CN113778591B - Method, device, server and storage medium for acquiring display card surface - Google Patents

Abstract

The application discloses a method, a device, a server and a storage medium for acquiring a display card surface, and belongs to the field of data processing. The method comprises the following steps: acquiring a target picture, wherein the target picture is a picture of a card surface of a photographed entity card; extracting a first key point and a first descriptor vector of a target picture by utilizing a feature extraction algorithm; matching the target picture with the card surface sample picture in the card surface database based on the first descriptor vector and the card surface sample data in the card surface database to obtain matching similarity of the target picture and the card surface sample picture; selecting the first N card surface sample pictures as alternative card surface pictures according to the sequence of the matching similarity from high to low; and sending the alternative card face picture to the user terminal, so that the user terminal displays the alternative card face picture for the user to select so as to determine the display card face picture of the entity card in the user terminal. According to the embodiment of the application, the maintenance cost is reduced on the basis of providing the display card face picture required by the user terminal for displaying the entity card.

Description

Method, device, server and storage medium for acquiring display card surface

Technical Field

The application belongs to the field of data processing, and particularly relates to a method, a device, a server and a storage medium for acquiring a display card surface.

Background

To meet the user's needs for the physical card image, the card issuing entity issues some physical cards with personalized card faces. With the development of electronic information technology, a user binds an entity card by using an application program installed in a terminal device, and the bound entity card function is realized through the operation of the application program.

In order to improve the visibility of the entity card in the application program of the terminal device, the personalized card surface of the entity card bound by the user can be displayed under the condition that the application program of the terminal device runs. At present, a background server of an application program needs to use a card number of an entity card, a corresponding relation between the card number and a card surface number is utilized in a server of an issuing mechanism through a special query interface, a card surface number corresponding to the card number is determined, a card surface picture corresponding to the card surface number is determined according to the card surface number, and the card surface picture is provided for a user terminal, so that the user terminal can display the card surface picture.

However, the acquisition of the personalized card surface of the entity card can be realized only by depending on the card number and the service of the server of the card issuing mechanism, and the server of the card issuing mechanism is required to be maintained on the basis of maintaining the background server, so that the maintenance cost is high.

Disclosure of Invention

The embodiment of the application provides a method, a device, a server and a storage medium for acquiring a display card surface, which can reduce maintenance cost on the basis of providing a display card surface picture required by a user terminal for displaying an entity card.

In a first aspect, an embodiment of the present application provides a method for obtaining a display card surface, including: the server acquires a target picture, wherein the target picture is a picture of a card surface of a photographed entity card; the method comprises the steps that a server extracts a first key point and a first descriptor vector of a target picture by utilizing a feature extraction algorithm, wherein the first descriptor vector is a descriptor vector of the first key point; the server matches the target picture with the card face sample picture in the card face database based on the first descriptor vector and the card face sample data in the card face database pre-established in the server to obtain the matching similarity of the target picture and the card face sample picture, wherein the card face sample data is used for representing the card face sample picture; the server selects the first N card surface sample pictures as alternative card surface pictures according to the sequence of the matching similarity from high to low, wherein N is a positive integer; the server sends the candidate card face picture to the user terminal, so that the user terminal displays the candidate card face picture for the user to select so as to determine the display card face picture of the entity card in the user terminal.

In a second aspect, an embodiment of the present application provides a device for obtaining a display card surface, including: the acquisition module is used for acquiring a target picture, wherein the target picture is a picture of a card surface of a photographed entity card; the extraction module is used for extracting a first key point and a first descriptor vector of the target picture by utilizing a feature extraction algorithm, wherein the first descriptor vector is a descriptor vector of the first key point; the matching module is used for matching the target picture with the card face sample picture in the card face database based on the first descriptor vector and card face sample data in the card face database which is pre-established in the card face display device, so as to obtain the matching similarity of the target picture and the card face sample picture, wherein the card face sample data is used for representing the card face sample picture; the selection module is used for selecting the first N card surface sample pictures as alternative card surface pictures according to the sequence of the matching similarity from high to low, wherein N is a positive integer; the sending module is used for sending the candidate card face picture to the user terminal, so that the user terminal can display the candidate card face picture for the user to select so as to determine the display card face picture of the entity card on the user terminal.

In a third aspect, an embodiment of the present application provides a server, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the method of obtaining the presentation card surface of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of obtaining a presentation card surface of the first aspect.

The embodiment of the application provides a method, a device, a server and a storage medium for acquiring a display card surface, wherein the server extracts key points and descriptor vectors of a shot card surface picture of an entity card, and based on a first descriptor vector and card surface sample data in a preset card surface database, the shot card surface picture of the entity card is matched with the card surface sample pictures in the card surface database, N card surface sample pictures with highest matching similarity with the shot card surface picture of the entity card are used as candidate card surface pictures to be sent to a user terminal, so that the user terminal can determine the display card surface picture used by the entity card in the user terminal in the candidate card surface pictures. The server provides the display card face picture required by the display entity card for the user terminal through the image recognition technology, the acquisition of the display card face picture does not depend on the card number of the entity card and the service of the server of the card issuing mechanism any more, the server for image recognition is only required to be maintained, and the maintenance cost is reduced on the basis that the display card face picture required by the display entity card of the user terminal can be provided.

Drawings

In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are needed to be used in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.

Fig. 1 is a schematic diagram of an example of an application program interface displayed by a user terminal according to an embodiment of the present application;

fig. 2 is a schematic diagram of an example of an application scenario of a method for obtaining a presentation card surface according to an embodiment of the present application;

FIG. 3 is a flowchart of a method for obtaining a display card surface according to an embodiment of the present application;

FIG. 4 is a flowchart of another embodiment of a method for obtaining a display card surface according to the present application;

FIG. 5 is a flowchart of a method for obtaining a display card surface according to another embodiment of the present application;

FIG. 6 is a flowchart of a method for obtaining a display card surface according to another embodiment of the present application;

FIG. 7 is a schematic diagram of an example of a card surface of a physical card and a common element according to an embodiment of the present application;

FIG. 8 is a flowchart of a method for obtaining a display card surface according to still another embodiment of the present application;

FIG. 9 is a flowchart of a method for obtaining a display card surface according to still another embodiment of the present application;

FIG. 10 is a flowchart of a method for obtaining a display card surface according to still another embodiment of the present application;

FIG. 11 is a flowchart of an example of a process for creating a card database according to an embodiment of the present application;

FIG. 12 is a flowchart of an example of a process for obtaining a display card surface in a method for obtaining a display card surface according to an embodiment of the present application;

FIG. 13 is a schematic structural diagram of an embodiment of a device for obtaining a display card surface according to the present application;

FIG. 14 is a schematic view of another embodiment of a device for capturing a display card surface according to the present application;

FIG. 15 is a schematic view of a device for capturing a display card according to another embodiment of the present application;

FIG. 16 is a schematic view of a device for capturing a display card according to another embodiment of the present application;

FIG. 17 is a schematic view of a device for capturing a display card according to still another embodiment of the present application;

fig. 18 is a schematic structural diagram of an embodiment of a server according to the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.

To meet the user's needs for the physical card image, the card issuing entity issues some physical cards with personalized card faces. With the development of electronic information technology, a user binds an entity card by using an application program installed in a terminal device, and the bound entity card function is realized through the operation of the application program. In order to improve the visibility of the entity card in the application program of the terminal device, the personalized card surface of the entity card bound by the user can be displayed under the condition that the application program of the terminal device runs. Fig. 1 is a schematic diagram of an example of an application program interface displayed by a user terminal according to an embodiment of the present application. As shown in fig. 1, the application program interface displayed by the user terminal includes personalized card face pictures of two bank cards and two aviation service membership cards, so that the user can intuitively confirm the entity card corresponding to the personalized card face.

