .

  For example, after limiting the region where the search range exists from a wide range, by making a determination from a portion close to the search range in the group, it is less likely to make a determination for data far from the search range Become. For this reason, the polygon which becomes a search result can be searched efficiently. Therefore, even when the target is 3D data with a large number of polygons in a wide range, a search can be performed at high speed.

  In addition, a group of multiple polygons is greatly involved in limiting the search range from a wide range or acquiring polygons at the start of the search, but this group creation can be used for river management, road management, etc. This can be done by analysis considering usage. For this reason, it is possible to perform a search according to the usage purpose at a high speed by analysis and index construction assuming the usage purpose.

  For example, when a flooded area spreads over time due to river inundation, use the results of structuring to identify adjacent groups in order of distance from the river, and from each group, the direction away from the river in order of distance from the river By specifying adjacent polygons in order toward, polygons near the river can be efficiently searched.

  In the second embodiment, the search device 13 includes the data analysis unit 2 and the management structure generation unit 3. However, based on the analysis result obtained by the analysis device 1 described in the first embodiment. The created index may be read from a storage medium such as a CD-ROM and stored in the storage unit.

  In this case, for example, like the search device 13A shown in FIG. 19, the data analysis unit 2 and the management structure generation unit 3 that function similarly to the analysis device 1 shown in the first embodiment may be omitted. The storage unit 4a in the search device 13A reads and stores an index created based on the analysis result from a storage medium such as a CD-ROM, and the search unit 14 stores the index stored in the storage unit 4a. Referring to the second embodiment, the search is performed.

Embodiment 3 FIG.
In the third embodiment, a spatial search such as a neighborhood search is performed using an index created based on the analysis result of the three-dimensional data shown in the first embodiment, and the terrain to be analyzed is used using the search result. A measurement apparatus that performs three-dimensional measurement such as acquisition of a cross-sectional shape such as a region, area, and volume calculation will be described.

  FIG. 20 is a block diagram showing a configuration of a measuring apparatus according to Embodiment 3 of the present invention. As shown in FIG. 20, the measurement device 17 according to the third embodiment is additionally provided with a measurement unit 18 in addition to the configuration of FIG. 11 shown in the second embodiment.

  Similar to the first embodiment, the data analysis unit 2 extracts a plurality of types of feature amounts from the image and the three-dimensional data, analyzes the extracted feature amounts, and relates the three-dimensional polygon data in the processing target data. Classify into multiple groups. The data analysis unit 2 includes a feature amount extraction unit 5 and a grouping unit 6 as in FIG.

  The management structure generation unit 3 structures the features into data having a hierarchical structure using the feature amount extracted by the data analysis unit 2 and the result of grouping, and a data structure (group and group) for extracting data at high speed. Create an index for 3D data. The management structure generation unit 3 includes a structuring unit 7 and an index generation unit 8 as in FIG. 1, and the index generation unit 8 includes a group index generation unit 9 and a three-dimensional data index generation unit 10.

  In addition to the analysis results extracted from images, images, and 3D data such as 3D data of topography and features (roads, buildings, etc.), images and 3D data, the storage unit 4 stores data (polygon, Vertex coordinates, vertex connection order) are stored. The three-dimensional data is polygon data having vertices having three-dimensional coordinates and a connection order of the vertices. In the following description, an example in which polygon data represented by triangles (including TIN) is used, but polygon data may be represented by quadrilateral or polygonal surfaces.

  The search unit 14 performs a spatial search on the processing target data using the index related to the three-dimensional data such as vertices and ridgelines and the index related to a group of a plurality of polygon data created by the management structure generation unit 3. Moreover, the search part 14 is provided with the vicinity search means 15 and the closed area search means 16 similarly to FIG.

  The measurement unit 18 uses the data searched by the search unit 14 to perform three-dimensional measurement of the area and volume of an object or feature represented by three-dimensional data such as a terrain area. The intersection data acquisition means 19 of the measuring unit 18 acquires polygon data that intersects a plane including a line segment specified at two points designated for taking a cross section, and data of the intersection of the plane and the polygon. The three-dimensional measuring means 20 of the measuring unit 18 performs three-dimensional measurement of distance, area, volume, and the like.

