Identification method for determining whether object to be identified is specific one among plurality of plural items generated by using template, and electronic apparatus

Abstract

An identification method for determining, by means of an electronic apparatus, whether an object to be identified is a specific one among a plurality of plural items generated by using a template. The method comprises: obtaining a plurality of identification images at a first positioning point of an object to be identified; obtaining a plurality of reference images of a specific one among a plurality of plural items as a reference object, wherein the plurality of reference images comprise: a plurality of positioning images on a second positioning point of the reference object, wherein the second positioning point of the reference object corresponds to the first positioning point of the object to be identified; and a plurality of first extended images on the second positioning point of the reference object, wherein the plurality of first extended images are generated by means of respectively performing adjustment on the basis of adjustment data in a specific one among the plurality of positioning images; and on the basis of the plurality of identification images and the plurality of reference images, determining whether the object to be identified is the specific one among the plurality of plural items.

Description

Identification method and electronic device for confirming whether an object to be identified is a specific one of multiple objects generated using a template Technical Field

[0001] The present invention relates to the field of object identification, and in particular, to an identification method and electronic device for confirming whether an object to be identified is a specific one of a plurality of objects generated using a template. Background Art

[0002] Despite the rapid advancement of anti-counterfeiting technology, many challenges remain for artworks, jewelry, antiques, and other items. Furthermore, for multiple artworks like prints and sculptures, since they are created by reusing a single template, each piece is authentic and extremely similar, making them difficult to distinguish. While each piece is numbered, the subtle differences between multiple pieces make it difficult to detect counterfeits. Even when multiple pieces are traded, not only is it necessary to verify the authenticity of the piece, but the accuracy of the serial number is also crucial.

[0003] Summary of the Invention

[0004] The present invention is made in view of the above-mentioned problems and provides an identification method and an identification device for confirming whether an object to be identified is a specific one of a plurality of items generated using a template.

[0005] In order to solve the above-mentioned problem, the present invention provides an identification method for confirming whether an object to be identified is a specific one of a plurality of plural objects generated using a template through an electronic device, the method comprising: obtaining a plurality of identification images at a first positioning point of the object to be identified; obtaining a plurality of reference images of the specific one of the plurality of plural objects as a reference object, wherein the plurality of reference images include: a plurality of positioning images at a second positioning point of the reference object, wherein the second positioning point of the reference object corresponds to the first positioning point of the object to be identified; and a plurality of first expanded images at the second positioning point of the reference object, wherein the plurality of first expanded images are An extended image is generated by adjusting adjustment data in a specific one of the multiple positioning images, wherein the adjustment data is selected from a plurality of specific data, the plurality of specific data including: a specific color data, wherein the specific color data in the specific one of the multiple positioning images is adjusted to form one of the multiple first extended images; and a specific area data, wherein the specific area data in the specific one of the multiple positioning images is adjusted to form another of the multiple first extended images; and based on the multiple recognition images and the multiple reference images, confirming whether the object to be identified is the specific one of the multiple plural objects.

[0006] To solve the above-mentioned problem, the present invention further provides an electronic device for confirming whether an object to be identified is a specific one of a plurality of plural objects generated using a template, the electronic device comprising: a processor; and a storage device, the storage device being coupled to the processor and storing a plurality of instructions, the plurality of instructions, when executed by the processor, causing the electronic device to: obtain a plurality of identification images at a first positioning point of the object to be identified; obtain a plurality of reference images of the specific one of the plurality of plural objects as a reference object, wherein the plurality of reference images comprise: a plurality of positioning images at a second positioning point of the reference object, wherein the second positioning point of the reference object corresponds to the first positioning point of the object to be identified; and the reference object a plurality of first extended images on the second positioning point, wherein the plurality of first extended images are generated by adjusting adjustment data in a specific one of the plurality of positioning images, wherein the adjustment data is selected from a plurality of specific data, the plurality of specific data including: a specific color data, wherein the specific color data in the specific one of the plurality of positioning images is adjusted to form one of the plurality of first extended images; and a specific area data, wherein the specific area data in the specific one of the plurality of positioning images is adjusted to form another of the plurality of first extended images; and based on the plurality of recognition images and the plurality of reference images, confirming whether the object to be identified is the specific one of the plurality of plural objects.

[0007] Through the above-described method, the recognition system and the electronic device can distinguish between multiple items generated using a template. If the recognition system and the electronic device only have multiple sample images of a specific one of the multiple items (serving as a reference object), the recognition system and the electronic device can still find the specific one from the multiple items. If the recognition system and the electronic device have multiple sample images of some of the multiple items (serving as multiple reference objects), the recognition system and the electronic device can still find the multiple items serving as the multiple reference objects from the multiple items.

[0008] Through the above method, the identification system and the electronic device can not only distinguish multiple objects generated using a template, but also confirm whether the object to be identified is the reference object when the serial number marked on the object to be identified is the same as the reference object. In other words, since the serial number does not change, the identification system and the electronic device can determine whether the object to be identified is a counterfeit. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the implementation methods or the description of the prior art. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] FIG. 1A is a block diagram of a sampling system for establishing sampling data for a reference object according to the present invention.

[0011] FIG. 1B is a block diagram of another sampling system for establishing sampling data for a reference object according to the present invention.

[0012] FIG2 is a block diagram of a sampling device for establishing sampling data for a reference object and performing sampling according to the present invention.

[0013] FIG3 is a flow chart of a sampling method for establishing sampling data for a reference object and performing sampling according to the present invention.

[0014] FIG. 4A is a schematic diagram of a sample image sampled on a reference object according to an exemplary embodiment of the present invention.

[0015] FIG. 4B is a schematic diagram of an expanded image generated by adjusting the sampled image of FIG. 4A according to the specific color data according to an exemplary embodiment of the present invention.

[0016] FIG. 4C is a schematic diagram of an expanded image generated by adjusting the sample image of FIG. 4A according to the specific region data according to an exemplary embodiment of the present invention.

[0017] FIG. 5A is a block diagram of an identification system for confirming whether an object to be identified is a specific one of a plurality of objects generated using a template according to the present invention.

[0018] FIG. 5B is a block diagram of another identification system for confirming whether an object to be identified is a specific one of a plurality of objects generated using a template according to the present invention.

[0019] FIG6 is a block diagram of an identification device for confirming whether an object to be identified is a specific one of a plurality of objects generated using a template according to the present invention.