The user terminal obtains the personalized card face picture corresponding to the entity card, and the personalized card face picture can be displayed. The user terminal needs to initiate a request to a background server of the application. The background server sends the card number of the entity card corresponding to the personalized card surface requested by the user terminal to the server of the card issuing mechanism through a special query interface between the background server and the server of the card issuing mechanism. The server of the card issuing mechanism determines the card face number corresponding to the requested card number by utilizing the corresponding relation between the card number and the card face number, acquires a corresponding card face picture according to the card face number, and sends the card face picture to the user terminal through the background server so that the user terminal displays the card face picture. The function of the background server for acquiring the card face picture to be displayed by the user terminal is tightly coupled with the service provided by the server of the card issuing mechanism, namely, the acquisition of the personalized card face can be realized only by the card number and the service of the server of the card issuing mechanism, so that the system structure required by acquiring the personalized card face is more complex, the server of the card issuing mechanism is required to be maintained on the basis of maintaining the background server, and the maintenance cost is higher.

The application provides a method, a device, a server and a storage medium for acquiring a display card surface, which can utilize the shot picture of the card surface of a physical card to identify and obtain a card surface sample picture corresponding to the shot picture of the card surface of the physical card in a card surface database and provide the card surface sample picture for a user terminal so that the user terminal can display the card surface picture corresponding to the physical card.

The application scenario of the method for obtaining the display card surface provided by the embodiment of the application can relate to a user terminal and a server. Fig. 2 is a schematic diagram of an example of an application scenario of a method for obtaining a presentation card surface according to an embodiment of the present application. As shown in fig. 2, the user terminal 11 may interact with the server 12 to transfer a photographed picture of the card face of the physical card, a displayed card face picture shown at the user terminal 11, and so on.

The user terminal 11 may be installed with an application program that can be bound to the physical card for implementing the functions of the physical card. The kind of the physical card is not limited herein, and for example, the physical card may be a bank card, a membership card, or the like. The terminal 11 may specifically include a mobile phone, a computer, a tablet computer, and other devices with display functions, which are not limited herein.

The server 12 may be a background server of an application installed in the user terminal 11. The server 12 stores a card side database that may include card side sample pictures and card side sample data for the card side sample pictures. The card face sample picture and the card face sample data of the card face sample picture can be used for matching and identifying the display card face picture displayed by the user terminal 11.

The method, the device, the server and the storage medium for acquiring the display card surface provided by the embodiment of the application are sequentially described below.

The application provides a method for acquiring a display card surface, which can be applied to a server, namely the method for acquiring the display card surface can be executed by the server. Fig. 3 is a flowchart of a method for obtaining a display card surface according to an embodiment of the present application. As shown in fig. 3, the method for obtaining the display card surface may include steps S201 to S205.

In step S201, the server acquires a target picture.

The target picture is a picture of the card surface of the photographed entity card. In some examples, the server may obtain the target picture from the user terminal. Specifically, the user terminal may shoot the card surface of the physical card, obtain a shot picture of the card surface of the physical card, and send the picture, i.e. the target picture, to the server, so that the server obtains the target picture.

In step S202, the server extracts a first key point and a first descriptor vector of the target picture using a feature extraction algorithm.

The feature extraction algorithm is an algorithm that can extract key points and descriptor vectors of the key points in the picture, and is not limited herein. In some examples, feature extraction algorithms such as Scale-invariant feature transform (Scale-Invariant Feature Transform, SIFT) algorithms, accelerated robust features (Speeded Up Robust Features, SURF) algorithms, and the like may be utilized to extract key points and descriptor vectors of the target picture, but the feature extraction algorithms are not limited to SIFT algorithms and SURF algorithms.

The first key point is a characteristic point of the target picture. The first descriptor vector is a descriptor vector of the first key point and can be used for describing the first key point. In step S202, a plurality of first keypoints and a plurality of first descriptor vectors of the target picture may be extracted, where the first descriptor vectors may correspond to the first keypoints one by one.

In step S203, the server matches the target picture with the card surface sample picture in the card surface database based on the first descriptor vector and the card surface sample data in the card surface database pre-established in the server, so as to obtain a matching similarity between the target picture and the card surface sample picture.

The server stores a pre-established card database. The card face database may include card face sample pictures and card face sample data corresponding to the card face sample pictures. The card surface sample picture is a picture which is a sample of the physical card surface. The card surface sample data can be obtained according to the card surface sample picture and can be used for representing the card surface sample picture. In some examples, the card face sample data may include key points extracted from the card face sample picture and descriptor vectors of the key points. In other examples, the key points and the descriptor vectors of the key points extracted from the card surface sample picture may be processed, and the processed data may be used as card surface sample data.

Under the condition that the card surface database comprises more than two card surface sample pictures, the target pictures can be matched with the card surface sample pictures in the card surface database in sequence. In some examples, the matching of the target picture and the card face sample picture may be specifically implemented as matching of the first descriptor vector of the target picture and the card face sample data corresponding to the card face sample picture. In other examples, the matching between the target picture and the card surface sample picture may be specifically implemented as matching between the data obtained after the first descriptor vector of the target picture is processed and the card surface sample data corresponding to the card surface sample picture. The implementation of matching the target picture and the card surface sample picture may be determined according to the type of the card surface sample data, which is not limited herein.

The matching similarity of the target picture and the card surface sample picture is a parameter capable of reflecting the similarity degree of the target picture and the card surface sample picture. The higher the matching similarity between the target picture and the card face sample picture, the higher the probability that the card face of the physical card in the target picture is consistent with the card face sample picture. The lower the matching similarity between the target picture and the card face sample picture, the lower the probability that the card face of the physical card in the target picture is consistent with the card face sample picture.

In step S204, the server selects the first N card surface sample pictures as candidate card surface pictures according to the order of the matching similarity from high to low.

The alternative card face picture is a card face sample picture for providing to a user for selection. The first N card face sample pictures with the highest similarity degree with the card faces of the entity cards in the target picture are the N card face sample pictures with the highest similarity degree with the card faces of the entity cards in the target picture.

N is a positive integer. Specifically, N may be 1 or an integer of 2 or more, and is not limited thereto.

In step S205, the server sends the candidate card face picture to the user terminal, so that the user terminal displays the candidate card face picture for the user to select to determine the display card face picture of the entity card in the user terminal.

The alternative card face picture can be used for providing the user with selection to obtain the display card face picture of the entity card in the user terminal. The display card face picture is a card face picture consistent with the card face of the entity card in the target picture. And when the user terminal needs to display the entity card, the user terminal displays the display card face picture.

The possibility that the card face picture consistent with the card face of the entity card in the target picture is in the first N card face sample pictures with the high-low matching degree similarity is very high, but the situation that the card face picture consistent with the card face of the entity card in the target picture does not exist in the first N card face sample pictures with the high-low matching degree similarity is also existed, in order to avoid that the wrong card face picture is displayed on the user terminal, the selected alternative card face picture can be sent to the user terminal. The user terminal may display the N Zhang Bei selection surface picture for selection by the user.

In some examples, n=1, and the user terminal determines this Zhang Bei card plane picture as the presentation card plane picture of the entity card at the user terminal in response to a confirmation input from the user. Namely, the Zhang Bei card selecting face picture is a card face picture consistent with the card face of the entity card in the target picture.

In other examples, N is an integer greater than or equal to 2, and the user terminal determines, in response to a selection input of a user, an alternative card surface indicated by the selection input in the N Zhang Bei card surface picture as a presentation card surface picture of the entity card on the user terminal. Namely, the N Zhang Bei card selecting face picture comprises a card face picture consistent with the card face of the entity card in the target picture.

In still other examples, N is a positive integer, and the user terminal determines that the presentation card side picture of the entity card on the user terminal is not acquired in response to a negative input from the user. Namely, the N Zhang Bei card-selecting face picture does not have a card face picture consistent with the card face of the entity card in the target picture.

In the embodiment of the application, the server extracts key points and description sub-vectors of the photographed pictures of the card surface of the entity card, matches the photographed pictures of the card surface of the entity card with the card surface sample pictures in the card surface database based on the first description sub-vectors and the card surface sample data in the preset card surface database, and sends N card surface sample pictures with highest matching similarity with the photographed pictures of the card surface of the entity card to the user terminal as alternative card surface pictures, so that the user terminal can determine the display card surface pictures used by the entity card in the user terminal in the alternative card surface pictures. The server provides the display card face picture required by the display entity card for the user terminal through the image recognition technology, the acquisition of the display card face picture does not depend on the card number of the entity card and the service of the server of the card issuing mechanism any more, the server for image recognition is only required to be maintained, and the maintenance cost is reduced on the basis that the display card face picture required by the display entity card of the user terminal can be provided.

