Image recognition method, device, electronic device and readable storage medium

Abstract

The present disclosure provides an image recognition method, including: respectively determining the minimum bounding box and edge information set of two areas to be recognized in an image, the edge information set including the position information of multiple edges of the areas to be recognized. Respectively deleting the position information of target edges from the two edge information sets, the target edges including the edges located outside the minimum bounding box of the other area to be recognized among the multiple edges of one of the areas to be recognized, to obtain two updated edge information sets. In different cases of determining whether the two updated edge information sets are non-empty sets, it is selected to identify the intersection area between the two areas to be recognized by the position relationship between any point in the area to be recognized corresponding to the non-empty set and the other area to be recognized, or by the position relationship between the two updated second edge information sets. The present disclosure provides an image recognition device, an electronic device, and a computer-readable storage medium.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and more specifically, to an image recognition method, device, electronic device, and readable storage medium. Background Art

[0002] With the continuous development of spatial measurement technology, spatiotemporal data has been increasingly used. For example, spatial measurement technology can use remote sensing data and land cover data to monitor the state changes of land. Spatial measurement technology can also use taxi trajectory data and urban heat maps to find efficient parking locations for picking up passengers.

[0003] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the related art: when facing complex spatiotemporal data, if a global optimization algorithm is used to judge all spatial topological relationships, the judgment process will generate a huge amount of calculation. Among them, the judgment process will also include a large number of repeated calculation steps. Summary of the invention

[0004] In view of this, the present disclosure provides an image recognition method and device.

[0005] One aspect of the present disclosure provides an image recognition method, including: determining a first minimum bounding box and a first edge information set of a first to-be-recognized area in an image, and a second minimum bounding box and a second edge information set of a second to-be-recognized area, respectively, the first and second edge information sets respectively including position information of multiple edges of the first and second to-be-recognized areas; deleting position information of target edges from the first and second edge information sets respectively, the target edges including edges located outside the second minimum bounding box among multiple edges of the first to-be-recognized area, and edges located outside the first minimum bounding box among multiple edges of the second to-be-recognized area, to obtain an updated first edge information set and an updated second edge information set; when it is determined that the updated first edge information set or the updated second edge information set is a non-empty set, identifying an intersection area between the first and second to-be-recognized areas according to a positional relationship between any point in the to-be-recognized area corresponding to the non-empty set and another to-be-recognized area; and when it is determined that both the updated first edge information set and the updated second edge information set are non-empty sets, identifying an intersection area between the first and second to-be-recognized areas according to a positional relationship between the updated first edge information set and the updated second edge information set.

[0006] According to an embodiment of the present disclosure, in the case where the updated first edge information set is a non-empty set, the intersection area between the first and second areas to be identified is identified based on the positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified, including: determining the center point of the second minimum circumscribed frame; taking any point in the first area to be identified as the starting point, determining a ray passing through the center point; and in the case where it is determined that the number of intersections between the ray and multiple edges of the second area to be identified is an odd number, identifying the intersection area between the first and second areas to be identified.

[0007] According to an embodiment of the present disclosure, when the ray passes through the vertex of the second area to be identified, identifying the intersection area between the first and second areas to be identified based on the positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified, also includes: when it is determined that two adjacent edges of the vertex in the second area are located on the same side of the ray, adding one to the number of intersections between the ray and multiple edges of the second area to be identified; and when it is determined that the number of intersections between the ray and multiple edges of the second area to be identified is an odd number, identifying the intersection area between the first and second areas to be identified.

[0008] According to an embodiment of the present disclosure, identifying the intersection area between the first and second areas to be identified based on the positional relationship between the updated first edge information set and the updated second edge information set includes: respectively determining the intersection points between the straight lines where multiple edges in the updated first edge information set are located and the straight lines where multiple edges in the updated second edge set are located; and in the case of determining that the intersection points are all located on the edges, identifying the intersection area between the first and second areas to be identified.

[0009] According to an embodiment of the present disclosure, determining an edge information set of a to-be-identified area in an image includes: constructing a rectangular coordinate system with an arbitrary point in the image as the origin; obtaining coordinate information of multiple edges of the to-be-identified area; and determining the edge information set of the to-be-identified area based on the coordinate information of the multiple edges.

[0010] According to an embodiment of the present disclosure, deleting the position information of the target edge from the first and second edge information sets respectively includes: determining the position information of the target edge of the first edge information set according to the coordinate information of multiple edges in the first edge information set and the coordinate information of the second minimum bounding box, and determining the position information of the target edge of the second edge information set according to the coordinate information of multiple edges in the second edge information set and the coordinate information of the first minimum bounding box; and deleting the position information of the target edge from the first and second edge information sets.

[0011] According to an embodiment of the present disclosure, a plurality of edges of the area to be identified constitute a geometric figure.