[0020] FIG. 7 is a flow chart of an identification method according to the present invention for confirming whether an object to be identified is a specific one of a plurality of objects generated using a template.

[0021] 8A-8D are schematic diagrams of overall images of four print works among a plurality of print works printed using a print template.

[0022] FIG9A is a schematic diagram of a plurality of sample images obtained in advance on a print work numbered A by a sampling device according to an exemplary embodiment of the present invention.

[0023] FIG. 9B is a schematic diagram of a plurality of sample images captured at random positions on the overall image of the print work numbered A. FIG.

[0024] FIG. 10A is a schematic diagram of a plurality of sample images obtained in advance on a print work numbered A by a sampling device according to an exemplary embodiment of the present invention.

[0025] FIG. 10B is a schematic diagram illustrating a plurality of expanded images generated by adjusting the plurality of sample images according to adjustment data according to an exemplary embodiment of the present invention.

[0026] FIG. 11 is a schematic diagram illustrating a distribution of confidence values ​​of 80 recognition images tested in a second experiment according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following description contains specific information related to exemplary embodiments of the present invention. The drawings and accompanying detailed descriptions herein are exemplary embodiments only. However, the present invention is not limited to these exemplary embodiments. Other variations and embodiments of the present invention will occur to those skilled in the art. Unless otherwise indicated, identical or corresponding elements in the drawings may be indicated by identical or corresponding reference numerals. In addition, the drawings and illustrations herein are generally not drawn to scale and are not intended to correspond to actual relative dimensions.

[0028] For the purpose of consistency and ease of understanding, the same features are indicated by reference numerals in the exemplary drawings (although not so indicated in some examples). However, features in different embodiments may differ in other aspects and should not be narrowly limited to the features shown in the drawings.

[0029] Phrases such as "at least one embodiment," "one embodiment," "multiple embodiments," "different embodiments," "some embodiments," and "this embodiment" may indicate that the embodiment of the invention so described may include particular features, structures, or characteristics, but not every possible embodiment of the invention must include the particular features, structures, or characteristics. In addition, repeated use of the phrases "in one embodiment" and "in this embodiment" does not necessarily refer to the same embodiment, although they may be the same. In addition, phrases such as "an embodiment" used in connection with "the present invention" do not mean that all embodiments of the invention must include particular features, structures, or characteristics, and it should be understood that "at least some embodiments of the present invention" include the described particular features, structures, or characteristics. The term "coupled" is defined as connected, whether directly or indirectly through intermediate elements, and is not necessarily limited to physical connections. When the term "including" is used, it means "including but not limited to," which expressly indicates the open inclusion or relationship of the stated combinations, groups, series, and equivalents.

[0030] In addition, for purposes of explanation and non-limiting, specific details such as functional entities, technologies, protocols, standards, etc. are set forth to provide an understanding of the described technology. In other instances, detailed descriptions of well-known methods, techniques, systems, architectures, etc. are omitted to avoid obscuring the description with unnecessary detail.

[0031] The terms "first," "second," and "third," etc., in the present description and accompanying drawings are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "comprise," "include," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or other steps or modules that are inherent to the process, method, product, or apparatus.

[0032] Those skilled in the art will readily recognize that any computing function or algorithm described herein may be implemented using hardware, software, or a combination of software and hardware. The modules corresponding to the described functions may be software, hardware, firmware, or any combination thereof. Software implementations may include computer-executable instructions stored on a computer-readable medium such as a memory or other type of storage. For example, one or more microprocessors or general-purpose computers with communication processing capabilities may be programmed with corresponding executable instructions to execute the described network functions or algorithms. The processors, microprocessors, or general-purpose computers may be implemented using application-specific integrated circuits (ASICs), programmable logic arrays, and / or one or more digital signal processors (DSPs). Although several exemplary embodiments described herein tend to be implemented using software installed and executed on computer hardware, alternative exemplary embodiments implemented using firmware, hardware, or a combination of hardware and software are also within the scope of the present invention.

[0033] The storage device may be a computer-readable medium, including but not limited to random access memory RAM (Random Access Memory), read-only memory ROM (Read Only Memory), erasable programmable read-only memory EPROM (Erasable Programmable Read-Only Memory), electrically erasable programmable read-only memory EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, compact disc read-only memory CD ROM (Compact Disc Read-Only Memory), magnetic cassettes, magnetic tapes, disk storage, or any other equivalent medium that can store computer-readable instructions.

[0034] The coupling between the various devices of the present invention may adopt customized protocols or follow existing standards or de facto standards, including but not limited to Ethernet, IEEE 802.11 or IEEE 802.15 series, Wireless USB, or telecommunications standards (including but not limited to GSM (Global System for Mobile Communications), CDMA2000 (Code Division Multiple Access), TD-SCDMA (Time Division-Synchronization Code Division Multiple Access), WiMAX (World Interoperability for Microwave Access), 3GPP-LTE (Long Term Evolution), or TD-LTE (Time Division Long Term Evolution). In addition, each device of the present invention may also include any device configured to transmit and / or store data to a computer-readable medium and receive data from a computer-readable medium. Furthermore, each device of the present invention may include a computer system interface that enables data to be stored on or received from a storage device. For example, the devices of the present invention may include chipsets that support Peripheral Component Interconnect (PCI) and Peripheral Component Interconnect Express (PCIe) bus protocols, proprietary bus protocols, Universal Serial Bus (USB) protocols, I2C, or any other logical and physical structures that can be used to interconnect peer devices.

[0035] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0036] Please refer to Figure 1A, which is a block diagram of a sampling system 101 for sampling a reference object to establish sampling data. In one embodiment, the sampling system 101 includes a server 10 and a sampling device 20. The server 10 may include an internal storage device 11 to store the sampling results. In one embodiment, when the sampling device 20 receives a sampling request, the sampling device 20 can be coupled to the server 10 to complete the sampling method described in the present invention. When the sampling system 101 completes the sampling method, the sampling device 20 can stop coupling with the server 10. In one embodiment, the sampling device 20 and the server 10 are both electronic devices. In one embodiment, the identification device 50 and the server 10 are both electronic devices and can execute the identification method of the present case.