In some embodiments, the card face sample data may include keypoints and descriptor vectors extracted from the card face sample picture, and correspondingly, the server may utilize the first descriptor vector to match the card face sample data. Fig. 4 is a flowchart of another embodiment of a method for obtaining a display card surface according to the present application. Fig. 4 is different from fig. 3 in that step S203 in fig. 3 may be specifically thinned into steps S2031 to S2034 in fig. 4.

In step S2031, the server calculates euclidean distances between the first descriptor vector and the second descriptor vectors corresponding to the respective card surface sample pictures.

In some examples, the target picture has a plurality of first keypoints, each of which may correspond to one of the first descriptor vectors. Each card face sample picture can be provided with a plurality of second key points, and each second key point corresponds to one second descriptor vector. Specifically, the euclidean distance between each first descriptor vector and each second descriptor vector corresponding to each card surface sample picture can be calculated. The smaller the Euclidean distance between a first descriptor vector and a second descriptor vector, the higher the matching degree between the first key point corresponding to the first descriptor vector and the second key point corresponding to the second descriptor vector.

In step S2032, the server selects the first M second descriptor vectors in order of the euclidean distance from small to large.

M is a positive integer. Specifically, for each first descriptor vector, the first M second descriptor vectors are selected in order of decreasing euclidean distance from the first descriptor vector. The Euclidean distance between the second descriptor vector and the first descriptor vector is in the order from small to large, that is, the order from high to low of the matching degree of the second key point corresponding to the second descriptor vector and the first key point corresponding to the first descriptor vector. For example, the number of the first descriptor vectors is P, and M second descriptor vectors are selected for each first descriptor vector, and p×m second descriptor vectors may be selected for the P first descriptor vectors.

In step S2033, the server determines, according to the euclidean distance between the selected second descriptor vector and the first descriptor vector, whether the first key point corresponding to the first descriptor vector is matched with the second key point corresponding to the selected second descriptor vector.

For each first descriptor vector, whether a first key point corresponding to the first descriptor vector matches a second key point corresponding to a selected second descriptor vector can be determined according to the Euclidean distance between the first descriptor vector and the selected second descriptor vector.

In some examples, m=1, i.e. a second descriptor vector is selected for a first descriptor vector. And under the condition that the Euclidean distance between the selected second descriptor vector and the first descriptor vector is smaller than or equal to a matching distance threshold value, the server determines that the first key point corresponding to the first descriptor vector is matched with the second key point corresponding to the selected second descriptor vector.

The matching distance threshold is a judgment threshold for judging whether the first key point and the second key point match, and may be set according to a scene, a requirement, experience, or the like, and is not limited thereto. The Euclidean distance between the first descriptor vector and the second descriptor vector is smaller than a matching distance threshold value, which indicates that a first key point corresponding to the first descriptor vector is matched with a second key point corresponding to the second descriptor vector; the Euclidean distance between the first descriptor vector and the second descriptor vector is larger than a matching distance threshold value, which indicates that the first key point corresponding to the first descriptor vector is not matched with the second key point corresponding to the second descriptor vector.

In other examples, m=2, i.e. two second descriptor vectors are chosen for one first descriptor vector. The server calculates the Euclidean distance ratio of the first Euclidean distance to the second Euclidean distance, and determines that a first key point corresponding to the first descriptor vector is matched with a second key point corresponding to the acquired first and second descriptor vectors under the condition that the Euclidean distance ratio is smaller than or equal to a matching ratio threshold value. The first Euclidean distance is the Euclidean distance between the selected first second descriptor vector and the first descriptor vector, and the second Euclidean distance is the Euclidean distance between the selected second descriptor vector and the first descriptor vector. For example, for the first descriptor vector a1, the second descriptor vector b1 and the second descriptor vector b2 are selected in order of decreasing euclidean distance from the first descriptor vector a1, where the euclidean distance between the first descriptor vector a1 and the second descriptor vector b1 is smaller than the euclidean distance between the first descriptor vector a1 and the second descriptor vector b2, where the euclidean distance between the first descriptor vector a1 and the second descriptor vector b1 is the first euclidean distance, and the euclidean distance between the first descriptor vector a1 and the second descriptor vector b2 is the second euclidean distance.

The matching ratio threshold is a determination threshold for determining whether the second key point has uniqueness to be able to match the first key point, and may be set according to a scene, a requirement, experience, or the like, but is not limited thereto. For example, the match ratio threshold is set to 0.5. The Euclidean distance ratio is the ratio of the first Euclidean distance to the second Euclidean distance. The Euclidean distance ratio corresponding to a first descriptor vector can represent the difference between a selected second descriptor vector closest to the first descriptor vector and a second descriptor vector second closest to the first descriptor vector. The Euclidean distance ratio is smaller than or equal to a matching ratio threshold, which indicates that the difference between a second descriptor vector closest to the first descriptor vector and a second descriptor vector second closest to the first descriptor vector is large, namely, a second key point corresponding to the second descriptor vector closest to the first descriptor vector has uniqueness, and can be considered to be matched with a first key point corresponding to the first descriptor vector; the euclidean distance ratio is greater than the match ratio threshold, indicating that the difference between the second descriptor vector closest to the first descriptor vector and the second descriptor vector second closest to the first descriptor vector is small, i.e., the second keypoint corresponding to the second descriptor vector closest to the first descriptor vector is not unique and cannot be considered as a match to the first keypoint corresponding to the first descriptor vector.

In step S2034, the server counts the number of matches of the second key points matched with the first key points in the sample pictures of the card surface, and obtains the matching similarity between the target picture and the sample pictures of the card surface according to the number of matches.

The matching quantity of the second key points matched with the first key points in one card surface sample picture can reflect the matching similarity of the card surface sample picture and the target picture. The number of matches is positively correlated with the matching similarity, i.e., the greater the number of matches, the higher the matching similarity; the smaller the number of matches, the lower the matching similarity. In the embodiment of the application, the matching number can be directly used as the matching similarity, and the parameter calculated according to the matching number can be used as the matching similarity, which is not limited herein.

In some embodiments, the card surface sample data may include a multi-dimensional vector of the card surface sample picture, and correspondingly, the server may process the first descriptor vector to obtain a multi-dimensional vector of the target picture, and match the card surface sample data with the multi-dimensional vector of the target picture. Fig. 5 is a flowchart of a method for obtaining a display card surface according to another embodiment of the present application. Fig. 5 is different from fig. 3 in that step S203 in fig. 3 may be specifically refined to steps S2035 to S2037.

In step S2035, the server obtains a second multidimensional vector of the target picture according to the first descriptor vector using a vector encoding algorithm.

The vector encoding algorithm is an algorithm for encoding the descriptor vector of the picture to obtain a multidimensional vector corresponding to the picture, and is not limited herein. In some examples, the Vector encoding algorithm may include, but is not limited to, a local aggregate Vector (Vector of Locally Aggregated Descriptors, VLAD) algorithm, a Fisher Vector (FV) algorithm, and the like.

Specifically, a plurality of first descriptor vectors of the target picture are processed by using a vector coding algorithm to obtain a second multidimensional vector. The second multi-dimensional vector is a multi-dimensional vector of the target picture. For a target picture, the target picture may correspond to a plurality of first descriptor vectors and a second multi-dimensional vector. And the first descriptor vectors of the target picture are coded into a second multidimensional vector through vectors, so that the calculation efficiency of matching the follow-up target picture with the card sample picture can be improved. Under the condition that the card surface sample data in the card surface database comprises the first multidimensional limit of the card surface sample picture, the first descriptor vector of the target picture is subjected to vector coding, so that the total searching efficiency of the target picture in the card surface database can be improved.

In order to facilitate the participation of the second multidimensional vector in the subsequent Euclidean distance calculation, the second multidimensional vector may be subjected to normalization processing, and the second multidimensional vector after normalization processing is utilized to participate in the subsequent Euclidean distance calculation. The normalization algorithm is not limited herein, and for example, the normalization may be performed using an L2 normalization algorithm. The normalization process can unify the measurement scale of the multidimensional vector, such as the vector length, so as to improve the efficiency of the subsequent Euclidean distance calculation.