[0012] Another aspect of the present disclosure provides an image recognition device, comprising: a first determination module, configured to respectively determine a first minimum bounding box and a first edge information set of a first to-be-recognized area in an image, and a second minimum bounding box and a second edge information set of a second to-be-recognized area, wherein the first and second edge information sets respectively include position information of multiple edges of the first and second to-be-recognized areas; a deletion module, configured to respectively delete position information of target edges from the first and second edge information sets, wherein the target edges include edges located outside the second minimum bounding box among multiple edges of the first to-be-recognized area, and edges of multiple edges of the second to-be-recognized area located outside the first minimum bounding box, to obtain an updated first edge information set and an updated second edge information set; a second determination module, configured to, when determining that the updated first edge information set or the updated second edge information set is a non-empty set, identify an intersection region between the first and second to-be-recognized areas according to a positional relationship between any point in the to-be-recognized area corresponding to the non-empty set and another to-be-recognized area; and a third determination module, configured to, when determining that both the updated first edge information set and the updated second edge information set are non-empty sets, identify an intersection region between the first and second to-be-recognized areas according to a positional relationship between the updated first edge information set and the updated second edge information set.

[0013] Another aspect of the present disclosure provides an electronic device, comprising one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any one of the above claims.

[0014] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0015] According to the embodiments of the present disclosure, since a technical means of classifying the edge set of the area to be identified is adopted, the technical problem of excessive calculation amount when identifying the intersection area is at least partially overcome, thereby achieving the technical effect of improving the recognition efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 Schematically illustrates an exemplary system architecture to which the method and apparatus for applying image recognition disclosed herein can be applied;

[0018] Figure 2 The application scenarios of the image recognition method and device according to the embodiments of the present disclosure are schematically shown;

[0019] Figure 3 The flowchart of the image recognition method according to the embodiment of the present disclosure is schematically shown;

[0020] Figures 4A to 4D A schematic diagram schematically shows the edge and the minimum bounding box of the area to be identified according to an embodiment of the present disclosure;

[0021] Figure 5A A flowchart for determining a set of side information of a region to be identified according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 5B A flowchart of deleting the location information of a target edge from an edge information set according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 5C A flowchart for identifying an intersection area between a first and a second area to be identified according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 5D A schematic diagram schematically shows the positional relationship between any point and the area to be identified according to an embodiment of the present disclosure;

[0025] Figure 5E A flowchart for identifying an intersection area between a first and a second area to be identified according to another embodiment of the present disclosure is schematically shown;

[0026] Fig. 5F A schematic diagram schematically shows the positional relationship between any point and the area to be identified according to another embodiment of the present disclosure;

[0027] Figure 5G A flowchart for identifying an intersection area between a first and a second area to be identified according to another embodiment of the present disclosure is schematically shown;

[0028] Figure 5H A schematic diagram schematically shows the positional relationship between the edges of the area to be identified according to an embodiment of the present disclosure;

[0029] Fig.5I A schematic diagram schematically shows the recognition rate of the image recognition method and the JTS library according to the present disclosure;

[0030] Figure 6 A block diagram schematically shows an image recognition device according to an embodiment of the present disclosure; and

[0031] Figure 7The block diagram schematically shows an electronic device suitable for implementing an image recognition device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0033] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0034] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0035] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0036] The embodiment of the present disclosure provides an image recognition method for identifying the intersection of multiple to-be-recognized areas in an image and a device capable of applying the method. The method comprises respectively determining a first minimum bounding box and a first edge information set of a first to-be-recognized area in an image and a second minimum bounding box and a second edge information set of a second to-be-recognized area, wherein the first and second edge information sets respectively include position information of multiple edges of the first and second to-be-recognized areas; respectively deleting the position information of target edges from the first and second edge information sets, wherein the target edges include edges located outside the second minimum bounding box among multiple edges of the first to-be-recognized area and edges located outside the first minimum bounding box among multiple edges of the second to-be-recognized area, to obtain an updated first edge information set and an updated second edge information set; in the case where it is determined that the updated first edge information set or the updated second edge information set is a non-empty set, identifying the intersection between the first and second to-be-recognized areas according to the positional relationship between any point in the to-be-recognized area corresponding to the non-empty set and another to-be-recognized area; and in the case where it is determined that both the updated first edge information set and the updated second edge information set are non-empty sets, identifying the intersection between the first and second to-be-recognized areas according to the positional relationship between the updated first edge information set and the updated second edge information set.

[0037] Figure 1 The exemplary system architecture 100 to which the method and apparatus for image recognition according to an embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0038] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0039] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as map applications, photo album applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0040] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0041] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0042] It should be noted that the image recognition method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the image recognition device provided in the embodiment of the present disclosure can generally be set in the server 105. The image recognition method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the image recognition device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Alternatively, the image recognition device provided in the embodiment of the present disclosure can also be executed by the terminal devices 101, 102 or 103, or can also be executed by other terminal devices different from the terminal devices 101, 102 or 103. Accordingly, the image recognition device provided in the embodiment of the present disclosure can also be set in the terminal devices 101, 102 or 103, or can be set in other terminal devices different from the terminal devices 101, 102 or 103.