  The data analysis unit 2, the management structure generation unit 3, the search unit 14, the intersection data acquisition unit 19 and the three-dimensional measurement unit 20 in the measurement unit 18 are, for example, a computer that configures the measurement device 17 with a measurement processing program according to the spirit of the present invention. This is executed as a specific means in which the software and the hardware cooperate with each other by causing the arithmetic processing unit to execute the operation.

Next, the operation will be described.
First, the feature amount extraction unit 5 of the data analysis unit 2 extracts a feature amount from an image or three-dimensional data stored in the storage unit 4. At this time, the feature quantity extraction unit 5 continuously performs identification and area segmentation of terrain and features in addition to general feature quantities (color information, curvature, etc.) related to images and three-dimensional shapes. The feature quantity considering the characteristic (global characteristic) of a simple polygon is extracted. The specific operation is the same as that in the first embodiment.

  The grouping unit 6 of the data analysis unit 2 divides the three-dimensional polygon data into a plurality of groups using a plurality of types of feature amounts extracted by the feature amount extraction unit 5. The specific operation is the same as that in the first embodiment.

  The structuring unit 7 of the management structure generating unit 3 uses the result of grouping by the grouping unit 6 of the data analyzing unit 2 to create a hierarchical structure in which adjacent groups and related groups are grouped, and The coordinate value of the circumscribed rectangular area is calculated. The specific operation is the same as that in the first embodiment.

Further, the group index generation unit 9 of the index generation unit 8 generates an index based on the data of the part that becomes the boundary of the group for the group generated by the grouping unit 6. The three-dimensional data index generation means 10 of the index generation unit 8 generates an index related to a graphic element such as a polygon or a ridge line with respect to the three-dimensional data.
Specific operations by these means 9 and 10 are the same as those in the first embodiment. An index structured in this way is constructed and stored in the storage unit 4.

  The search unit 14 reads out an index related to the three-dimensional polygon data or an index related to a group of a plurality of polygon data from the storage unit 4, and performs a spatial search using these indexes. The specific operations of the neighborhood search unit 15 and the closed region search unit 16 are the same as those in the second embodiment. In the third embodiment, the search unit 14 searches the processing target data (polygon or the like) for calculating the cross-sectional shape, area, and volume by the measurement unit 17.

  The measurement unit 17 uses the data searched by the search unit 14 to perform three-dimensional measurement of the area and volume of an object or feature represented by three-dimensional data such as a terrain area. Hereinafter, a process in which the measurement unit 17 acquires the cross-sectional shape of the topographic area represented by the polygon as a search result will be described.

  FIG. 21 is a diagram for explaining the process of measuring the cross-sectional shape of the terrain area, and shows an example in which the cross-sectional shape of a straight line connecting two points designated by the user is obtained. FIG. 22 is a flowchart showing the flow of processing for measuring the cross-sectional shape. The operation will be described along FIG. 22 while using FIG. 21 as appropriate.

  When the user inputs the coordinates of two points serving as both ends of a line segment for which a cross section is to be obtained using an input device (not shown), the vicinity search unit 15 of the search unit 14 starts from one of the two input points. The neighborhood search shown in the second embodiment is executed, the polygon where the start point exists (the polygon to which the start point belongs) is specified, and the polygon information is acquired.

  For example, in FIG. 21, the case where the coordinate values of two points P1 and P2 are input from the user and the point P1 is set as the starting point will be described. In this case, when the neighborhood search is performed on the point P1 by the neighborhood search means 15, the polygon D is specified as the polygon to which the point P1 belongs. Polygon information defining the specified polygon D is stored in the storage unit 4 as a search result by the neighborhood search unit 15 and also output to the intersection data acquisition unit 19 of the measurement unit 17.

  When the intersection data acquisition unit 19 inputs the coordinate values of the two points P1 and P2, the polygon D having the start point (here, the point P1) stored from the proximity search unit 15 of the search unit 14 or the storage unit 4 exists. The polygon information is acquired, and a process of obtaining a polygon that intersects a plane including a line segment connecting the two points P1 and P2 and an intersection of the line segment and the ridge line of the polygon is executed.