In step S2036, the server calculates a target euclidean distance corresponding to each card surface sample picture.

The card face sample data includes a first multi-dimensional vector of card face sample pictures. The first multi-dimensional vector is the multi-dimensional vector of the card face sample picture. The first multidimensional vector is obtained according to the second key point of the card surface sample picture and the second descriptor vector. The second key point is the key point of the card face sample picture. The second descriptor vector is a descriptor vector of the second keypoint. The first multidimensional vectors are in one-to-one correspondence with the card surface sample pictures, namely one card surface sample picture corresponds to one first multidimensional vector.

The target Euclidean distance is the Euclidean distance of the second multidimensional vector and the first multidimensional vector corresponding to the card surface sample picture.

In step S2037, the server obtains the matching similarity between the target picture and the card surface sample picture according to the target euclidean distance.

The matching similarity of the target picture and the card surface sample picture can be embodied by the target Euclidean distance. The target Euclidean distance is inversely related to the matching similarity, namely, the larger the target Euclidean distance between the second multi-dimensional vector and the first multi-dimensional vector is, the lower the matching similarity between the target picture corresponding to the second multi-dimensional vector and the card surface sample picture corresponding to the first multi-dimensional vector is; the smaller the target Euclidean distance between the second multidimensional vector and the first multidimensional vector is, the higher the matching similarity between the target picture corresponding to the second multidimensional vector and the card surface sample picture corresponding to the first multidimensional vector is. In the embodiment of the present application, the reciprocal of the target euclidean distance may be used as the matching similarity, or other parameters calculated according to the target euclidean distance may be used as the matching similarity, which is not limited herein.

In some embodiments, the target picture may be further processed before the first keypoint and the first descriptor vector of the target picture are extracted, so that the extracted first keypoint and first descriptor vector can be more accurate. The extracted first key point and the first descriptor vector can also be filtered, and invalid data which is easy to influence the matching similarity can be filtered out, so that the accuracy of the matching similarity can be improved. Fig. 6 is a flowchart of a method for obtaining a display card surface according to another embodiment of the present application. Fig. 6 is different from fig. 3 in that the method for obtaining the display card surface shown in fig. 6 may further include steps S206 to S210.

In step S206, the server scales the target picture to a predetermined size using an image scaling algorithm.

The size of the target picture may be different due to the different sources of the target picture. For ease of processing, the target picture may be scaled to a uniform size. The image scaling algorithm may include, but is not limited to, an area interpolation algorithm. The predetermined size may be set according to a scene, a demand, experience, etc., and is not limited herein. For example, the predetermined size may be 170×107 pixels. The accuracy of the matching similarity is further improved by scaling the target picture to a uniform size.

In step S207, the server identifies the card surface boundary of the physical card in the target picture using a boundary identification algorithm.

The target picture is a picture of the card surface of the photographed entity card. Although the card face of the physical card can be framed through a viewfinder in the shooting process of the user terminal, it is possible for the target picture to include contents other than the card face of the physical card. And the boundary recognition algorithm is utilized to recognize the boundary of the card surface of the entity card in the target picture, so that noise information interfering with the matching of the target picture and the card surface sample picture can be reduced.

The boundary recognition algorithm is an algorithm capable of recognizing the edges of the pattern in the picture, and is not limited thereto. In some examples, the boundary recognition algorithm may include a Canny algorithm, a Hough-to-Hough detection algorithm, and the like, without limitation.

In step S208, the server determines the area surrounded by the card surface boundary as the effective area of the target picture.

The effective area is used for extracting the first key point and the first descriptor vector, namely the first key point and the first descriptor vector are extracted from the effective area, and noise information outside the effective area is eliminated. For example, an edge portion in the target picture can be identified through a Canny algorithm, then an Hough straight line detection algorithm is used for extracting edge straight lines belonging to a card surface of the entity card, the edge straight lines are intersected, and a rectangular area formed by minimum values and maximum values of horizontal coordinates and vertical coordinates in the intersection point of the edge straight lines is obtained, wherein the rectangular area is an effective area.

By determining the effective area and eliminating noise information in the target picture, the accuracy of the matching similarity can be further improved, and therefore the accuracy of the alternative card face picture provided for the user is improved.

In step S209, the server matches the first descriptor vector with a preset third descriptor vector to obtain a first descriptor vector matched with the third descriptor vector.

The third descriptor vector is a descriptor vector of the third keypoint. The third keypoint is a keypoint of the common element. The third keypoint and the third descriptor vector may be extracted from the common element using a feature extraction algorithm. The common element is the same pattern in the card face of the physical card, and is not limited herein. For example, the physical card comprises a bank card, and the card faces of the bank card have the same card organization identification pattern, so that the card organization identification pattern can be used as a public element; the card surface of the bank card also has the same chip pattern, and the chip pattern can also be used as a common element.

The matching of the first descriptor vector and the third descriptor vector may be referred to as the matching of the first descriptor vector and the second descriptor vector in the above embodiment, which is not described herein.

The first descriptor vector matched with the third descriptor vector is the first descriptor vector with similarity exceeding a certain degree, and the first key point corresponding to the first descriptor vector matched with the third descriptor vector is the key point of the common element. Because the card surfaces of the entity cards are provided with the common elements, the common elements can influence the matching of the target picture and the card surface sample picture, and the accuracy of the matching similarity is reduced.

In step S210, the server filters out, from the first descriptor vector, the first descriptor vector that matches the third descriptor vector.

In order to avoid the influence of the common element on the matching of the card surface of the physical card and the card surface sample picture, the first descriptor vector which can influence the matching of the target picture and the card surface sample picture can be filtered, and the first descriptor vector which can influence the matching of the target picture and the card surface sample picture is the first descriptor vector matched with the third descriptor vector. The first descriptor vector matched with the third descriptor vector is filtered, namely the first descriptor vector remained after the first descriptor vector matched with the third descriptor vector is filtered to participate in the matching of the subsequent target picture and the card surface sample picture, so that the influence of the common element on the matching of the target picture and the card surface sample picture is avoided, and the accuracy of the matching similarity is improved.

For example, fig. 7 is a schematic diagram of an example of a card surface of a physical card and a common element according to an embodiment of the present application. As shown in fig. 7, the left side is a common element, specifically, a card organization identification pattern, and the right side is a picture of the card surface of the physical card, which has the card organization identification pattern. The common element corresponds to the card organization identification pattern on the picture of the card face of the entity card, the circle on the common element is a third key point, the circle on the card organization identification pattern on the picture of the card face of the entity card is a first key point matched with the third key point, and the connection line between the circles represents the matching relation between the third key point and the first key point.

In the process of executing the method for acquiring the display card surface, one or more than two sets of the steps S206, S207, S208, S209, and S210 may be selected for execution, which is not limited herein.

In some embodiments, after the matching of the target picture and the card surface sample picture is completed, the target picture may be stored in the card surface database as the card surface sample picture, and the card surface data of the target picture may be stored in the card surface database as the card surface sample data, so as to perfect the rich card surface database. Fig. 8 is a flowchart of a method for obtaining a display card surface according to still another embodiment of the present application. Fig. 8 is different from fig. 3 in that the method for obtaining the display card surface shown in fig. 8 may further include steps S211 to S213, and step S214.

In step S211, the server obtains a slave card sample picture based on the target picture.

The sample picture from the card surface is a picture of the card surface including the physical card after desensitization treatment. The desensitization processing here refers to eliminating user sensitive information in the target picture, for example, the card number, the card validity period, etc. in the picture of the card face of the physical card can be eliminated.

In some examples, after desensitizing the target picture, the card face of the physical card in the target picture may also be identified, a portion of the card face of the physical card in the target picture may be retained, and scaled to a predetermined size, resulting in a sample picture from the card face. That is, the sample picture from the card surface may be a picture of the card surface including the physical card after the desensitization process, the boundary recognition process, and the scaling process, which is not limited herein.

In step S212, the server obtains card surface sample data corresponding to the card surface sample picture according to the first key point and the first descriptor vector corresponding to the card surface sample picture.

The first key point corresponding to the card surface sample picture is the first key point of the target picture, and the first descriptor vector corresponding to the card surface sample picture is the first descriptor vector of the target picture.