[0043] For example, the image to be processed may be originally stored in any one of the terminal devices 101, 102, or 103 (for example, the terminal device 101, but not limited thereto), or stored in an external storage device and may be imported into the terminal device 101. Then, the terminal device 101 may locally execute the image recognition method provided by the embodiment of the present disclosure, or send the image to be processed to other terminal devices, servers, or server clusters, and the other terminal devices, servers, or server clusters that receive the image to be processed may execute the image recognition method provided by the embodiment of the present disclosure.

[0044] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0045] Figure 2The application scenarios of the image recognition method and device according to the embodiments of the present disclosure are schematically illustrated.

[0046] like Figure 2 As shown, the same geographic image includes a first area to be identified 201 and a second area to be identified 202. The area boundary of the area to be identified can be formed into a complex polygon including tens of thousands of vertices. Among them, the first area to be identified 201 also includes a large number of hollow areas and self-intersecting areas. For example, the first area to be identified 201 is a target development area, and the second area to be identified 202 is a developed area. Therefore, by identifying the intersection area of ​​the first area to be identified 201 and the second area to be identified 202, it can be determined whether there is a developed area in the target development area, thereby realizing accurate development planning for the target development area.

[0047] Figure 3 The flowchart of the image recognition method according to the embodiment of the present disclosure is schematically shown.

[0048] like Figure 3 As shown, the method includes operations S301 to S304.

[0049] In operation S301, a first minimum bounding box and a first edge information set of a first area to be identified in an image and a second minimum bounding box and a second edge information set of a second area to be identified are determined respectively, and the first and second edge information sets respectively include position information of multiple edges of the first and second areas to be identified.

[0050] For example, the image can be a two-dimensional plane image or a three-dimensional stereoscopic image. The area to be identified can be a two-dimensional plane area or a three-dimensional stereoscopic area. Multiple edges of the area to be identified form a geometric figure. Therefore, the geometric figure formed by multiple edges of the area to be identified includes a plane geometric image and a stereoscopic geometric image.

[0051] According to the boundary distribution of the area to be identified, the area to be identified can be converted into a geometric figure. The edge information set includes the position information of all geometric edges of the geometric figure corresponding to the area to be identified. In the case where the image is a two-dimensional plane image, the minimum bounding box is the minimum bounding rectangle of the geometric figure corresponding to the area to be identified.

[0052] In operation S302, position information of target edges is deleted from the first and second edge information sets respectively, where the target edges include edges outside the second minimum bounding box among multiple edges of the first area to be identified, and edges outside the first minimum bounding box among multiple edges of the second area to be identified, to obtain an updated first edge information set and an updated second edge information set.

[0053] In the embodiment of the present disclosure, the position information of the edges of one of the to-be-identified areas is deleted based on the positional relationship between the position information of the edges and the minimum bounding box of another to-be-identified area, thereby reducing the amount of data in the subsequent judgment process.

[0054] In addition, the updated side information set is classified according to its features. Different judgment methods are selected for the to-be-identified area according to the features of different side information sets, so as to select a simple judgment method for a simple position relationship and reduce the amount of calculation in the judgment process.

[0055] In operation S303, when it is determined that the updated first edge information set or the updated second edge information set is a non-empty set, an intersection area between the first and second areas to be identified is identified based on a positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified.

[0056] In operation S304, when it is determined that both the updated first edge information set and the updated second edge information set are non-empty sets, an intersection area between the first and second to-be-identified areas is identified according to a positional relationship between the updated first edge information set and the updated second edge information set.

[0057] In the disclosed embodiment, the intersection relationship between two to-be-identified areas is preliminarily determined by judging whether the updated edge information set is a non-empty set.

[0058] The following 4A to 4D The specific contents of the side information set and the updated side information set are described. 4A to 4D The schematic diagram schematically shows the edge of the area to be identified and the minimum bounding box according to an embodiment of the present disclosure.

[0059] like Figure 4A As shown in FIG. 4 , all edges of the region to be identified B are located outside the minimum bounding box 401 of the region to be identified A. All edges of the region to be identified A are also located outside the minimum bounding box 402 of the region to be identified B. Therefore, after deleting the position information of the edges located outside another region to be identified in each edge information set, the updated edge information set S of the region to be identified A is A and the updated side information set S of the area to be identified B B Are empty sets.

[0060] like Figure 4BAs shown in FIG. 4 , all edges of the region to be identified B are located inside the minimum bounding box 403 of the region to be identified A. All edges of the region to be identified A are located outside the minimum bounding box 404 of the region to be identified B. Therefore, after deleting the position information of the edges located outside another region to be identified in each edge information set, the updated edge information set S of the region to be identified A is A is an empty set, and the updated side information set S of the area to be identified B B Is a non-empty collection.