[0037] Please refer to FIG. 1B , which is a block diagram of another sampling system 102 for establishing sampling data for a reference object according to the present invention. In one embodiment, the sampling system 102 includes a server 10, a sampling device 20, and an online storage device 30. In one embodiment, when the sampling device 20 receives a sampling request, it can couple with the server 10, which can further couple with the online storage device 30 to perform the sampling method described herein. After the sampling system 102 completes the sampling method, the sampling device 20 can decouple from the server 10, and the server 10 can also decouple from the online storage device 30. In another embodiment, when the sampling device 20 receives a sampling request, it can couple with the server 10 and the online storage device 30 to perform the sampling method described herein. After the sampling system 102 completes the sampling method, the sampling device 20 can decouple from the server 10 and the online storage device 30.

[0038] In the embodiment described above, the online storage device 30 can be a network data storage device or a blockchain storage device. When the online storage device 30 is a blockchain storage device, the characteristics of blockchain can be leveraged to reduce the possibility of tampering or replacement of sampling results. In the embodiment described above, the online storage device 30 can store records of all transactions involving the reference object, the time and results of inspections, and updated images.

[0039] In one embodiment, the reference object may be a specific one of multiple plural items produced using a template. The multiple plural items may be multiple prints printed using a print template, multiple sculptures cast using a sculpture template, or multiple photographs developed using a film. Therefore, as long as multiple manufactured items can be produced through repeated use of a single template, they can be used as the multiple plural items in this case. In one embodiment, by using the previously established sampling data of the reference object as a basis for subsequent identification of the object to be identified, it can be confirmed whether the object to be identified is the specific one of the multiple plural items. Even if the object to be identified is another object in the multiple plural items, it can still be identified that the object to be identified is not the specific one of the multiple plural items.

[0040] Please refer to Figure 2, which is a block diagram of a sampling device 20 for establishing sampling data for a reference object according to the present invention. Sampling device 20 can be a mobile phone, tablet computer, desktop computer, laptop computer, camera, video recorder, or other electronic device, without limitation. Sampling device 20 includes an image capture unit 21, a receiving unit 22, a transmitting unit 23, a processor 24, and a memory 25.

[0041] The image capture unit 21 is used to acquire multiple sample images. When the sampling device 20 samples the reference object, the image capture unit 21 can be moved to multiple different sampling positions, allowing the image capture unit 21 to capture images of the reference object at different sampling positions to obtain the multiple sample images. The image capture unit 21 can be a charge-coupled device (CCD) image sensor, a complementary metal-oxide-semiconductor (CMOS) image sensor, or a camera. In one embodiment, the image capture unit 21 can include a high-magnification image capture lens to obtain clearer and more detailed information.

[0042] The receiving unit 22 and the transmitting unit 23 may utilize custom protocols or comply with existing or de facto standards, including but not limited to Ethernet, IEEE 802.11 or IEEE 802.15 series, Wireless USB, or telecommunication standards (including but not limited to GSM, CDMA2000, TD-SCDMA, WiMAX, 3GPP-LTE, or TD-LTE), to transmit the sampling characteristics to devices other than the sampling device 20 and receive information from devices other than the sampling device 20. In one embodiment, the receiving unit 22 and the transmitting unit 23 may be integrated into a communication unit.

[0043] The processor 24 and the memory 25 are coupled to each other. The memory 25 stores a plurality of instructions for the processor 24 to execute the sampling method of the sampling device 20 according to the plurality of instructions stored in the memory 25. To implement the sampling method of the present invention, the memory 25 stores a sampling program 250. In the embodiment, the sampling program 250 further includes a sampling module 251 and an expansion module 252. The sampling module 251 is used to enable the processor 24 to assist the image capture unit 21 in obtaining the required sampling image at the sampling position. The expansion module 252 is used to enable the processor 24 to adjust the adjustment data of each of the plurality of sampling images to generate a plurality of expanded images. When the number of the plurality of sampling images is N, the expansion module 252 is used to enable the processor 24 to generate a plurality of first expanded images, a plurality of second expanded images, ..., a plurality of N-1th expanded images, and a plurality of Nth expanded images. In one embodiment, N is a positive integer greater than or equal to 1.

[0044] FIG3 is a flow chart of a sampling method 300 for establishing sampling data for a reference object according to the present invention. Because there are many ways to implement the sampling method 300, the sampling method 300 shown in FIG3 is merely an example. The sampling method 300 can be implemented using the configurations shown in FIG1A , FIG1B , and FIG2 . When describing the sampling method 300, please refer to the various elements shown in FIG1A , FIG1B , and FIG2 . Each step shown in FIG3 may represent one or more processes, methods, or subroutines to be performed, and the order of the steps may be adjusted arbitrarily without departing from the essence of the sampling method 300 and the scope of the technical solution of the sampling method 300.

[0045] In step S310 , a fixed point on the reference object is obtained.

[0046] In one embodiment, the positioning point can be used as a sampling position so that the image capture unit 21 of the sampling device 20 can obtain multiple sampling images for the sampling position. In one embodiment, the sampling device 20 can select the positioning point of the reference object by itself. In one embodiment, when the sampling device 20 receives a request to establish the sampling data for the reference object, the sampling device 20 can select the positioning point on the reference object by itself. In one embodiment, the image capture unit 21 of the sampling device 20 can obtain an entire image for the reference object, and the user can select a position in the reference object from the entire image as the positioning point. In another embodiment, the image capture unit 21 of the sampling device 20 can obtain an entire image for the reference object and select a position on the reference object as the positioning point randomly or through a preset selection method.

[0047] In one embodiment, the sampling device 20 may receive the positioning point for the reference object from a device other than the sampling device 20. In such an embodiment, the receiving unit 22 of the sampling device 20 may receive data provided to the sampling device 20 from a device other than the sampling device 20.

[0048] In one embodiment, when the sampling device 20 receives a request to create sampling data for the reference object, the image capture unit 21 of the sampling device 20 may capture an entire image of the reference object and transmit the entire image to the server 10 via the transmission unit 23. Upon receiving the request to create sampling data for the reference object and the entire image, the server 10 may select the location point from the entire image based on a predetermined selection method and transmit the location point back to the receiving unit of the sampling device 20. In one embodiment, the server 10 may store the location point in the internal storage device 11 of the server 10. In another embodiment, the server 10 may transmit the location point to the online storage device 30 for storage.

[0049] In step S320 , a plurality of sample images are acquired based on the positioning point of the reference object.