The type of the card face sample data corresponding to the card face sample picture can be consistent with the type of the card face sample data in the card face database. In some examples, the card face sample data corresponding to the card face sample picture may include a first keypoint and a first descriptor vector corresponding to the card face sample picture. In other examples, the card face sample data corresponding to the card face sample picture may include the second multi-dimensional vector of the target picture in the above embodiments.

In step S213, the server stores the slave card face sample picture as a card face sample picture in the card face database, and stores the card face sample data corresponding to the slave card face sample picture in the card face database.

The card face sample picture corresponding to the target picture and serving as the display card face picture is a main card face sample picture corresponding to the slave card face sample picture. The slave card face sample picture and the card face sample data of the slave card face sample picture in the card face database are used for matching with the target picture, the master card face sample picture and the card face sample data of the master card face sample picture are used for matching with the target picture, and the master card face sample picture can also be used as an alternative card face picture.

In step S214, if the first N selected card surface sample pictures include the slave card surface sample pictures, the server replaces the slave card surface sample picture in the first N selected card surface sample pictures with the corresponding master card surface sample picture.

If the first N card surface sample pictures selected according to the order of the matching similarity from high to low include the slave card surface sample picture, the master open surface sample picture corresponding to the slave card surface sample picture may be used to replace the slave card surface sample picture as the candidate card surface picture.

The matching is participated in from the card surface sample picture, so that the matching success rate can be improved, namely the probability of identifying the card surface sample picture corresponding to the target picture can be improved, and the data in the card surface database is expanded and enriched. The main card face sample picture is a standard card face sample picture, the auxiliary card face sample picture is obtained from the picture of the card face of the photographed entity card, the definition and the aesthetic degree are lower than those of the main card face sample picture, namely, the main card face sample picture is more suitable for being displayed on a user terminal than the auxiliary card face sample picture, the main card face sample picture is utilized to replace the auxiliary card face sample picture as an alternative card face picture, the definition and the aesthetic degree of displaying the card face picture on the user terminal can be improved, the user can conveniently execute card selecting operation on the user terminal, and the user experience is improved.

In some embodiments, the creation of the card face database may be foreseen in the server. Fig. 9 is a flowchart of a method for obtaining a display card surface according to still another embodiment of the present application. Fig. 9 is different from fig. 3 in that the method for obtaining the display card surface shown in fig. 9 may further include steps S215 to S218.

In step S215, the server enters a card face sample picture.

The recording of the card surface sample picture can be manually recorded, and can also be imported from equipment such as a server of a card issuing mechanism through an interface, and the recording mode of the card surface sample picture is not limited.

In step S216, the server extracts the second key point and the second descriptor vector of the card surface sample picture by using the feature extraction algorithm.

The second key point is the key point of the card face sample picture. The second descriptor vector is a descriptor vector of the second keypoint. In some examples, one card face sample picture has a plurality of second keypoints, and correspondingly, one second keypoint corresponds to one second descriptor vector, i.e., one card face sample picture has a plurality of second descriptor vectors.

The feature extraction algorithm is the same as the feature extraction algorithm in step S202 in the above embodiment, and the specific content of the feature extraction algorithm, the second key point of the card surface sample picture, and the second descriptor vector extraction may be referred to the related description of the feature extraction algorithm, the first key point, and the first descriptor vector extraction in the above embodiment, which are not described herein again.

In some examples, before step S216 is performed, the second descriptor vector may be further matched with a preset third descriptor vector, so as to obtain a second descriptor vector matched with the third descriptor vector, and in the second descriptor vector, the second descriptor vector matched with the third descriptor vector is filtered out. The third descriptor vector is a descriptor vector of the third keypoint. The third keypoint is a keypoint of the common element. The common elements are the same patterns in at least part of the card face sample pictures. Details of the common elements may be referred to in the above embodiments, and will not be described herein.

The content of the matching of the second descriptor vector and the third descriptor vector can be referred to in the above embodiment for the related description of the matching of the first descriptor vector and the third descriptor vector, which is not repeated herein. In the second descriptor vector, the second descriptor vector matched with the third descriptor vector is filtered out, so that data related to the common element is filtered out from the card face sample data, interference of the common element on matching of the card face sample picture of the target picture is avoided, and accuracy of the obtained matching similarity is improved.

In step S217, the server obtains card surface sample data according to the second key point and the second descriptor vector.

In some examples, the card face sample data includes a second keypoint and a second descriptor vector in the event that the number of card face sample pictures is less than or equal to a complexity number threshold. The complexity threshold is a number threshold for determining whether the number of card surface sample pictures in the card surface database to be built reaches a complexity level, and may be set according to a scene, a requirement, experience, and the like, and is not limited herein. The number of the card surface sample pictures in the card surface database to be established is smaller than or equal to the complex number threshold, which means that the number of the card surface sample pictures in the card surface database to be established is smaller, the number of the corresponding second key points and the second descriptor vectors is also smaller, and the card surface database can directly store the second key points and the second descriptor vectors. In particular, the second keypoint and the second descriptor vector may be stored in a list manner in the established card face database.

In other examples, when the number of card surface sample pictures is greater than the complex number threshold, a vector encoding algorithm is utilized to obtain a first multi-dimensional vector of each card surface sample picture according to a second key point and a second descriptor vector of each card surface sample picture, and the first multi-dimensional vector of each card surface sample picture is determined to be card surface sample data. The number of the card surface sample pictures is larger than the complex number threshold, which means that the number of the card surface sample pictures in the card surface database to be established is larger, and the number of the corresponding second key points and the second descriptor vectors are also larger.

In the case that the card face sample data includes the first multi-dimensional vector, the card face sample data may also be stored in the card face database in an index structure. The index structure may be constructed using an index construction algorithm and a first multi-dimensional vector. In some examples, the index construction algorithm may include, but is not limited to, a ball-tree algorithm, a locality sensitive hashing algorithm, a Hierarchical Navigable Small World algorithm, i.e., HNSW algorithm, and the like.

In step S218, the server creates a card surface database based on the card surface sample picture and the card surface sample data.

The card face database comprises a card face sample picture and card face sample data.

In some embodiments, the card side database may include more than two sub-databases. The sub-databases may be partitioned according to card issuers to which the card face sample pictures belong. The card face sample pictures in one sub-database belong to the same card issuing organization. Fig. 10 is a flowchart of a method for obtaining a display card surface according to still another embodiment of the present application. Fig. 10 is different from fig. 3 in that the method for acquiring the display card surface shown in fig. 10 may further include step S219, and step S203 in fig. 3 may be specifically subdivided into step S2038 and step S2039 in fig. 10.

In step S219, the server acquires binding information of the physical card in the target picture.

The server is a background server of the application program, and the binding card information of the entity card is stored in the server. The binding card information includes a first identification characterizing a card issuer of the physical card. The first identifier may be used to indicate the card issuer, for example, the first identifier may include a card issuer number, etc., and is not limited herein. The binding card information may also include other information, which is not limited herein.

In step S2038, the server determines a target sub-database according to the first identification.

The target sub-database is a sub-database corresponding to the card issuing organization indicated by the first identifier. The sub-databases in the card face database are divided according to the card issuing organization, and the sub-databases corresponding to the card issuing organization to which the entity card belongs can be determined according to the first identification.

In step S2039, the server matches the target picture with the card surface sample picture in the target sub-database based on the first description sub-vector and the card surface sample data in the target sub-database, to obtain a matching similarity between the target picture and the card surface sample picture.

The matching is carried out by directly utilizing the card surface sample in the target sub-database and the target picture, so that the matching range can be reduced, and the matching speed and the matching accuracy can be improved.

Based on the first descriptor vector and the card surface sample data in the target sub-database, matching the target picture with the card surface sample picture in the target sub-database to obtain the matching similarity between the target picture and the card surface sample picture, which is described in the above embodiment, and will not be described herein.

For ease of understanding, a flow of the server establishing the card side database in the method of acquiring the presentation card side will be described below with an example. Fig. 11 is a flowchart of an example of a process of creating a card database according to an embodiment of the present application. As shown in fig. 11, the process of creating the card surface database may include steps S301 to S305.

In step S301, the server receives an entered card face sample picture.