FIG. 22 is a flowchart showing the flow of operation by the intersection data acquisition means, and the intersection data acquisition process will be described with reference to this figure.
First, the intersection data acquisition unit 19 reads an index related to a polygon from the storage unit 4, and refers to the index related to the polygon, and includes a line segment connecting the point P1 and the point P2 with respect to the polygon D, perpendicular to the paper surface of FIG. An intersection between a flat plane (hereinafter referred to as a transverse section) and a ridge line is determined, a ridge line intersecting with the transverse section is specified, and the intersection point is obtained (step ST1b). In the example shown in FIG. 21, in polygon D, ridge line 45 is specified as a ridge line that intersects the cross section including the line segment connecting points P <b> 1 and P <b> 2, and point C <b> 1 is obtained as the intersection of the cross section and ridge line 45.

  Next, the intersection data acquisition unit 19 refers to the polygon-related index, acquires polygon information of an adjacent polygon via the ridge line acquired in step ST1b, and relates to the ridge line other than the current ridge line with respect to the adjacent polygon. Crossing with the cross section is determined, and intersecting ridgelines and intersections are obtained (step ST2b). In the case of FIG. 21, the polygon E is specified as a polygon adjacent to the polygon D via the ridge 45, and data relating to the polygon E is acquired. Then, among the ridge lines of the polygon E, the ridge line 49 is specified as the ridge line that intersects the cross section other than the ridge line 45, and the point C2 is calculated as the intersection point.

  Thereafter, the process of step ST2b is repeated until the polygon M to which the end point P2 belongs is reached (step ST3b). In this process, the polygon H, the polygon G, the polygon I, and the polygon K are specified, and the intersections C3 to C6 are acquired as the intersections of the cross section and the ridgelines 48, 78, 108, and 1012. In the intersection data acquisition means 19, after acquiring the intersection C 6, the polygon M is specified as a polygon adjacent to the polygon K via the ridge line 1012, and polygon information relating to this polygon M is acquired. When the intersection data acquisition unit 19 acquires the polygon information of the polygon M where the end point P2 exists, the process ends.

  The intersection data acquisition means 19 stores the data about the polygons (hereinafter referred to as intersection polygons), the ridgelines, and the intersections obtained by the processing described above, which are stored in the storage unit 4 and stored in the measurement unit 18. Output to the three-dimensional measuring means 20.

  In addition, although the case where a cross section is taken on a plane including a line segment connecting two points is shown, the same applies to a case where a cross section is taken on a plane including a plurality of line segments by designating three or more points. As shown in FIG. 23, when three points P1 to P3 are designated, for example, a polygon intersecting a cross section including a line segment connecting the points P1 and P2 with the point P1 as the starting point, The intersection of the polygon ridgeline and the cross section is obtained, and then the polygon intersecting the cross section including the line segment connecting the points P2 and P3, starting from the point P2, and the ridgeline and crossing of the polygon. Find the intersection with the surface.

  In the three-dimensional measuring means 20, when the reference elevation used for calculation of the cross-sectional area input by the user via an input device (not shown) is acquired, the reference elevation, the designated points P1 and P2, and the intersection data acquiring means 19 are acquired. A three-dimensional shape of the cross section is generated from the coordinate values of the intersections and the cross sectional area is calculated. FIG. 24 is a diagram showing a cross-sectional shape of the cross section shown in FIG. The reference altitude described above is the lowest altitude used as a reference when calculating the cross-sectional area, as shown in FIG.

  Further, the cross-sectional area can be calculated for both the portion A and the portion B shown in FIG. For example, when the cross-sectional area of the portion A is obtained, the line segments constituting the cross-sectional shape shown in FIG. 24A are obtained using the three-dimensional coordinates of the intersection obtained by the intersection data obtaining unit 19, and these lines are obtained. By calculating the distance (height) between the minute and the reference altitude position and integrating the distance in the range of the line segment constituting the cross-sectional shape, the cross-sectional area of the portion A can be obtained.