[0050] In one embodiment, the sampling device 20 can obtain multiple sample images at the positioning point. In one embodiment, the sampling device 20 can configure multiple sampling methods and sampling times for the positioning point. In one embodiment, because each sampling process may vary slightly, each sample image may also differ slightly. In one embodiment, different sampling times may result in slight variations in the position of the image capture unit 21, leading to slight deviations between the sample images due to factors such as distance and angle. For example, variations in sampling distance may cause a specific area of ​​the reference object to occupy slightly different proportions in the multiple sample images. Furthermore, variations in the sampling angle may cause the size of the specific area to vary slightly from location to location. The sampling angle can even affect the ambient light during sampling, further affecting the image color of the multiple sample images. Therefore, slight variations may still exist between the multiple sample images obtained by the sampling device 20 at the positioning point.

[0051] In step S330 , a plurality of expanded images are generated based on the plurality of sample images.

[0052] In one embodiment, the plurality of sample images may be adjusted based on respective adjustment data to generate the plurality of extended images. In one embodiment, the plurality of sample images may be N in number, and the plurality of sample images may include a first sample image, a second sample image, ..., an N-1th sample image, and an Nth sample image. In one embodiment, the first sample image may be adjusted based on the first adjustment data to generate a plurality of first extended images, the second sample image may be adjusted based on the second adjustment data to generate a plurality of second extended images, and the Nth sample image may be adjusted based on the Nth adjustment data to generate a plurality of Nth extended images. In one embodiment, the first adjustment data, the second adjustment data, ..., the N-1th adjustment data, and the Nth adjustment data may be identical. In another embodiment, the first adjustment data, the second adjustment data, ..., the N-1th adjustment data, and the Nth adjustment data may be generated by selecting from a plurality of specific data, and thus the first adjustment data, the second adjustment data, ..., the N-1th adjustment data, and the Nth adjustment data may be completely or partially different from each other. In one embodiment, N is a positive integer greater than or equal to 1.

[0053] In one embodiment, the adjustment data is selected from a plurality of specific data, which may be specific color data. In one embodiment, adjusting the specific color data in a specific one of the plurality of positioning images can generate one of the plurality of first extended images. For example, if the plural items are prints, multiple colors are printed one by one during the printmaking process. Therefore, even in a high-magnification sampling result, the different colors can still be clearly seen. In one embodiment, when the first sample image is adjusted based on the specific color data, the specific color data in the first sample image can be shifted in a specific direction to generate one of the plurality of first extended images. For example, if the specific color data is green color data, all of the green color data in the first sample image can be shifted in the specific direction by a first specific distance to generate one of the plurality of first extended images. Alternatively, all of the green color data in the first sample image can be shifted in the specific direction by a second specific distance to generate one of the plurality of first extended images. Furthermore, the green color data in the first sampled image can all be shifted in another specific direction by the first specific distance, the second specific distance, or another specific distance to generate a portion of the multiple first extended images. Furthermore, the other color data in the first sampled image can all be shifted in the specific direction or another specific direction by the specific distances to generate another portion of the multiple first extended images. For example, FIG4A is a schematic diagram of a sampled image 400 sampled on a reference object according to an exemplary embodiment of the present invention. FIG4B is a schematic diagram of an extended image 410 generated by adjusting the sampled image 400 of FIG4A based on the specific color data according to an exemplary embodiment of the present invention. In FIG4B , the specific color data is color data 411. The color data 411 in the sampled image 400 of FIG4A is shifted to the left to generate the extended image 410 of FIG4B .

[0054] In one embodiment, the multiple specific data may be specific area data. In one embodiment, adjusting the specific area data in the specific one of the multiple positioning images may form one of the multiple first extended images. In one embodiment, when the first sampling image is adjusted according to the specific area data, a plurality of color blocks may be extracted from the first sampling image, and the plurality of color blocks may be swapped with each other to generate one of the multiple first extended images. In one embodiment, the number of the plurality of color blocks may be two, three or more color blocks. In one embodiment, the size of the plurality of color blocks may be X×Y, where X and Y may both be positive integers greater than 1, and X may be equal to or different from Y. In one embodiment, the size of the plurality of color blocks may be 10 pixels×10 pixels. In one embodiment, the selection of the plurality of color blocks may be randomly selected. In one embodiment, the selection of the plurality of color blocks may also be selected through a preset selection method. In another embodiment, when the first sampled image is adjusted based on the specific region data, one or more color blocks may be extracted from the first sampled image and arbitrarily replaced with other color blocks. For example, the one or more color blocks may be randomly replaced with a single color block (e.g., a red block, a blue block, etc.). For example, FIG4C is a schematic diagram of an expanded image 420 generated by adjusting the sampled image 400 of FIG4A based on the specific region data according to an exemplary embodiment of the present invention. In FIG4C , the specific region data is two color blocks 421 and 422. The two color blocks in the sampled image 400 of FIG4A are swapped with each other, resulting in the expanded image 420 of FIG4C having the two color blocks 421 and 422.

[0055] In one embodiment, the sampling device 20 can independently generate the multiple extended images based on the multiple sampling images, and the sampling device 20 transmits the multiple sampling images and the multiple extended images to the server 10 via the transmitting unit 23 for storage by the server 10 or the online storage device 30. In one embodiment, the sampling device 20 can directly transmit the multiple sampling images to the server 10 via the transmitting unit 23, and the server 10 then generates the multiple extended images based on the multiple sampling images and stores the multiple sampling images and the multiple extended images in the server 10 or the online storage device 30. In one embodiment, the multiple sampling images and the multiple extended images are stored in the server 10 or the online storage device 30 as multiple stored images of the specific plural item.

[0056] In one embodiment, the sampling device 20 can directly transmit the multiple sampling images to the server 10 via the transmission unit 23, and the server 10 can directly store the multiple sampling images on the server 10 or in the online storage device 30. In this embodiment, neither the sampling device 20 nor the server 10 generates the multiple extended images based on the multiple sampling images. Instead, the sampling device 20 and the server 10 directly store the multiple sampling images. When the multiple sampling images are needed to identify the object to be identified, the multiple extended images are generated based on the multiple sampling images to perform the identification of the object to be identified. In one embodiment, the multiple sampling images are stored in the server 10 or the online storage device 30 as the multiple stored images of the specific plural object. In one embodiment, the multiple stored images can be the multiple sampling images obtained based on the positioning point of the reference object. In another embodiment, the plurality of stored images may be the overall image of the reference object, and the overall image may be a high-magnification clear image for subsequent identification of the object to be identified. When the positioning point is selected to generate the plurality of expanded images, a sufficiently clear image can still be retained to generate the plurality of expanded images.