In step S302, the server extracts a second key point and a second descriptor vector of each card surface sample picture.

In step S303, if the number of the card surface sample pictures is less than or equal to the complex number threshold, the server uses the second key point and the second descriptor vector of each card surface sample picture as the card surface sample data.

In step S304, if the number of the card surface sample pictures is greater than the complex number threshold, the server encodes the second descriptor vector of each card surface sample picture to obtain a first multi-dimensional vector of each card surface sample picture as card surface sample data, and constructs an index structure of the first multi-dimensional vector by using the first multi-dimensional vector of each card surface sample picture.

In step S305, the server creates a card face database based on the card face sample picture and the card face sample data.

The details of the steps S301 to S305 can be referred to the description of the embodiments, and are not repeated here.

For ease of understanding, the process of acquiring the presentation card surface between the user terminal and the server is described below as an example. Fig. 12 is a flowchart of an example of a process of obtaining a presentation card surface in the method of obtaining a presentation card surface according to the embodiment of the present application. As shown in fig. 12, the process of obtaining the display card surface may include steps S401 to S410.

In step S401, the user terminal photographs the card surface of the physical card to obtain the target picture.

In step S402, the user terminal uploads the target picture to the server.

In step S403, the server extracts a first keypoint and a first descriptor vector from the target picture.

In step S404, if the number of the card surface sample pictures in the card surface database is less than or equal to the complex number threshold, the server matches the target picture with the card surface sample pictures in the card surface database by using the first descriptor vector and the second descriptor vector in the card surface database, so as to obtain a matching similarity.

In step S405, if the number of card surface sample pictures in the card surface database is greater than the complex number threshold, the server obtains a second multi-dimensional vector of each card surface sample picture according to the first descriptor vector of each card surface sample picture, and matches the target picture with the card surface sample picture in the card surface database by using the second multi-dimensional vector and the first multi-dimensional vector in the card surface database to obtain a matching similarity.

In step S406, the server selects the first N card surface sample pictures as candidate card surface pictures according to the order of the matching similarity from high to low.

In step S407, the server transmits the alternative card face picture to the user terminal.

In step S408, the user terminal displays the alternative card face picture.

In step S409, the user terminal receives an operation input of the user.

In step S410, the user terminal responds to the operation input, and selects the alternative card face picture indicated by the operation input as the display card face picture of the entity card in the user terminal.

The details of the steps S401 to S410 can be referred to the description of the embodiments, and are not repeated here.

The application provides a device for acquiring a display card surface. Fig. 13 is a schematic structural diagram of an embodiment of a device for acquiring a display card surface according to the present application. As shown in fig. 13, the apparatus 500 for acquiring a display card surface may include a first acquisition module 501, an extraction module 502, a matching module 503, a selection module 504, and a sending module 505.

The first acquisition module 501 may be used to acquire a target picture.

The target picture is a picture of the card surface of the photographed entity card.

The extraction module 502 may be configured to extract a first keypoint and a first descriptor vector of the target picture using a feature extraction algorithm.

The first descriptor vector is a descriptor vector of the first keypoint.

The matching module 503 may be configured to match the target picture with the card surface sample picture in the card surface database based on the first descriptor vector and the card surface sample data in the card surface database pre-established in the device for displaying the card surface, so as to obtain a matching similarity between the target picture and the card surface sample picture.

The card face sample data is used for representing card face sample pictures.

The selection module 504 may be configured to select the first N card surface sample pictures as the candidate card surface pictures according to the order of the matching similarity from high to low.

N is a positive integer.

The sending module 505 may be configured to send the candidate card face picture to the user terminal, so that the user terminal displays the candidate card face picture for the user to select to determine the display card face picture of the entity card in the user terminal.

In the embodiment of the application, the device for acquiring the display card surface extracts key points and description sub-vectors of the photographed card surface picture of the physical card, the photographed card surface picture of the physical card is matched with the card surface sample picture in the card surface database based on the first description sub-vector and the card surface sample data in the preset card surface database, N card surface sample pictures with highest matching similarity with the photographed card surface picture of the physical card are used as candidate card surface pictures to be sent to the user terminal, so that the user terminal can determine the display card surface picture used by the physical card in the user terminal in the candidate card surface pictures. The device for acquiring the display card surface provides the display card surface picture required by the display entity card for the user terminal through the image recognition technology, the acquisition of the display card surface picture does not depend on the card number of the entity card and the service of the server of the card issuing mechanism any more, only the device for acquiring the display card surface for image recognition is required to be maintained, and the maintenance cost is reduced on the basis of being capable of providing the display card surface picture required by the display entity card for the user terminal.

In some examples, the set of card face sample data includes a second keypoint of the one card face sample picture and a second descriptor vector, the second descriptor vector being a descriptor vector of the second keypoint.

The matching module 503 may be configured to: calculating Euclidean distance between the first descriptor vector and a second descriptor vector corresponding to each card surface sample picture; selecting first M second descriptor vectors according to the sequence from small to large of Euclidean distance, wherein M is a positive integer; determining whether a first key point corresponding to the first descriptor vector is matched with a second key point corresponding to the selected second descriptor vector according to the Euclidean distance between the selected second descriptor vector and the first descriptor vector; and counting the matching quantity of second key points matched with the first key points in each card surface sample picture, and obtaining the matching similarity of the target picture and the card surface sample picture according to the matching quantity, wherein the matching quantity and the matching similarity are positively correlated.

Specifically, the matching module 503 may be configured to: and under the condition that the Euclidean distance between the selected one second descriptor vector and the first descriptor vector is smaller than or equal to a matching distance threshold value, determining that a first key point corresponding to the first descriptor vector is matched with a second key point corresponding to the selected one second descriptor vector, wherein M=1.

Alternatively, the matching module 503 may be configured to: calculating the Euclidean distance ratio of the first Euclidean distance to the second Euclidean distance, wherein the first Euclidean distance is the Euclidean distance between the selected first second descriptor vector and the first descriptor vector, the second Euclidean distance is the Euclidean distance between the selected second descriptor vector and the first descriptor vector, and M=2; and under the condition that the Euclidean distance ratio is smaller than or equal to a matching ratio threshold, determining that a first key point corresponding to the first descriptor vector is matched with a second key point corresponding to the acquired first and second descriptor vectors.

In other examples, the card face sample data includes a first multi-dimensional vector of the card face sample picture, the first multi-dimensional vector being derived from a second keypoint of the card face sample picture and a second descriptor vector, the second descriptor vector being a descriptor vector of the second keypoint.

The matching module 503 may be configured to: obtaining a second multidimensional vector of the target picture according to the first descriptor vector by using a vector coding algorithm; calculating a target Euclidean distance corresponding to each card surface sample picture, wherein the target Euclidean distance is the Euclidean distance between the second multidimensional vector and the first multidimensional vector corresponding to the card surface sample picture; and obtaining the matching similarity of the target picture and the card surface sample picture according to the target Euclidean distance, wherein the target Euclidean distance is in negative correlation with the matching similarity.

Fig. 14 is a schematic structural diagram of another embodiment of a device for acquiring a display card surface according to the present application. Fig. 14 differs from fig. 13 in that the apparatus 500 for acquiring a presentation card surface shown in fig. 14 may further include an interference filtering module 506, a scaling module 507, and a boundary dividing module 508.

The interference filtering module 506 may be configured to: matching the first descriptor vector with a preset third descriptor vector to obtain a first descriptor vector matched with the third descriptor vector, wherein the third descriptor vector is a descriptor vector of a third key point, the third key point is a key point of a common element, and the common element is the same pattern in a card surface of the entity card; and filtering the first descriptor vector matched with the third descriptor vector in the first descriptor vector.

The scaling module 507 may be used to scale the target picture to a predetermined size using an image scaling algorithm.

The boundary partitioning module 508 may be used to: identifying the boundary of the card surface of the entity card in the target picture by utilizing a boundary identification algorithm; and determining an area surrounded by the boundary of the clamping surface as an effective area of the target picture, wherein the effective area is used for extracting the first key point and the first descriptor vector.