  Moreover, when calculating | requiring the cross-sectional area of the part of B, for example, the maximum altitude (indicated by an arrow in FIG. 24 (b)) at which the distance from the reference altitude to the line segment constituting the cross-sectional shape is the maximum is obtained. The cross-sectional area of the portion B can be obtained by sequentially calculating the altitude position and the distance to the line segment constituting the cross-sectional shape and integrating the distance in the range of the line segment constituting the cross-sectional shape.

Next, the operation | movement which calculates | requires the distance between several points is demonstrated.
When the user inputs a plurality of points whose distances are to be obtained using an input device (not shown), the intersection data acquisition unit 19 performs a crossing including line segments defined by the plurality of points input by the same operation as described above. Get the three-dimensional coordinates of the intersection of the surface and the polygon. The three-dimensional measuring unit 20 receives the coordinates of the intersection acquired by the intersection data acquiring unit 19 and calculates the distance between the intersections as the distance between the input points.

  When three or more points are input as points for which the distance is to be obtained, a line segment is obtained from one of the inputted points as a starting point, and the intersection of the cross section including the line segment and the polygon 3 Dimensional coordinates are acquired and the distance between each intersection is calculated. Then, in the same manner as described above, a line segment is obtained starting from the input point adjacent to the input point that was the previous start point, and the three-dimensional coordinates of the intersection of the cross section including the line segment and the polygon are obtained. Can be processed by calculating up to the input point of the end point.

Next, an operation for calculating the area and volume in the designated closed region will be described.
The area can be obtained by two types of methods when the closed region is projected on a two-dimensional plane (when the vertex Z coordinate of each polygon is not considered) and when the vertex Z coordinate of each polygon is taken into consideration. .

A process for calculating the area of the closed region projected on the two-dimensional plane (when the vertex Z coordinate of each polygon is not considered) will be described.
When the user inputs the coordinates of the vertices constituting the closed region using an input device (not shown), the coordinates are input to the three-dimensional measuring means 20 of the measuring unit 18.
The three-dimensional measuring means 20 obtains the area of the closed region projected on the two-dimensional plane using the following equation (2) using the coordinate sequence of the vertices constituting the closed region input by the user. However, the coordinates of the n points P1, P2,..., Pn constituting the closed region are (x ₁ , y ₁ ), (x ₂ , y ₂ ), ..., (x _n , y _n ). Since it is a closed region, the point P1 and the point Pn are the same point. Further, P1, P2,..., Pn are arranged in a clockwise direction. If the vertices are arranged counterclockwise, the absolute value | S | is the area.

Next, an area calculation process considering the vertex Z coordinate of each polygon will be described.
When the user inputs coordinates of vertices constituting the closed area using an input device (not shown), the closed area search means 16 of the search unit 14 includes polygons included in the closed area and polygons intersecting the closed area. Get polygon information of. The specific operation is the same as that in the second embodiment. The acquired polygon information is stored in the storage unit 4 as a search result, and is also input to the three-dimensional measuring unit 20 of the measuring unit 18.

  When the three-dimensional measuring means 20 obtains polygon information of polygons included in the closed region, it calculates the area of each polygon. Also, with respect to polygons that intersect the line segment that constitutes the closed area, the intersection between the line segment that constitutes the closed area and the polygon is calculated, and the vertices that exist in the closed area are obtained from the vertices that constitute the polygon. The area on the side in the closed region is calculated. When the area of each polygon is obtained in this way, the three-dimensional measuring means 20 calculates the total area of the polygons obtained as described above as the area in consideration of the vertex Z coordinate of the polygon.

Next, the process for calculating the volume in the closed region will be described.
When the user inputs the coordinates of vertices constituting the closed area using an input device (not shown), the closed area search means 16 of the search unit 14 is a polygon included in the closed area as in the second embodiment. And polygon information of a polygon intersecting the closed region is acquired. The three-dimensional measuring unit 20 uses the polygon information acquired by the closed region search unit 16 to obtain the volume of each polygon in the closed region and the polygon intersecting the closed region, and calculates the total volume of each polygon in the closed region. Calculated as the volume of. As in the case of calculating the area, the volume of the portion in the closed region is obtained for the polygon that intersects the closed region.