[0061] like Figure 4C As shown in FIG. 4 , all edges of the region to be identified B are located inside the minimum bounding box 405 of the region to be identified A. All edges of the region to be identified A are located outside the minimum bounding box 406 of the region to be identified B. Therefore, after deleting the position information of the edges located outside another region to be identified in each edge information set, the updated edge information set S of the region to be identified A is A is an empty set, and the updated side information set S of the area to be identified B B Is a non-empty collection.

[0062] like Figure 4D As shown in FIG. 4 , some edges of the region to be identified B are located outside the minimum bounding box 407 of the region to be identified A. Some edges of the region to be identified A are located outside the minimum bounding box 408 of the region to be identified B. Therefore, after deleting the position information of the edges located outside another region to be identified in each edge information set, the updated edge information set S of the region to be identified A is A and the updated side information set S of the area to be identified B B All are non-empty sets.

[0063] exist Figure 4A In the example, since the updated side information set S of the area to be identified A A and the updated side information set S of the area to be identified B B If both of the updated edge information sets are empty sets, it can be determined that there is no intersection between the to-be-recognized area A and the to-be-recognized area B. Therefore, when both of the updated edge information sets are empty sets, the subsequent judgment process can be terminated, and it can be determined that there is no intersection between the two to-be-recognized areas. This can reduce the amount of calculation in the judgment process.

[0064] exist Figure 4B and Figure 4C In the example, since the updated side information set S of the area to be identified A A is an empty set, and the updated side information set S of the area to be identified B BIf the positional relationship between the to-be-recognized region A and the to-be-recognized region B is a non-empty set, it can be determined that the positional relationship between the to-be-recognized region A and the to-be-recognized region B may be that there is no intersection between the to-be-recognized region A and the to-be-recognized region B, or that the to-be-recognized region B is located inside the to-be-recognized region A. In this case, the positional relationship between the two to-be-recognized regions is determined by the method of determining the positional relationship between the point and the geometric figure. Therefore, when only one of the two updated side information sets is a non-empty set, it is possible to further identify whether there is an intersection between the two to-be-recognized regions based on the positional relationship between any point in the to-be-recognized region corresponding to the non-empty set and the other to-be-recognized region.

[0065] exist Figure 4D In the example, since the updated side information set S of the area to be identified A A and the updated side information set S of the area to be identified B B Both are non-empty sets, and it is impossible to directly determine whether there is an intersection between the to-be-recognized area A and the to-be-recognized area B. In this case, the positional relationship between the two to-be-recognized areas is determined by judging the positional relationship between the edges of the two geometric figures. Therefore, when both updated edge information sets are non-empty sets, it is possible to determine whether there is an intersection between the two to-be-recognized areas based on the positional relationship between the updated first edge information set and the updated second edge information set.

[0066] Through the disclosed embodiment, a simpler and more efficient judgment method is selected according to the types of the two updated edge information sets, thereby reducing redundant calculation processes, thereby achieving the effect of reducing the amount of calculation and saving calculation time.

[0067] Reference below Figure 5A to Figure 5I , combined with specific embodiments Figure 3 The method shown is further explained.

[0068] Figure 5A The flowchart of determining the side information set of the area to be identified according to an embodiment of the present disclosure is schematically shown.

[0069] like Figure 5A As shown, an example method for determining a set of side information of a region to be identified in an image may include operations S511 to S513.

[0070] In operation S511, a rectangular coordinate system is constructed with any point in the image as the origin;

[0071] In operation S512, coordinate information of multiple sides of the area to be identified is obtained; and

[0072] In operation S513, an edge information set of the to-be-identified area is determined according to the coordinate information of the plurality of edges.

[0073] In the disclosed embodiment, after a rectangular coordinate system is constructed with any point in the image as the origin, the position information of the edge of each area to be identified can be obtained through a unified standard. If the image is a two-dimensional image, a plane rectangular coordinate system is constructed. If the image is a three-dimensional image, a three-dimensional spatial rectangular coordinate system is constructed.

[0074] The coordinate information of the edge includes the coordinate information of the two endpoints. The value range of the edge in each coordinate axis direction. In a plane rectangular coordinate system, get the domain of each edge in the X-axis direction and the range of each edge in the Y-axis direction. In a spatial rectangular coordinate system, get the domain of each edge in the X-axis and Y-axis directions, and the range of each edge in the Z-axis direction.

[0075] Figure 5B The flowchart of deleting the location information of the target edge from the edge information set according to an embodiment of the present disclosure is schematically shown.

[0076] like Figure 5B As shown, based on operations S511 to S513, an example method for deleting the location information of the target edge from the first and second edge information sets respectively may include operations S521 to S523.

[0077] In operation S521, coordinate information of the first and second minimum bounding boxes is obtained;

[0078] At operation S522, position information of a target edge of the first edge information set is determined based on the coordinate information of the plurality of edges in the first edge information set and the coordinate information of the second minimum bounding box, and position information of a target edge of the second edge information set is determined based on the coordinate information of the plurality of edges in the second edge information set and the coordinate information of the first minimum bounding box; and

[0079] In operation S523, the location information of the target edge is deleted from the first and second edge information sets.