Fig. 15 is a schematic structural diagram of another embodiment of a device for acquiring a display card surface according to the present application. Fig. 15 differs from fig. 13 in that the apparatus 500 for acquiring a presentation card surface shown in fig. 15 may further include a first processing module 509, a storage module 510, and a replacement module 511.

The first processing module 509 may be configured to: obtaining a card surface sample picture based on the target picture, wherein the card surface sample picture is a picture of a card surface which comprises an entity card after desensitization treatment; and obtaining card surface sample data corresponding to the card surface sample picture according to the first key point and the first descriptor vector corresponding to the card surface sample picture.

The storage module 510 may be used to: and storing the slave card face sample picture as a card face sample picture into a card face database, and storing card face sample data corresponding to the slave card face sample picture into the card face database, wherein the card face sample picture corresponding to the target picture as a display card face picture is a master card face sample picture corresponding to the slave card face sample picture.

The replacement module 511 may be configured to: and under the condition that the first N selected card surface sample pictures comprise the slave card surface sample pictures, replacing the slave card surface sample pictures in the first N selected card surface sample pictures with the corresponding master card surface sample pictures.

Fig. 16 is a schematic structural view of another embodiment of a device for acquiring a display card surface according to the present application. Fig. 16 differs from fig. 13 in that the apparatus 500 for acquiring a presentation card side shown in fig. 16 may further include an entry module 512, a second processing module 513, and a database module 514.

The entry module 512 may be used to enter the card face sample pictures.

The extracting module 502 may be configured to extract the second key point and the second descriptor vector of the card surface sample picture by using a feature extracting algorithm.

The second descriptor vector is a descriptor vector of the second keypoint.

The second processing module 513 may be configured to obtain card surface sample data according to the second keypoint and the second descriptor vector.

Database module 514 may be configured to build a card side database based on the card side sample pictures and the card side sample data.

In some examples, the card face sample data includes a second keypoint and a second descriptor vector in the event that the number of card face sample pictures is less than or equal to a complexity number threshold.

In other examples, the second processing module 513 may be configured to: under the condition that the number of the card surface sample pictures is larger than a complex number threshold value, a vector coding algorithm is utilized to obtain a first multidimensional vector of each card surface sample picture according to a second key point and a second descriptor vector of each card surface sample picture; and determining the first multidimensional vector of the card surface sample picture as card surface sample data.

Specifically, the card face sample data is stored in the card face database in an index structure.

In some embodiments, the interference filtering module 506 may be further configured to: matching the second descriptor vector with a preset third descriptor vector to obtain a second descriptor vector matched with the third descriptor vector, wherein the third descriptor vector is a descriptor vector of a third key point, the third key point is a key point of a common element, and the common element is at least partially the same pattern in the card face sample picture; and filtering out the second descriptor vector matched with the third descriptor vector in the second descriptor vector.

In some embodiments, the card face database includes more than two sub-databases, the card face sample pictures in one sub-database belonging to the same card issuer. Fig. 17 is a schematic structural view of a device for obtaining a display card surface according to still another embodiment of the present application. Fig. 17 differs from fig. 13 in that the apparatus 500 for capturing a presentation card surface shown in fig. 17 may further include a second capturing module 515.

The second obtaining module 515 may be configured to obtain binding information of the physical card in the target picture, where the binding information includes a first identifier of a card issuing mechanism that characterizes the physical card.

The matching module 503 may be configured to: determining a target sub-database according to the first identifier, wherein the target sub-database is a sub-database corresponding to the card issuing institution indicated by the first identifier; and matching the target picture with the card surface sample picture in the target sub-database based on the first description sub-vector and the card surface sample data in the target sub-database to obtain the matching similarity of the target picture and the card surface sample picture.

The application also provides a server. Fig. 18 is a schematic structural diagram of an embodiment of a server according to the present application. As shown in fig. 18, the server 600 includes a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.

In one example, the processor 602 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.

The Memory 601 may include Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic disk storage media devices, optical storage media devices, flash Memory devices, electrical, optical, or other physical/tangible Memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to a method of acquiring a presentation card face in accordance with embodiments of the application.

The processor 602 executes a computer program corresponding to the executable program code by reading the executable program code stored in the memory 601 for implementing the method of acquiring a presentation card surface in the above-described embodiment.

In one example, server 600 may also include a communication interface 603 and a bus 604. As shown in fig. 18, the memory 601, the processor 602, and the communication interface 603 are connected to each other via a bus 604 and perform communication with each other.

The communication interface 603 is mainly used for implementing communication between each module, apparatus, unit and/or device in the embodiment of the present application. Input devices and/or output devices may also be accessed through the communication interface 603.

Bus 604 includes hardware, software, or both, coupling the components of server 600 to one another. By way of example, and not limitation, bus 604 may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics Bus, an enhanced industry standard architecture (Enhanced Industry Standard Architecture, EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industrial Standard Architecture, ISA) Bus, an Infiniband interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (Micro Channel Architecture, MCa) Bus, a peripheral component interconnect (Peripheral Component Interconnect, PCI) Bus, a PCI-Express (PCI-X) Bus, a serial advanced technology attachment (Serial Advanced Technology Attachment, SATA) Bus, a video electronics standards association local (Video Electronics Standards Association Local Bus, VLB) Bus, or other suitable Bus, or a combination of two or more of these. Bus 604 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.

The embodiment of the application also provides a computer readable storage medium, on which computer program instructions are stored, which when executed by a processor, can implement the method for acquiring the display card surface in the above embodiment, and can achieve the same technical effects, and in order to avoid repetition, the description is omitted here. The computer readable storage medium may include a non-transitory computer readable storage medium, such as Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, and the like, but is not limited thereto.

It should be understood that, in the present specification, each embodiment is described in an incremental manner, and the same or similar parts between the embodiments are all referred to each other, and each embodiment is mainly described in a different point from other embodiments. For apparatus embodiments, server embodiments, computer readable storage medium embodiments, the relevant points may be found in the description of method embodiments. The application is not limited to the specific steps and structures described above and shown in the drawings. Those skilled in the art will appreciate that various alterations, modifications, and additions may be made, or the order of steps may be altered, after appreciating the spirit of the present application. Also, a detailed description of known method techniques is omitted here for the sake of brevity.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the above-described embodiments are exemplary and not limiting. The different technical features presented in the different embodiments may be combined to advantage. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in view of the drawings, the description, and the claims. In the claims, the term "comprising" does not exclude other means or steps; the word "a" does not exclude a plurality; the terms "first," "second," and the like, are used for designating a name and not for indicating any particular order. Any reference signs in the claims shall not be construed as limiting the scope. The functions of the various elements presented in the claims may be implemented by means of a single hardware or software module. The presence of certain features in different dependent claims does not imply that these features cannot be combined to advantage.

Claims (18)

1. A method of obtaining a display card surface, comprising:

the method comprises the steps that a server obtains a target picture, wherein the target picture is a picture of a card surface of a photographed entity card;

the server extracts a first key point and a first descriptor vector of the target picture by utilizing a feature extraction algorithm, wherein the first descriptor vector is a descriptor vector of the first key point;

the server matches the target picture with the card face sample picture in the card face database based on the first descriptor vector and card face sample data in the card face database pre-established in the server to obtain matching similarity between the target picture and the card face sample picture, wherein the card face sample data is used for representing the card face sample picture;

the server selects the first N card surface sample pictures as alternative card surface pictures according to the sequence of the matching similarity from high to low, wherein N is a positive integer;

and the server sends the alternative card face picture to the user terminal, so that the user terminal displays the alternative card face picture for the user to select so as to determine the display card face picture of the entity card in the user terminal.

2. The method of claim 1, wherein a set of the card face sample data includes a second keypoint of the card face sample picture and a second descriptor vector, the second descriptor vector being a descriptor vector of the second keypoint;

The server matches the target picture with the card surface sample picture in the card surface database based on the first descriptor vector and the card surface sample data in the card surface database pre-established in the server to obtain the matching similarity of the target picture and the card surface sample picture, and the matching similarity comprises the following steps:

the server calculates Euclidean distance between the first descriptor vector and the second descriptor vector corresponding to each card surface sample picture;

the server selects the first M second descriptor vectors according to the order of the Euclidean distance from small to large, wherein M is a positive integer;

the server determines whether the first key point corresponding to the first descriptor vector is matched with the second key point corresponding to the selected second descriptor vector according to the Euclidean distance between the selected second descriptor vector and the first descriptor vector;

and the server counts the matching quantity of second key points matched with the first key points in each card surface sample picture, and obtains the matching similarity between the target picture and the card surface sample picture according to the matching quantity, wherein the matching quantity and the matching similarity are positively correlated.