[0057] Please refer to FIG. 5A , which is a block diagram of an identification system 501 provided by the present invention for determining whether an object to be identified is a specific one of a plurality of items generated using a template. In one embodiment, the identification system 501 includes a server 10 and an identification device 50. The server 10 may include an internal storage device 11 for pre-storing a sample image obtained by the sampling device 20 of FIG. 2 . In one embodiment, when the identification device 50 receives an identification request, the identification device 50 may couple with the server 10 to perform the identification method described in the present invention. After the identification system 501 completes the identification method, the identification device 50 may terminate the coupling with the server 10.

[0058] Please refer to Figure 5B, which is a block diagram of another identification system 502 provided by the present invention for confirming whether an object to be identified is a specific one of a plurality of plural items generated using a template. In one embodiment, the identification system 502 includes a server 10, an identification device 50, and an online storage device 30. In one embodiment, when the identification device 50 receives an identification request, the identification device 50 can be coupled to the server 10, and the server 10 can be further coupled to the online storage device 30 to complete the identification method described in the present invention. When the identification system 502 completes the identification method, the identification device 50 can stop coupling with the server 10, and the server 10 can also stop coupling with the online storage device 30. In another embodiment, when the identification device 50 receives an identification request, the identification device 50 can be coupled to the server 10 and the online storage device 30 to complete the identification method described in the present invention. When the identification system 502 completes the identification method, the identification device 50 may stop coupling with the server 10 and the online storage device 30 .

[0059] In the described embodiment, the online storage device 30 can be a network data storage device or a blockchain storage device. When the online storage device 30 is a blockchain storage device, the characteristics of the blockchain can be leveraged to reduce the possibility of tampering or replacement of previously stored sampling features. In the described embodiment, the online storage device 30 can store records of all transactions involving the reference object, the time and results of inspections, and updated images.

[0060] Please refer to Figure 6, which is a block diagram of an identification device 50 according to the present invention for determining whether an object to be identified is a specific one of multiple objects generated using a template. Identification device 50 can be a mobile phone, tablet computer, desktop computer, laptop computer, camera, video recorder, or other electronic device, without limitation. Identification device 50 includes an image capture unit 51, a receiving unit 52, a transmitting unit 53, a processor 54, and a memory 55.

[0061] The image capture unit 51 is used to obtain multiple recognition images. When the recognition device 50 is to identify the object to be identified, the image capture unit 51 can be moved to multiple different recognition positions so that the image capture unit 51 can capture images of the object to be identified at different recognition positions to obtain the multiple recognition images. The image capture unit 51 can be a charge-coupled device (CCD) image sensor, a complementary metal-oxide-semiconductor (CMOS) image sensor, or a camera. In one embodiment, the image capture unit 51 can include a high-magnification image capture lens to obtain clearer and more detailed information.

[0062] The receiving unit 52 and the transmitting unit 53 may utilize custom protocols or comply with existing standards or de facto standards, including but not limited to Ethernet, IEEE 802.11 or IEEE 802.15 series, Wireless USB, or telecommunications standards (including but not limited to GSM, CDMA2000, TD-SCDMA, WiMAX, 3GPP-LTE, or TD-LTE), to transmit identification features to devices other than the identification device 50 and to receive sample images transmitted from devices other than the identification device 50. In one embodiment, the receiving unit 52 and the transmitting unit 53 may be integrated into a communication unit.

[0063] The processor 54 and the storage 55 are coupled to each other. The storage 55 stores a plurality of instructions for the processor 54 to execute the recognition method of the recognition device 50 according to the plurality of instructions stored in the storage 55. To implement the recognition method of the present invention, the storage 55 stores a recognition program 550. In the embodiment described, the recognition program 550 further includes a capture module 551 and a recognition module 552. The capture module 551 is used to enable the processor 54 to assist the image capture unit 21 in obtaining the required recognition image at the recognition position. The recognition module 552 is used to enable the processor 54 to confirm whether the object to be recognized is the specific one of the plurality of items based on the plurality of recognition images and the plurality of reference images of the reference object.

[0064] FIG7 is a flowchart of an identification method 700 provided by the present invention for confirming whether an object to be identified is a specific one of a plurality of plural items generated using a template. Because there are many ways to perform the identification method, the identification method 700 shown in FIG3 is only an example. The identification method 700 can be performed using the configurations shown in FIG5A, FIG5B and FIG6, and when describing the identification method 700, please refer to the various elements in FIG5A, FIG5B and FIG6. Each step shown in FIG7 can represent one or more processes, methods or subroutines performed, and the order of each step can be adjusted arbitrarily without causing the essence of the identification method 700 to deviate from the scope of the technical solution of the identification method 700.

[0065] In step S710 , a plurality of recognition images at a first positioning point of the object to be recognized are obtained.

[0066] In one embodiment, to confirm whether an object to be identified is a specific one of multiple items generated using a template, the identification device 50 first requires the server 10 to obtain information about the object to be identified. The server 10 then uses the information to identify a reference object corresponding to the object to be identified. In this embodiment, the reference object is the specific one of the multiple items generated using the template. In another embodiment, the information about the object to be identified obtained by the server 10 can be text entered by the user or a captured image of the object to be identified.

[0067] In one embodiment, when the server 10 or online storage device 30 stores multiple stored images for a specific plural object including the multiple sample images, the recognition device 50 needs to know which area of ​​the object to be identified should be captured before capturing the multiple recognition images so that it can compare them with the stored images stored in the online storage device 30 or internal storage device 11. Therefore, the recognition device 50 may first transmit a request for the location point of the object to be identified in order to obtain the location point of the object to be identified. In this embodiment, the multiple stored images may further include multiple expanded images, or may not include the multiple expanded images. In the embodiment described above, referring to FIG. 2 , the location point requested by the recognition device 50 is the location point at which the sampling module 251 of the sampling device 20 samples the sampled image (to distinguish between the two locations, the location point requested by the recognition device 50 is the first location point, while the location point at which the sampling module 251 samples the sampled image may be the second location point). In one embodiment, when the recognition device 50 obtains the second location point of the sampling module 251 via the receiving unit 52, the recognition device 50 can obtain the first location point corresponding to the second location point of the reference object on the object to be identified based on the second location point. In one embodiment, because the server 10 can confirm the identity of the object to be identified by requesting the location point of the object to be identified from the recognition device 50, it can transmit relevant information about the reference object corresponding to the object to be identified (e.g., including the second location point) to the receiving unit 52 of the recognition device 50. Therefore, the identification device 50 can determine the first positioning point of the object to be identified through the second positioning point of the reference object corresponding to the object to be identified.