[0080] In the disclosed embodiment, in a rectangular coordinate system, coordinate information of a minimum bounding box is obtained. The coordinate information of the minimum bounding box includes the maximum coordinate value and the minimum coordinate value of the minimum bounding box in different coordinate axis directions in the coordinate system. By comparing the coordinate values ​​of the two end points of each edge in the area to be identified with the maximum and minimum coordinate values ​​of the minimum bounding box of another area to be identified, all edges of one area to be identified that are located outside the minimum bounding box of another area to be identified are determined.

[0081] After deleting the target edge in each edge information set, an updated edge information set is obtained. The updated edge information can be used to make a preliminary judgment on the positional relationship between the two areas to be identified, and the amount of data in the edge information set can be reduced, thereby reducing the amount of calculation that will be generated in the subsequent judgment process.

[0082] Figure 5C The flowchart of identifying the intersection area between the first and second to-be-identified areas according to an embodiment of the present disclosure is schematically shown.

[0083] When the updated first edge information set is a non-empty set, the intersection area between the first and second areas to be identified is identified according to the positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified.

[0084] like Figure 5C As shown, an example method for identifying an intersection area between a first and a second to-be-identified area may include operations S531 to S533.

[0085] In operation S531, determining a center point of a second minimum bounding box;

[0086] In operation S532, taking any point in the first to-be-identified area as a starting point, a ray passing through the center point is determined; and

[0087] In operation S533, when it is determined that the number of intersections between the ray and the plurality of edges of the second to-be-recognized region is an odd number, an intersection region between the first and second to-be-recognized regions is identified.

[0088] In the embodiment of the present disclosure, when it is determined that the updated first edge information set is a non-empty set, the positional relationship between the first area to be identified and the second area to be identified includes two situations: 1) the first area to be identified is located inside the second area to be identified; 2) there is no intersection area between the first area to be identified and the second area to be identified.

[0089] In this case, the positional relationship between the two to-be-identified areas is determined by determining the positional relationship between the points and the geometric figures. For example, by determining whether any point in the to-be-identified area corresponding to the non-empty set is located inside the other to-be-identified area, it is possible to determine whether the to-be-identified area corresponding to the non-empty set is inside the other to-be-identified area.

[0090] For example, a ray passing through the center point of the second minimum bounding box is determined with any point in the first area to be identified as the starting point. When it is determined that the number of intersections between the ray and multiple edges of the second area to be identified is an odd number, the intersection area between the first and second areas to be identified can be determined, and then the intersection area between the first and second areas to be identified can be identified.

[0091] In the case where the geometric figure corresponding to the area to be identified is a plane geometric figure, the center point of the minimum bounding box can be considered as the intersection of the diagonal lines. In the case where the geometric figure corresponding to the area to be identified is a solid geometric figure, the center point of the minimum bounding box can be considered as the intersection of the diagonal lines of the body. Therefore, the center point of the minimum bounding box is located inside the minimum bounding box. Therefore, starting from any point in the first area to be identified, the ray passing through the center point of the second minimum bounding box will pass through the inside of the second area to be identified.

[0092] Figure 5D A schematic diagram schematically shows the positional relationship between any point and the area to be identified according to an embodiment of the present disclosure.

[0093] As shown in Figure 5, when the number of intersections between the ray and the edges of the area to be identified is an odd number, the starting point of the ray is inside the area to be identified. It can be determined that the first area to be identified is inside the second area to be identified, and there is an intersecting area between the first area to be identified and the second area to be identified.

[0094] When the number of intersections between the ray and the edges of the region to be identified is even, the starting point of the ray is outside the region to be identified, thereby determining that there is no intersection between the first region to be identified and the second region to be identified.

[0095] Figure 5E The flowchart of identifying the intersection area between the first and second to-be-identified areas according to another embodiment of the present disclosure is schematically shown.

[0096] like Figure 5E As shown, in the case where the ray passes through the vertex of the second area to be identified, identifying the intersection area between the first and second areas to be identified further includes operations S534 to S535 based on operations S531 to S533.

[0097] For example, an example method of identifying an intersection area between first and second to-be-identified areas may include operations S534 to S535 .

[0098] In operation S534, when it is determined that two adjacent edges of the vertex in the second region are located on the same side of the ray, the number of intersections between the ray and the edges of the second region to be identified is increased by one; and

[0099] In operation S535 , when it is determined that the number of intersections between the ray and the plurality of edges of the second to-be-recognized region is an odd number, an intersection region between the first and second to-be-recognized regions is identified.

[0100] In the embodiment of the present disclosure, when the ray passes through a vertex of the second area to be identified, it is necessary to further determine the positional relationship between the vertex and the ray, and then determine whether the starting point of the ray is inside the second area to be identified.