  As described above, according to the third embodiment, the cross-sectional shape is obtained based on the intersection point between the plane including the cross-section cut with respect to the line segment defined by the input designated point and the polygon (three-dimensional polygon data). Therefore, when using high-definition three-dimensional polygon data, a cross-sectional shape can be obtained without degrading accuracy. In addition, since an index constructed based on the analysis result shown in the first embodiment is used, even when the number of polygons of the three-dimensional data is enormous, intersections are acquired at high speed, and a highly accurate cross section is obtained. The shape can be measured at high speed.

  Further, according to the third embodiment, since the area and volume are calculated based on the polygons existing in the designated closed region, accuracy is not deteriorated when using high-definition three-dimensional polygon data. The area and volume can be calculated. In addition, since the index constructed based on the analysis result shown in the first embodiment is used, even if the number of polygons in the three-dimensional data is enormous, the polygons existing in the closed region or intersecting the closed region Polygons to be searched can be searched at high speed, and the area and volume can be obtained with high accuracy and high speed.

  In the said Embodiment 3, although the structure provided with the data analysis part 2 and the management structure production | generation part 3 was shown as the measuring apparatus 17, it produced based on the analysis result by the analysis apparatus 1 shown in the said Embodiment 1. FIG. The index may be read from a storage medium such as a CD-ROM and stored in the storage unit.

  In this case, for example, like the measurement device 17A shown in FIG. 25, the data analysis unit 2 and the management structure generation unit 3 that function in the same manner as the analysis device 1 described in the first embodiment may be omitted. In the storage unit 4b in the measurement device 17A, an index created based on the analysis result is read and stored from a storage medium such as a CD-ROM, and the search unit 14 is stored in the storage unit 4b. With reference to the index, a search is performed in the same manner as in the second embodiment, and the measurement unit 18 can calculate a cross-sectional shape and a volume as in the third embodiment.

In the first to third embodiments, a terrain area having a terrain such as a river or a feature such as a road is taken as an example of the shape to be processed. However, the three-dimensional data to be index generated is a terrain area. It is not limited to the data regarding. In other words, the present invention can be applied as long as it has a shape that can be expressed by three-dimensional data (polygon data) or the like.
Similarly, the search for the vicinity of the designated point, the data to be searched for the data existing in the closed area, and the data intersecting with the closed area are not limited to the data related to the terrain area. The target for dimension measurement is not limited to data relating to the terrain area.

It is a block diagram which shows the structure of the analyzer by Embodiment 1 of this invention. It is a figure which shows the topography area | region of analysis object, and its grouping result. It is a figure for demonstrating the process which determines a specific area | region in the topography area | region in FIG. It is a figure which shows an example of structuring of process target data. It is a figure which shows an example of structuring in the case where the group which is not mutually related exists. It is a figure which shows the example of data structuring with respect to the terrain area of the analysis object shown on the right side of FIG. It is a figure for demonstrating the process which produces a line group. It is a figure which shows the example of the index regarding the group A in FIG. It is a figure which shows the component line and the polygon which touches this. It is a figure which shows an example of an index. It is a block diagram which shows the structure of the search device by Embodiment 2 of this invention. It is a figure for demonstrating search operation | movement of a designated point. It is a figure for demonstrating the search process of the polygon to which the designated point belongs. It is a figure for demonstrating the process which searches N vertices in the order nearest to a designated point. It is a flowchart which shows the flow of a search process in case the composition line of a group exists in a closed area | region. It is a figure for demonstrating the search process in case the structure line of a group exists in a closed area | region. It is a flowchart which shows the flow of a search process when the group composition line does not exist in a closed area | region. It is a figure for demonstrating the search process when the group composition line does not exist in a closed area | region. It is a block diagram which shows the other structural example of the search device by Embodiment 2. It is a block diagram which shows the structure of the measuring device by Embodiment 3 of this invention. It is a figure for demonstrating the process which measures the cross-sectional shape of a topography area | region. It is a flowchart which shows the flow of operation | movement by an intersection data acquisition means. It is a figure for demonstrating the case where a cross section is taken with the line segment specified by 3 points | pieces. It is a figure which shows the cross-sectional shape in the cross section shown in FIG. It is a block diagram which shows the other structural example of the measuring device by Embodiment 3.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 Analysis apparatus, 2 Data analysis part, 3 Management structure production | generation part, 4, 4a, 4b Storage part, 5 Feature-value extraction means, 6 Grouping means, 7 Structured part, 8 Index production | generation part, 9 Group index production | generation means, DESCRIPTION OF SYMBOLS 10 3D data index production | generation means, 11 Maple, 12 Feature ridgeline, 13, 13A search device, 14 Search part, 15 Neighbor search means, 16 Closed area search means, 17, 17A Measurement apparatus, 18 Measurement part, 19 Cross data Acquisition means, 20 3D measurement means.