[0068] In one embodiment, when the server 10 or online storage device 30 stores multiple stored images for a specific plural object, including the multiple sample images, the recognition device 50 needs to know which area of ​​the object to be recognized should be captured before capturing the multiple recognition images. Therefore, the recognition device 50 may first transmit a request for the multiple stored images to obtain the first positioning point of the object to be recognized by comparing the relationship between the multiple sample images and the object to be recognized. In this embodiment, the multiple stored images may further include or exclude multiple extended images. In this embodiment, referring to FIG. 2 , the first positioning point retrieved by the recognition device 50 is the second positioning point of the multiple positioning images obtained by the sampling module 251 of the sampling device 20. Therefore, by comparing the multiple sample images with the object to be recognized, the second positioning point when the sampling image was sampled and the first positioning point corresponding to the second positioning point can be inferred. In the embodiment, the recognition device 50 may receive the plurality of positioning images of the second positioning point of the reference object provided by the server 10 , and determine the first positioning point of the object to be recognized based on the second positioning point of the reference object.

[0069] In one embodiment, when the multiple stored images stored by the server 10 or the online storage device 30 for the specific multiple objects include the multiple sample images (i.e., excluding the multiple expanded images), and the multiple sample images are multiple whole images of the reference object, before the recognition device 50 captures the multiple recognition images, since the multiple sample images are the multiple whole images of the reference object and there is no clear positioning point, the recognition device 50 can select the first positioning point from the identification object randomly or in a preset manner, or receive the first positioning point specified randomly or in a preset manner from the server 10.

[0070] In step S720 , a plurality of reference images of the specific one of the plurality of objects serving as a reference object are obtained.

[0071] In one embodiment, the plurality of reference images of the reference object include a plurality of positioning images at the second positioning point of the reference object and the plurality of augmented images are generated by adjusting the plurality of positioning images based on respective adjustment data. In this embodiment, since the plurality of augmented images are generated based on the plurality of positioning images at the second positioning point of the reference object, the plurality of augmented images are also generated by the second positioning point of the reference object. Therefore, the plurality of reference images include the plurality of positioning images at the second positioning point of the reference object and the plurality of augmented images at the second positioning point of the reference object.

[0072] In one embodiment, when the server 10 or online storage device 30 stores multiple stored images for a specific plural object, including the multiple sample images and the multiple expanded images, the recognition device 50 may first transmit a request for identifying the first location point of the object to be identified, or transmit a request for the multiple stored images, before capturing the multiple identified images. In this embodiment, when transmitting the request for identifying the first location point of the object to be identified, the recognition device 50 may also simultaneously request the multiple stored images from the server 10 or online storage device 30. In this embodiment, the multiple sample images in the multiple stored images are the multiple location images of the reference object at the second location point, and the multiple expanded images in the multiple stored images are the expanded images generated by adjusting the adjustment data in the multiple location images.

[0073] In one embodiment, when the server 10 or online storage device 30 stores multiple stored images for a specific plural object, including the multiple sample images (and excluding the multiple expanded images), the recognition device 50 may first transmit a request for identifying the first location point of the object to be identified, or transmit a request for the multiple stored images, before capturing the multiple identified images. In this embodiment, the recognition device 50 may also simultaneously request the multiple stored images from the server 10 or online storage device 30 when transmitting the request for identifying the first location point of the object to be identified. In this embodiment, the multiple sample images in the multiple stored images are the multiple location images of the reference object at the second location point. In one embodiment, the recognition device 50 may independently adjust the multiple location images based on the adjustment data of each of the multiple location images to generate the multiple expanded images.

[0074] In one embodiment, when the server 10 or online storage device 30 stores multiple images of a specific plural object, including the multiple sample images (but not the multiple expanded images), and the multiple sample images are multiple whole images of the reference object, after the recognition device 50 obtains the first positioning point on the object to be recognized, the recognition device 50 can determine the second positioning point corresponding to the first positioning point from the multiple whole images of the reference object based on the first positioning point of the object to be recognized. In this embodiment, the recognition device 50 can receive the multiple whole images from the server 10 or online storage device 30 before determining the second positioning point. In one embodiment, the recognition device 50 can extract the multiple positioning images from the multiple whole images based on the second positioning point, and the recognition device 50 can independently adjust the multiple positioning images based on the adjustment data of each of the multiple positioning images to generate the multiple expanded images.

[0075] In one embodiment, when the server 10 or online storage device 30 stores multiple images of a specific plural object, including the multiple sample images (but not the multiple expanded images), and the multiple sample images are multiple whole images of the reference object, after the recognition device 50 obtains the first positioning point on the object to be recognized, the server 10 can determine the second positioning point corresponding to the first positioning point from the multiple whole images of the reference object based on the first positioning point of the object to be recognized. In this embodiment, after determining the first positioning point, the recognition device 50 can provide the server 10 with at least one of the multiple recognition images and the first positioning point. In one embodiment, the server 10 can extract the multiple positioning images from the multiple whole images based on the second positioning point and transmit the multiple positioning images to the recognition device 50. In one embodiment, the multiple expanded images can be generated by the server 10 and then transmitted to the recognition device 50. In another embodiment, after receiving the plurality of positioning images, the recognition device 50 may independently perform adjustments according to the respective adjustment data of the plurality of positioning images to generate the plurality of expanded images.

[0076] In one embodiment, the adjustment data is selected from a plurality of specific data, which may be specific color data. For example, if the plural items are prints, multiple colors are printed one by one during the printmaking process. Therefore, even in a high-magnification sampling result, the different colors can still be clearly seen. In one embodiment, taking the first sample image among the multiple sample images as an example, when the first sample image is adjusted based on the specific color data, the specific color data in the first sample image may be shifted in a specific direction. In one embodiment, the multiple specific data may be specific region data. In one embodiment, taking the first sample image as an example, when the first sample image is adjusted based on the specific region data, multiple color blocks may be extracted from the first sample image and swapped with each other. In another embodiment, when the first sample image is adjusted based on the specific region data, one or more color blocks may be extracted from the first sample image and arbitrarily replaced with other color blocks.