[0101] Fig. 5F A schematic diagram schematically shows the positional relationship between any point and the area to be identified according to another embodiment of the present disclosure.

[0102] As shown in FIG5 , when the ray passes through the vertex of the second area to be identified, when it is determined that the two adjacent sides of the vertex are on the same side of the ray, the number of intersections between the ray and the multiple sides of the second area to be identified is increased by one. When it is determined that the two adjacent sides of the vertex are on the opposite sides of the ray, the number of intersections between the ray and the multiple sides of the second area to be identified remains unchanged. Then, by determining that the updated number of intersections is an odd number, the intersection area between the first and second areas to be identified is determined, and then the intersection area between the first and second areas to be identified is identified.

[0103] Through the above embodiment, when only one of the two updated edge information sets is a non-empty set, a method of determining whether there is an intersection between the two to-be-identified areas is selected only by determining the positional relationship between the point and the geometric image. This avoids the need to determine the intersection area based on the positional relationship between the edges of the two edge information sets, thereby reducing the amount of calculation in the determination process.

[0104] Figure 5G The flowchart of identifying the intersection area between the first and second to-be-identified areas according to another embodiment of the present disclosure is schematically shown.

[0105] When it is determined that both the updated first edge information set and the updated second edge information set are non-empty sets, an intersection area between the first and second to-be-identified areas is identified according to a positional relationship between the updated first edge information set and the updated second edge information set.

[0106] like Figure 5E As shown, for example, an exemplary method for identifying an intersection area between a first and a second to-be-identified area may include operations S541 to S542.

[0107] In operation S541, intersection points between straight lines where a plurality of edges in the updated first edge information set are located and straight lines where a plurality of edges in the updated second edge set are located are respectively determined; and

[0108] In operation S542, when it is determined that the intersection points are all located on the edge, an intersection area between the first and second to-be-identified areas is identified.

[0109] In the embodiment of the present disclosure, since both updated edge information sets are non-empty sets, in this case, the positional relationship between the two regions to be identified is determined by determining the positional relationship between the edges of the two geometric figures. For example, the updated first and second edge information sets are traversed to determine whether the edges in the updated first edge information set intersect with the edges in the updated second edge information set, thereby determining whether there is an intersecting region between the first region to be identified and the second region to be identified, and then identifying the intersecting region between the first and second regions to be identified.

[0110] For example, the line equation of each line where each edge in the two updated edge information sets is located can be determined first, and then the slope of the line equation and the coordinates of the intersection of the two line equations can be calculated to determine whether the edges of the two updated edge information sets intersect with each other.

[0111] Figure 5H The schematic diagram schematically shows the positional relationship between the edges of the area to be identified according to an embodiment of the present disclosure.

[0112] like Figure 5H As shown in (a), the slope of the straight line where the edge of the first to-be-identified area is located is the same as the slope of the straight line where the edge of the first to-be-identified area is located, and the two edges are parallel and do not intersect. Figure 5H As shown in (b), if the slope of the straight line where the edge of the first to-be-identified area is located is different from the slope of the straight line where the edge of the first to-be-identified area is located, and the intersection of the two straight lines is located on the two edges, then it is determined that the two edges intersect. Figure 5H As shown in (c), when the slope of the straight line where the edge of the first to-be-identified area is located is different from the slope of the straight line where the edge of the first to-be-identified area is located, and the intersection of the two straight lines is not located on the two edges, it is determined that the two edges do not intersect.

[0113] A method for determining whether the intersection point of the lines is located on two edges may be by comparing the coordinates of the intersection point with the coordinates of the endpoints of the two edges.

[0114] After determining that the multiple edges in the updated first edge information set and the multiple edges in the updated second edge set are in an intersecting relationship with each other, an intersecting area between the first and second to-be-identified areas is identified.

[0115] Through the embodiments of the present disclosure, when it is determined that both updated edge information sets are non-empty sets, a method is selected to determine whether there is an intersecting area between two areas to be identified by judging the positional relationship between the edges of the edge information sets. The image recognition method provided by the present disclosure classifies the updated edge information sets and preliminarily classifies the positional relationship between the two areas to be identified. Then, a corresponding judgment method is selected for the two areas to be identified with each positional relationship, so that the two areas to be identified with a simple positional relationship are judged by a simple recognition method, and the two areas to be identified with a relatively complex positional relationship are judged by a relatively complex recognition method. The image recognition method provided by the present disclosure removes redundant calculation steps in the judgment process, reduces the overall calculation amount in the judgment process, saves recognition time, and improves recognition efficiency.

[0116] In order to more comprehensively highlight the effectiveness and advantages of the image recognition method provided by the above embodiment of the present disclosure, the image recognition method provided by the embodiment of the present disclosure is used to perform recognition experiments on three types of surface data with different complexities. By performing an intersection judgment experiment on two surfaces, the detection is repeated 30 times and the time required for judgment is measured. Among them, the three types of surface data include 25,000 vertex data, 90,000 vertex data, and 190,000 vertex data respectively.