Claims (17)

A storage unit for storing target data obtained by converting the shape of the processing target into data;
A data analysis unit that extracts the feature amount of the shape from the target data and classifies the target data into data for each feature portion of the shape specified by the feature amount;
A management structure generation unit that defines a hierarchical structure between data for each feature unit based on a classification result of the data analysis unit, and generates index information in which the data for each feature unit is hierarchically referenced according to the hierarchical structure And an analysis device.   The management structure generation unit is configured such that data for each feature is classified into a plurality of groups, the data for each feature is hierarchically referenced according to a hierarchical structure defined between the groups, and the boundary between the features The analysis apparatus according to claim 1, wherein index information for referring to data relating to a graphic element representing the characteristic portion is generated based on data related to a portion. The data analysis department
Feature amount extraction means for extracting feature amounts from the target data;
Grouping means for classifying the target data into groups for each feature portion specified by the feature amount;
The management structure generator
A structuring unit for defining a hierarchical structure between the groups based on a classification result of the grouping means;
The analysis apparatus according to claim 1, further comprising: an index generation unit that generates index information in which data for each feature unit is hierarchically referenced according to the hierarchical structure. The index generator
Group index generating means for generating index information of data related to the boundary portion between the feature parts classified for each group;
4. The analysis apparatus according to claim 3, further comprising: three-dimensional data index generation means for generating index information of data relating to graphic elements representing the characteristic parts classified for each group.   The data analysis unit extracts a feature amount representing a global characteristic of the shape to be processed from the target data, and classifies the target data into data for each feature portion of the shape specified by the feature amount. The analysis device according to claim 1. The data analysis unit divides the target data into a plurality of divided data, extracts a feature amount of the shape represented by the divided data, and extracts the divided data for each feature portion of the shape specified by the feature amount. Classify it into data,
The management structure generation unit defines a hierarchical structure between the divided data and the data for each feature, and generates index information in which the divided data and the data for each characteristic are referenced hierarchically according to the hierarchical structure The analysis apparatus according to claim 1, wherein: A storage unit for storing target data obtained by converting the shape of the processing target into data;
A data analysis unit that extracts the feature amount of the shape from the target data and classifies the target data into data for each feature portion of the shape specified by the feature amount;
A management structure generation unit that defines a hierarchical structure between data for each feature unit based on a classification result of the data analysis unit, and generates index information in which the data for each feature unit is hierarchically referenced according to the hierarchical structure When,
A search device comprising: a search unit that searches for data relating to the designated part by referring to the data for each feature part based on the index information when searching for data relating to the designated part in the shape to be processed. Index information that stores target data obtained by converting the shape of the processing target into data, defines a hierarchical structure between the data for each feature of the shape, and hierarchically refers to the data for each feature according to this hierarchical structure A storage unit for storing
A search device comprising: a search unit that searches for data relating to the designated part by referring to the data for each feature part based on the index information when searching for data relating to the designated part in the shape to be processed. The search part
Neighborhood search means for searching for data related to a designated portion and its vicinity in data for each feature using index information;
The closed area search means for searching for data related to a portion inside the closed area designated as the shape to be processed and a portion intersecting with the boundary of the closed area using the index information. 8. The search device according to 8. The storage unit categorizes the data for each feature part into a plurality of groups, hierarchically refers to the data for each feature part according to the hierarchical structure defined between the groups, and at the boundary between the feature parts. Storing index information to which data related to graphic elements representing the feature portion is referred based on related data;
The search device according to claim 7 or 8, wherein the search unit searches for data related to a designated part in the shape to be processed using the index information. A storage unit for storing target data obtained by converting the shape of the processing target into data;