[0077] In one embodiment, when the number of the plurality of sample images is N, the expansion module 252 is configured to cause the processor 24 to generate a plurality of first expanded images, a plurality of second expanded images, ..., a plurality of (N-1)th expanded images, and a plurality of (N)th expanded images. In one embodiment, the plurality of first expanded images are generated by adjusting the adjustment data in a first locator image among the plurality of locator images, the plurality of second expanded images are generated by adjusting the adjustment data in a second locator image among the plurality of locator images, and the plurality of (N)th expanded images are generated by adjusting the adjustment data in an (N)th locator image among the plurality of locator images. In one embodiment, N is a positive integer greater than or equal to 1.

[0078] In step S730 , based on the plurality of recognition images and the plurality of reference images, it is determined whether the object to be recognized is the specific one of the plurality of objects.

[0079] In one embodiment, the multiple reference images can be used to train a recognition model. In the embodiment, the recognition model can be a machine learning model. In one embodiment, the multiple positioning images of the multiple reference images are generated by actual sampling from the reference object, and thus the multiple positioning images are set as images of the reference object. In one embodiment, the multiple expanded images of the multiple reference images are generated by adjusting the multiple positioning images, and thus the multiple expanded images are set as images that are not of the reference object. Therefore, after the recognition model is trained with the images set as the reference object and the images set as not belonging to the reference object in the multiple reference images, the recognition model can be used to accurately identify whether the object to be identified is the specific one of the multiple plural items.

[0080] In one embodiment, the plurality of reference images may be trained in the recognition device 50 to form the recognition model, and the recognition device 50 may input the plurality of recognition images into the recognition model to confirm whether the object to be recognized is the specific one of the plurality of items.

[0081] In one embodiment, the multiple reference images may be trained on the server 10 to form the recognition model. Therefore, the recognition device 50 may retransmit the multiple recognition images to the server 10 via the transmission unit 53. The server 10 then inputs the multiple recognition images into the recognition model to determine whether the object to be recognized is the specific one of the multiple items, and finally transmits the recognition result back to the recognition device 50. In this embodiment, the recognition device 50 may not need to obtain the multiple expanded images. Therefore, the recognition device 50 only needs to determine the first positioning point using the second positioning point or the multiple sample images provided by the server 10, and then extract the multiple recognition images accordingly. In another embodiment, when the server 10 or online storage device 30 stores only the multiple whole-body images of the reference object, the recognition device 50 may then directly transmit the multiple recognized images to the server 10 after independently determining the first positioning point on the object to be recognized and capturing the multiple recognized images. The server then determines the second positioning point to obtain the multiple positioning images and the multiple expanded images from the multiple whole-body images, thereby further training the recognition model. Alternatively, after capturing the multiple recognized images on the object to be recognized, the recognition device 50 may further determine the second positioning point to obtain the multiple positioning images (or even the multiple expanded images) from the multiple whole-body images, and upload the multiple positioning images and the multiple recognized images to the server 10 for subsequent training and recognition. In this embodiment, the multiple expanded images may be generated by the server 10 or the recognition device 50.

[0082] In one embodiment, because the differences between the multiple items generated using the template are minimal, relying solely on the multiple positioning images of the reference object at the second positioning point would make it difficult to distinguish the differences between the multiple items, resulting in the multiple items being identified as the same. In one embodiment, because the multiple items are all generated based on the template, the differences between the multiple items often lie in slight color differences and / or slight regional differences. For example, the multiple items generated using the same template may have slight positional deviations when the various colors are applied, resulting in minimal color shifts. Furthermore, minimal regional differences may also arise due to missing color blocks in specific areas between the various colors. Therefore, adjusting the multiple positioning images using the adjustment data helps the recognition method pre-identify possible differences between the reference object and the other multiple items, enabling the recognition method to identify a specific object among the multiple items that is identical to the reference object.

[0083] To more clearly illustrate this case, the following experiment uses multiple prints as the basis. Figures 8A-8D are schematic diagrams of the overall images of four prints from a plurality of prints printed using a print template. The four prints are numbered A, B, C, and D, respectively, with A, B, C, and D being unique numbers.

[0084] Suppose a user obtains four prints numbered A, B, C, and D, but the user does not know the numbers of the four prints, nor does the user know whether the four prints are authentic. However, if the sampling device 20 previously only has the sampling results of the print numbered A (i.e., the sampling device 20 previously sampled only the print numbered A as a reference object and obtained multiple sample images), the identification device 50 can only automatically number the four prints as the first, second, third, and fourth objects to be identified, and then use the multiple first, second, third, and fourth identification images obtained from the four objects to be identified to determine whether the print numbered A is present in the four objects to be identified.

[0085] In a first experiment, since the sampling device 20 only has a plurality of sample images and an overall image of the print work numbered A beforehand, the identification method can use the plurality of sample images as the plurality of positioning images and set them as the image of the reference object, and arbitrarily capture a plurality of sample images at any position in the overall image. Figure 9A is a schematic diagram of a plurality of sample images previously captured by the sampling device 10 on the print work numbered A according to an exemplary embodiment of the present invention. Figure 9B is a schematic diagram of a plurality of sample images captured at arbitrary positions on the overall image of the print work numbered A. Because the positions of the plurality of sample images are different from the second positioning point of the plurality of sample images, the plurality of sample images are obviously different from the images that should be at the second positioning point. Therefore, the plurality of sample images can be considered not to be the image of the reference object compared to the second positioning point. After the recognition model is trained using the multiple positioning images identified as belonging to the reference object and the multiple sample images identified as not belonging to the reference object, the multiple first recognition images, the multiple second recognition images, the multiple third recognition images, and the multiple fourth recognition images of the four objects to be recognized can be input into the recognition model. After inputting 20 first recognition images, 20 second recognition images, 20 third recognition images, and 20 fourth recognition images into the recognition model, the recognition results shown in Table 1 below were obtained:

[0086] Table 1

[0087] The confidence value described in the table is the judgment result after the recognition image is input into the recognition model. The closer it is to 100%, the higher the probability that the recognition model believes that the object to be recognized is the reference object. The farther it is from 100%, the lower the probability that the recognition model believes that the object to be recognized is the reference object.