[0117] Fig.5I The schematic diagram of the image recognition method and the JTS library recognition rate according to the present disclosure is schematically shown. The JTS library is a geometric graphics algorithm package included in the open source component library GeoTools provided by the Open Source Geographic Information Association (OSGeo). The JTS library uses the nine-intersection model as the basis and basis for judging the spatial position relationship.

[0118] like Fig.5I As shown, for the complex surface with 25,000 vertex data, the average time consumed by the JTS library is about 255 milliseconds, and the average time consumed by the image recognition method provided by the present disclosure is about 54 milliseconds. For the complex surface with 90,000 vertex data, the average time consumed by the JTS library is 1.6 seconds, and the average time consumed by the image recognition method provided by the present disclosure is about 270 milliseconds. For the complex surface with 190,000 vertex data, the average time consumed by the JTS library is about 16.4 seconds, and the average time consumed by the image recognition method provided by the present disclosure is about 423 milliseconds.

[0119] from Fig.5I It can be clearly seen from the provided result data that, compared with the prior art using the JTS library for intersection judgment, the image recognition method provided by the embodiment of the present disclosure is faster.

[0120] Figure 6 The block diagram schematically shows an image recognition device according to an embodiment of the present disclosure.

[0121] like Figure 6 As shown, the image recognition device 600 includes a first determination module 610 , a deletion module 620 , a second determination module 630 and a third determination module 640 .

[0122] The first determination module 610 is used to respectively determine a first minimum bounding box and a first edge information set of a first area to be identified in the image and a second minimum bounding box and a second edge information set of a second area to be identified, wherein the first and second edge information sets respectively include position information of multiple edges of the first and second areas to be identified.

[0123] The deleting module 620 is used to delete the position information of the target edge from the first and second edge information sets respectively, where the target edge includes the edge located outside the second minimum circumscribed frame among the multiple edges of the first area to be identified, and the edge located outside the first minimum circumscribed frame among the multiple edges of the second area to be identified, to obtain an updated first edge information set and an updated second edge information set.

[0124] The second determination module 630 is used to identify the intersection area between the first and second areas to be identified based on the positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified when it is determined that the updated first side information set or the updated second side information set is a non-empty set.

[0125] The third determination module 640 is used to identify the intersection area between the first and second to-be-identified areas according to the positional relationship between the updated first edge information set and the updated second edge information set when it is determined that both the updated first edge information set and the updated second edge information set are non-empty sets.

[0126] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0127] For example, any multiple of the first determination module 610, the deletion module 620, the second determination module 630, and the third determination module 640 can be combined in one module / unit / subunit for implementation, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more of these modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the first determination module 610, the deletion module 620, the second determination module 630, and the third determination module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in any appropriate combination of any of them. Alternatively, at least one of the first determination module 610 , the deletion module 620 , the second determination module 630 , and the third determination module 640 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0128] According to an embodiment of the present disclosure, the first determination module 610 includes: a construction unit, used to construct a rectangular coordinate system with any point in the image as the origin; a first acquisition unit, used to obtain coordinate information of multiple edges of the area to be identified; and a first determination unit, used to determine the edge information set of the area to be identified based on the coordinate information of the multiple edges.

[0129] According to an embodiment of the present disclosure, the deletion module 620 includes: a second acquisition unit, used to acquire coordinate information of the first and second minimum bounding boxes; a second determination unit, used to determine the position information of the target edge of the first edge information set based on the coordinate information of multiple edges in the first edge information set and the coordinate information of the second minimum bounding box, and to determine the position information of the target edge of the second edge information set based on the coordinate information of multiple edges in the second edge information set and the coordinate information of the first minimum bounding box; and a deletion unit, used to delete the position information of the target edge from the first and second edge information sets.

[0130] According to an embodiment of the present disclosure, the second determination module 620 includes: a third determination unit, used to determine the center point of the second minimum circumscribed frame; a fourth determination unit, used to determine a ray passing through the center point with any point in the first area to be identified as the starting point; and a fifth determination unit, used to identify the intersection area between the first and second areas to be identified when the number of intersections between the ray and multiple edges of the second area to be identified is an odd number.

[0131] The second determination module 620 also includes a sixth determination unit, which is used to increase the number of intersections between the ray and the multiple edges of the second area to be identified by one when it is determined that two adjacent edges of the vertices in the second area are located on the same side of the ray; and a seventh determination unit, which is used to identify the intersection area between the first and second areas to be identified when it is determined that the number of intersections between the ray and the multiple edges of the second area to be identified is an odd number.

[0132] According to an embodiment of the present disclosure, the third determination module 630 includes: an eighth determination unit, used to respectively determine the intersection points between the straight lines where multiple edges in the updated first edge information set are located and the straight lines where multiple edges in the updated second edge set are located; and a ninth determination unit, used to identify the intersection area between the first and second areas to be identified when it is determined that all the intersection points are located on the edges.