A data analysis unit that extracts the feature amount of the shape from the target data and classifies the target data into data for each feature portion of the shape specified by the feature amount;
A management structure generation unit that defines a hierarchical structure between data for each feature unit based on a classification result of the data analysis unit, and generates index information in which the data for each feature unit is hierarchically referenced according to the hierarchical structure When,
In searching for data related to the designated part in the shape to be processed, a search part for searching for data related to the designated part by referring to the data for each feature part by the index information;
A measurement apparatus comprising: a measurement unit that performs measurement related to the shape of the processing target using data searched by the search unit. Index information that stores target data obtained by converting the shape of the processing target into data, defines a hierarchical structure between the data for each feature of the shape, and hierarchically refers to the data for each feature according to this hierarchical structure A storage unit for storing
In searching for data related to the designated part in the shape to be processed, a search part for searching for data related to the designated part by referring to the data for each feature part by the index information;
A measurement apparatus comprising: a measurement unit that performs measurement related to the shape of the processing target using data searched by the search unit. The measurement unit
Using the data searched by the search unit, an intersection data acquisition means for acquiring data relating to a part that intersects a plane including a cross section at a specified line segment in the shape to be processed;
The measurement apparatus according to claim 11, further comprising: a three-dimensional measurement unit that performs three-dimensional measurement related to a shape to be processed using the data acquired by the intersection data acquisition unit. The storage unit categorizes the data for each feature part into a plurality of groups, hierarchically refers to the data for each feature part according to the hierarchical structure defined between the groups, and at the boundary between the feature parts. Storing index information to which data related to graphic elements representing the feature portion is referred based on related data;
The search unit uses the index information to search for data related to the designated part in the shape to be processed,
The measurement unit
The measurement apparatus according to claim 11, wherein measurement related to the shape of the processing target is performed using data searched by the search unit. A storage unit for storing target data obtained by converting the shape of the processing target into data;
A data analysis unit that extracts the feature amount of the shape from the target data and classifies the target data into data for each feature portion of the shape specified by the feature amount;
A management structure generation unit that defines a hierarchical structure between data for each feature unit based on a classification result of the data analysis unit, and generates index information in which the data for each feature unit is hierarchically referenced according to the hierarchical structure As a program that allows the computer to function. A storage unit for storing target data obtained by converting the shape of the processing target into data;
A data analysis unit that extracts the feature amount of the shape from the target data and classifies the target data into data for each feature portion of the shape specified by the feature amount;
A management structure generation unit that defines a hierarchical structure between data for each feature unit based on a classification result of the data analysis unit, and generates index information in which the data for each feature unit is hierarchically referenced according to the hierarchical structure ,
A program that causes a computer to function as a search unit that searches for data related to a designated part by referring to data for each feature part based on the index information when searching for data related to a designated part in the shape to be processed. A storage unit for storing target data obtained by converting the shape of the processing target into data;
A data analysis unit that extracts the feature amount of the shape from the target data and classifies the target data into data for each feature portion of the shape specified by the feature amount;
A management structure generation unit that defines a hierarchical structure between data for each feature unit based on a classification result of the data analysis unit, and generates index information in which the data for each feature unit is hierarchically referenced according to the hierarchical structure ,
In searching for data relating to the designated part in the shape to be processed, a search part for searching for data relating to the designated part with reference to the data for each feature part by the index information,
A program that causes a computer to function as a measurement unit that performs measurement related to the shape of the processing target using data searched by the search unit.

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