[0088] As shown in Table 1, even though the user does not know the serial numbers of the four objects to be identified, or even whether they are genuine, the first test still results in the user identifying all four objects as prints numbered A, B, C, and D. However, there is only one print numbered A. In other words, the first test cannot correctly identify whether the four objects are prints numbered A.

[0089] In a second experiment, since the sampling device 20 only has a plurality of sample images and a full image of the print work numbered A, the recognition method, using recognition method 700, can use the plurality of sample images as the plurality of positioning images and set them as images of the reference object, and the plurality of expanded images within the plurality of reference images can be set as images other than the reference object. Figure 10A is a schematic diagram of a plurality of sample images previously acquired by the sampling device 10 on the print work numbered A according to an exemplary embodiment of the present invention. Figure 10B is a schematic diagram of a plurality of expanded images generated by adjusting the plurality of sample images according to adjustment data according to an exemplary embodiment of the present invention. After the recognition model is trained using the plurality of positioning images identified as images of the reference object and the plurality of expanded images identified as images other than the reference object, the first, second, third, and fourth recognition images of the four objects to be recognized can be input into the recognition model. After inputting 20 first recognition images, 20 second recognition images, 20 third recognition images, and 20 fourth recognition images into the recognition model, the recognition results shown in Figure 11 and Table 2 below were obtained. Figure 11 is a schematic diagram showing the distribution of confidence values ​​for 80 recognition images tested in the second experiment according to an exemplary embodiment of the present invention.

[0090] Table 2

[0091] As can be seen from the results in Figure 11 and Table 2, although the user does not know the serial numbers of the four objects to be identified, or even whether the four objects to be identified are authentic, when the four objects to be identified are actually prints numbered A, B, C, and D, the second test result clearly distinguishes that the first object to be identified should be the print numbered A, while the second, third, and fourth objects to be identified are not prints numbered A.

[0092] Therefore, as long as sampling has been performed on multiple items with a specific number beforehand, identification method 700 can accurately identify multiple items with that specific number from among the multiple items generated by the template, rather than mistaking all of the multiple items for the same print. Furthermore, because the differences between the multiple items are minimal and identification method 700 can already discern the differences between them, identification method 700 can also accurately identify whether the object to be identified is a genuine or counterfeit multiple item with that specific number. Furthermore, if sampling has been performed on multiple items with different numbers beforehand, identification method 700 can even more accurately identify the multiple items with the corresponding number from among the multiple items to be identified.

[0093] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining whether an object to be identified is a specific one of a plurality of objects generated using a template by an electronic device, the method comprising: Acquire a plurality of recognition images at a first positioning point of the object to be recognized; Acquire a plurality of reference images of the specific one of the plurality of objects as a reference object, wherein the plurality of reference images include: A plurality of positioning images at a second positioning point of the reference object, wherein the second positioning point of the reference object corresponds to the first positioning point of the object to be identified; and A plurality of first expanded images at the second positioning point of the reference object, wherein the plurality of first expanded images are generated by respectively adjusting adjustment data in a specific one of the plurality of positioning images, wherein the adjustment data is selected from a plurality of specific data, the plurality of specific data comprising: a specific color data, wherein the specific color data in the specific one of the plurality of positioning images is adjusted to form one of the plurality of first extended images; and a specific region data, wherein the specific region data in the specific one of the plurality of positioning images is adjusted to form another one of the plurality of first expanded images; and Based on the multiple recognition images and the multiple reference images, it is confirmed whether the object to be recognized is the specific one of the multiple objects.

2. The method according to claim 1, further comprising: receiving an entire image of the entire reference object provided by a server; and According to the first positioning point, the corresponding second positioning point is found from the overall image.

3. The method according to claim 1, further comprising: providing at least one of the plurality of recognition images and the first positioning point to a server; as well as The plurality of positioning images provided by the server are received.

4. The method according to claim 1, further comprising: Receive the plurality of positioning points on the second positioning point of the reference object provided by a server bit image; as well as The first positioning point of the object to be identified is determined according to the second positioning point of the reference object.

5. The method according to claim 3 or 4, further comprising: The plurality of first extended images provided by the server are received.

6. The method according to claim 1, wherein: When the number of the plurality of positioning images is N, the plurality of reference images include: a plurality of second extended images, ..., a plurality of N-1th extended images, and a plurality of Nth extended images; and N is a positive integer greater than or equal to 1.

7. An electronic device for confirming whether an object to be identified is a specific one of a plurality of plural objects generated using a template, the electronic device comprising: processor; as well as a storage device, the storage device being coupled to the processor and storing a plurality of instructions, which when executed by the processor cause the electronic device to: Acquire a plurality of recognition images at a first positioning point of the object to be recognized; Acquire a plurality of reference images of the specific one of the plurality of objects as a reference object, wherein the plurality of reference images include: A plurality of positioning images at a second positioning point of the reference object, wherein the second positioning point of the reference object corresponds to the first positioning point of the object to be identified; and A plurality of first expanded images at the second positioning point of the reference object, wherein the plurality of first expanded images are generated by respectively adjusting adjustment data in a specific one of the plurality of positioning images, wherein the adjustment data is selected from a plurality of specific data, the plurality of specific data comprising: a specific color data, wherein the specific color data in the specific one of the plurality of positioning images is adjusted to form one of the plurality of first extended images; and a specific region data, wherein the specific region data in the specific one of the plurality of positioning images is adjusted to form another one of the plurality of first expanded images; as well as Based on the multiple recognition images and the multiple reference images, it is confirmed whether the object to be recognized is the specific one of the multiple objects.

8. The electronic device according to claim 7, wherein: The plurality of instructions, when executed by the processor, further cause the electronic device to: receiving an entire image of the entire reference object provided by a server; and According to the first positioning point, the corresponding second positioning point is found from the overall image.

9. The electronic device according to claim 7, wherein: The plurality of instructions, when executed by the processor, further cause the electronic device to: providing at least one of the plurality of recognition images and the first positioning point to a server; and The plurality of positioning images provided by the server are received.

10. The electronic device according to claim 7, wherein: The plurality of instructions, when executed by the processor, further cause the electronic device to: receiving the plurality of positioning images at the second positioning point of the reference object provided by a server; and The first positioning point of the object to be identified is determined according to the second positioning point of the reference object.

Images