[0133] It should be noted that the image recognition device part in the embodiment of the present disclosure corresponds to the image recognition method part in the embodiment of the present disclosure. The description of the image recognition device part specifically refers to the image recognition method part, which will not be repeated here.

[0134] Figure 7 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0135] like Figure 7 As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0136] In RAM 703, various programs and data required for the operation of system 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other through bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0137] According to an embodiment of the present disclosure, the system 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The system 700 may further include one or more of the following components connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0138] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0139] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0140] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0141] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or the RAM 703 described above and / or one or more memories other than the ROM 702 and the RAM 703 .

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, a program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0143] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0144] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. An image recognition method, comprising: Determine a first minimum bounding box and a first edge information set of a first area to be identified in the image and a second minimum bounding box and a second edge information set of a second area to be identified, respectively, wherein the first and second edge information sets respectively include position information of a plurality of edges of the first and second areas to be identified; Deleting the position information of the target edge from the first and second edge information sets respectively, the target edge including the edge located outside the second minimum circumscribed frame among the multiple edges of the first to-be-identified area, and the edge located outside the first minimum circumscribed frame among the multiple edges of the second to-be-identified area, to obtain an updated first edge information set and an updated second edge information set; When it is determined that the updated first side information set or the updated second side information set is a non-empty set, identifying an intersection area between the first and second areas to be identified according to a positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified; as well as When it is determined that both the updated first edge information set and the updated second edge information set are non-empty sets, an intersection area between the first and second to-be-identified areas is identified according to a positional relationship between the updated first edge information set and the updated second edge information set.

2. The method according to claim 1, wherein: In the case where the updated first edge information set is a non-empty set, identifying the intersection area between the first and second areas to be identified according to a positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified, comprises: Determine the center point of the second minimum bounding box; Taking any point in the first to-be-identified area as a starting point, determining a ray passing through the center point; and When it is determined that the number of intersections between the ray and the multiple edges of the second area to be identified is an odd number, an intersection area between the first and second areas to be identified is identified.

3. The method according to claim 2, wherein: In the case where the ray passes through a vertex of the second area to be identified, identifying the intersection area between the first and second areas to be identified according to the positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified, further comprising: When it is determined that two adjacent edges of the vertex in the second region are located on the same side of the ray, the number of intersections between the ray and the edges of the second region to be identified is increased by one; and When it is determined that the number of intersections between the ray and the multiple edges of the second area to be identified is an odd number, an intersection area between the first and second areas to be identified is identified.

4. The method according to claim 1, wherein: The step of identifying the intersection area between the first and second to-be-identified areas according to the positional relationship between the updated first edge information set and the updated second edge information set includes: Respectively determining intersection points between straight lines where a plurality of edges in the updated first edge information set are located and straight lines where a plurality of edges in the updated second edge set are located; and When it is determined that the intersection points are all located on the edge, an intersection area between the first and second to-be-identified areas is identified.

5. The method according to claim 1, wherein: Determine the set of side information of the area to be identified in the image, including: Constructing a rectangular coordinate system with any point in the image as the origin; Obtaining coordinate information of multiple edges of the area to be identified; and An edge information set of the to-be-identified area is determined according to the coordinate information of the multiple edges.

6. The method according to claim 5, wherein: The deleting the location information of the target edge from the first and second edge information sets respectively includes: Obtaining coordinate information of the first and second minimum bounding boxes; Determine the position information of the target edge of the first edge information set according to the coordinate information of the multiple edges in the first edge information set and the coordinate information of the second minimum bounding box, and determine the position information of the target edge of the second edge information set according to the coordinate information of the multiple edges in the second edge information set and the coordinate information of the first minimum bounding box; and The location information of the target edge is deleted from the first and second edge information sets.

7. The method according to any one of claims 1 to 6, wherein: A plurality of edges of the area to be identified constitute a geometric figure.

8. An image recognition device, comprising: A first determination module is used to respectively determine a first minimum bounding box and a first edge information set of a first area to be identified in the image and a second minimum bounding box and a second edge information set of a second area to be identified, wherein the first and second edge information sets respectively include position information of multiple edges of the first and second areas to be identified; a deleting module, configured to delete the position information of the target edge from the first and second edge information sets respectively, the target edge including the edge located outside the second minimum circumscribed frame among the multiple edges of the first to-be-identified area, and the edge located outside the first minimum circumscribed frame among the multiple edges of the second to-be-identified area, to obtain an updated first edge information set and an updated second edge information set; A second determination module is used to identify an intersection area between the first and second areas to be identified based on a positional relationship between any point in the area to be identified corresponding to the non-empty set and another area to be identified when it is determined that the updated first side information set or the updated second side information set is a non-empty set; as well as The third determination module is used to identify the intersection area between the first and second to-be-identified areas according to the positional relationship between the updated first side information set and the updated second side information set when it is determined that both the updated first side information set and the updated second side information set are non-empty sets.

9. An electronic device, comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 7.

Images