3. The method of claim 2, wherein the server determining whether the first keypoints corresponding to the first descriptor vector match the second keypoints corresponding to the selected second descriptor vector according to the euclidean distance between the selected second descriptor vector and the first descriptor vector comprises:

the server determines that the first key point corresponding to the first descriptor vector is matched with the second key point corresponding to the selected second descriptor vector when the Euclidean distance between the selected second descriptor vector and the first descriptor vector is smaller than or equal to a matching distance threshold value, wherein M=1;

or alternatively, the process may be performed,

the server calculates the Euclidean distance ratio of a first Euclidean distance to a second Euclidean distance, wherein the first Euclidean distance is the Euclidean distance between a selected first second descriptor vector and the first descriptor vector, and the second Euclidean distance is the Euclidean distance between a selected second descriptor vector and the first descriptor vector, and M=2; and under the condition that the Euclidean distance ratio is smaller than or equal to a matching ratio threshold, the server determines that the first key point corresponding to the first descriptor vector is matched with the second key point corresponding to the acquired first and second descriptor vectors.

4. The method of claim 1, wherein the card face sample data comprises a first multi-dimensional vector of the card face sample picture, the first multi-dimensional vector being obtained from a second keypoint of the card face sample picture and a second descriptor vector, the second descriptor vector being a descriptor vector of the second keypoint;

the server matches the target picture with the card surface sample picture in the card surface database based on the first descriptor vector and the card surface sample data in the card surface database pre-established in the server to obtain the matching similarity of the target picture and the card surface sample picture, and the matching similarity comprises the following steps:

the server obtains a second multidimensional vector of the target picture according to the first descriptor vector by using a vector coding algorithm;

the server calculates a target Euclidean distance corresponding to each card surface sample picture, wherein the target Euclidean distance is the Euclidean distance between the second multidimensional vector and the first multidimensional vector corresponding to the card surface sample picture;

and the server obtains the matching similarity of the target picture and the card surface sample picture according to the target Euclidean distance, and the target Euclidean distance and the matching similarity are in negative correlation.

5. The method of claim 1, further comprising, before the server matches the target picture with a card face sample picture in a card face database based on the first descriptor vector and card face sample data in a card face database pre-established in the server:

the server matches the first descriptor vector with a preset third descriptor vector to obtain the first descriptor vector matched with the third descriptor vector, wherein the third descriptor vector is a descriptor vector of a third key point, the third key point is a key point of a common element, and the common element is the same pattern in a card surface of the entity card;

the server filters the first descriptor vector matched with the third descriptor vector in the first descriptor vector.

6. The method of claim 1, further comprising, prior to the server extracting the first keypoint and the first descriptor vector of the target picture using a feature extraction algorithm:

the server scales the target picture to a predetermined size using an image scaling algorithm.

7. The method of claim 1, further comprising, prior to the server extracting the first keypoint and the first descriptor vector of the target picture using a feature extraction algorithm:

the server utilizes a boundary recognition algorithm to recognize the boundary of the card surface of the entity card in the target picture;

and the server determines the area surrounded by the boundary of the card surface as an effective area of the target picture, wherein the effective area is used for extracting the first key point and the first descriptor vector.

8. The method as recited in claim 1, further comprising:

the server obtains a slave card surface sample picture based on the target picture, wherein the slave card surface sample picture is a picture of the card surface of the entity card after desensitization treatment;

the server obtains the card surface sample data corresponding to the slave card surface sample picture according to the first key point and the first descriptor vector corresponding to the slave card surface sample picture;

the server stores the slave card face sample picture as the card face sample picture to the card face database, stores the card face sample data corresponding to the slave card face sample picture into the card face database, and the card face sample picture corresponding to the target picture as the display card face picture is a master card face sample picture corresponding to the slave card face sample picture.

9. The method of claim 8, further comprising, after the server selects the first N card face sample pictures as candidate card face pictures in order of high-to-low matching similarity:

and under the condition that the first N selected card surface sample pictures comprise the slave card surface sample pictures, the server replaces the slave card surface sample pictures in the first N selected card surface sample pictures with the corresponding master card surface sample pictures.

10. The method of claim 1, further comprising, before the server obtains the target picture, before the target picture is a picture of the captured physical card:

the server inputs a card surface sample picture;

the server extracts a second key point and a second descriptor vector of the card surface sample picture by utilizing the feature extraction algorithm, wherein the second descriptor vector is a descriptor vector of the second key point;

the server obtains the card surface sample data according to the second key point and the second descriptor vector;

and the server establishes the card surface database based on the card surface sample picture and the card surface sample data.

11. The method of claim 10, wherein the step of determining the position of the first electrode is performed,

and under the condition that the number of the card surface sample pictures is smaller than or equal to a complex number threshold, the card surface sample data comprises the second key point and the second descriptor vector.

12. The method of claim 10, wherein obtaining the card face sample data from the second keypoint and the second descriptor vector comprises:

the server obtains a first multidimensional vector of each card face sample picture according to the second key point and the second descriptor vector of each card face sample picture by using a vector coding algorithm under the condition that the number of the card face sample pictures is larger than a complex number threshold value;

the server determines a first multi-dimensional vector of the card face sample picture as the card face sample data.

13. The method according to claim 4 or 12, wherein the card side sample data is stored in the card side database in an index structure.

14. The method of claim 10, further comprising, prior to the server extracting the second keypoint and the second descriptor vector of the card face sample picture using the feature extraction algorithm:

The server matches the second descriptor vector with a preset third descriptor vector to obtain the second descriptor vector matched with the third descriptor vector, wherein the third descriptor vector is a descriptor vector of a third key point, the third key point is a key point of a common element, and the common element is at least partially the same pattern in the card surface sample picture;

the server filters the second descriptor vector matched with the third descriptor vector in the second descriptor vector.

15. The method of claim 1, wherein the card face database comprises more than two sub-databases, the card face sample pictures in one sub-database belonging to the same card issuing organization;

the method further comprises the steps of:

the server acquires the binding card information of the entity card in the target picture, wherein the binding card information comprises a first identifier of an issuer representing the entity card;

the server matches the target picture with the card surface sample picture in the card surface database based on the first descriptor vector and the card surface sample data in the card surface database pre-established in the server to obtain the matching similarity of the target picture and the card surface sample picture, and the matching similarity comprises the following steps:

The server determines a target sub-database according to the first identifier, wherein the target sub-database is the sub-database corresponding to the card issuing institution indicated by the first identifier;

and the server matches the target picture with the card face sample picture in the target sub-database based on the first description sub-vector and the card face sample data in the target sub-database to obtain the matching similarity of the target picture and the card face sample picture.

16. An apparatus for capturing a display card surface, comprising:

the acquisition module is used for acquiring a target picture, wherein the target picture is a picture of a card surface of a photographed entity card;

the extraction module is used for extracting a first key point and a first descriptor vector of the target picture by utilizing a feature extraction algorithm, wherein the first descriptor vector is a descriptor vector of the first key point;

the matching module is used for matching the target picture with the card face sample picture in the card face database based on the first descriptor vector and the card face sample data in the card face database which is pre-established in the device for acquiring the display card face, so as to obtain the matching similarity of the target picture and the card face sample picture, wherein the card face sample data is used for representing the card face sample picture;

The selection module is used for selecting the first N card surface sample pictures as alternative card surface pictures according to the sequence of the matching similarity from high to low, wherein N is a positive integer;

and the sending module is used for sending the alternative card face picture to the user terminal, so that the user terminal can display the alternative card face picture for the user to select so as to determine the display card face picture of the entity card on the user terminal.

17. A server, comprising: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a method of obtaining a presentation card surface as claimed in any one of claims 1 to 15.

18. A computer-readable storage medium, having stored thereon computer program instructions which, when executed by a processor, implement a method of obtaining a presentation card surface as claimed in any one of claims 1 to 15.