Cooked product identification support device, cooked product identification support method, cooked product identification support program, and recording medium

Abstract

To provide a cooked product identification support device capable of easily identifying cooked products.SOLUTION: A cooked product identification support device of the present disclosure includes an image obtaining unit and a cooked product identification unit. The image obtaining unit obtains a cooked product image in which a cooked product is captured. The cooked product identification unit obtains container feature information of a container in which the cooked product is contained based on the cooked product image, and identifies a type of the cooked product based on the container feature information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a cooking product identification assistance device, a cooking product identification assistance method, a cooking product identification assistance program, and a recording medium. [Background technology]

[0002] In recent years, the ready-meal industry, which provides so-called ready-meals such as boxed lunches and side dishes, has been attracting attention. In the ready-meal industry, various cooked dishes need to be produced by unspecified employees, so an appropriate cooking support system is required from the viewpoint of quality control. For example, Patent Document 1 describes a cooking support method in a cooking support system in which a server device that stores information related to cooking recipes and a terminal device having a display are connected via a network. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-068785 A Summary of the Invention [Problem to be solved by the invention]

[0004] The system described in Patent Document 1 is only capable of displaying recipes for cooking products, and is unable to confirm whether the cook has used appropriate ingredients and tools. Therefore, it is conceivable to use image recognition by AI to identify finished cooking products. However, even if the type of cooking product is the same, the appearance of each cooking product made at a different cooking site varies greatly, and there is a problem that a classifier trained using images from an existing library cannot achieve the accuracy of identification.

[0005] Therefore, an object of the present disclosure is to provide a cooking item identification assistance device, a cooking item identification assistance method, a cooking item identification assistance program, and a recording medium that can easily identify cooking items. [Means for solving the problem]

[0006] In order to achieve the above object, the cooking product identification assistance device of the present disclosure includes: The cooking device includes an image acquisition unit and a cooking product identification unit, The image acquisition unit acquires a cooked item image obtained by capturing an image of the cooked item, The cooked product identification unit acquires container characteristic information of a container in which the cooked product is contained based on the cooked product image, and identifies the type of the cooked product based on the container characteristic information.

[0007] The cooking product identification support method of the present disclosure includes: The method includes an image acquisition step and a cooking product identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product, The cooking product identifying step includes acquiring container characteristic information of a container in which the cooking product is accommodated based on the cooking product image, and identifying the type of the cooking product based on the container characteristic information.

[0008] The cooking product identification assistance program of the present disclosure is An image acquisition step and a food item identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product; The cooking product identification step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; The program is for causing a computer to execute each of the above procedures.

[0009] The recording medium of the present disclosure includes: An image acquisition step and a food item identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product; The cooking product identification step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; A computer-readable recording medium having recorded thereon a cooking product identification assistance program for causing a computer to execute each of the above procedures. Effect of the Invention

[0010] According to the present disclosure, cooked items can be easily identified. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing a configuration of an example of a cooking product identification assistance device according to the present disclosure. [Diagram 2] FIG. 2 is a block diagram showing an example of a hardware configuration of the cooking product identification assistance device of the present disclosure. [Diagram 3] FIG. 3 is a flowchart showing an example of processing in the cooking product identification assistance device of the present disclosure. [Figure 4] FIG. 4 is a block diagram showing a configuration of an example of a cooking product identification assistance device according to the present disclosure. [Diagram 5] FIG. 5 is a flowchart showing an example of processing in the cooking product identification assistance device of the present disclosure. [Figure 6] FIG. 6 is a block diagram showing a configuration of an example of a cooking product identification assistance device according to the present disclosure. [Figure 7] FIG. 7 is a flowchart showing an example of processing in the cooking product identification assistance device of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] In the present disclosure, the "cooked food" is not particularly limited, and examples thereof include prepared foods, bento boxes, meals served at restaurants, etc.

[0013] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiment. In each of the drawings, the same parts are given the same reference numerals. In addition, the description of each embodiment can be mutually incorporated unless otherwise specified, and the configurations of each embodiment can be combined unless otherwise specified. Can be obtained.

[0014] [Embodiment 1] Fig. 1 is a block diagram showing an example of the configuration of a cooking product identification support device 10 (hereinafter also referred to as "the device") according to the present embodiment. As shown in Fig. 1, the device 10 includes an image acquisition unit 11 and a cooking product identification unit 12. In addition, the device 10 may include, for example, an input unit, an output unit, a display unit, and / or a storage unit, although these are not shown.

[0015] The device 10 may be, for example, a single device including each of the above-mentioned units, or a device to which each of the above-mentioned units can be connected via a communication line network. The device 10 may be connected to an external device described later via the communication line network. The communication line network is not particularly limited, and a known network may be used, for example, wired or wireless. Examples of the communication line network include an Internet line, a World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), a Local 5G, and a LPWA. Examples of the wireless communication may include a form in which each device directly communicates with each other (Ad Hoc communication), an infrastructure (infrastructure communication), and an indirect communication via an access point. The device 10 may be incorporated into a server as a system, for example. Furthermore, the present device 10 may be, for example, a personal computer (PC, for example, desktop type or notebook type) in which the program of the present disclosure is installed, a smartphone, a tablet terminal, etc. The present device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the above-mentioned units is on a server and the other units are on a terminal.

[0016] 2 illustrates a block diagram of a hardware configuration of the present device 10. The present device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, an output device 106, a communication device 107, etc. Each part of the present device 10 is connected to each other via the bus 103 by each interface (I / F).

[0017] The central processing unit 101 cooperates with other components through a controller (such as a system controller or an I / O controller) and is responsible for overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein and other programs, and also reads and writes various information. Specifically, for example, the central processing unit 101 functions as an image acquisition unit 11 and a cooking product identification unit 12. The device 10 may include, as a computing device, other computing devices such as a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or a combination of these.

[0018] The bus 103 can also be connected to, for example, an external device. Examples of the external device include a user terminal, an external storage device (external database, etc.), a printer, an external input device, an external display device, an external imaging device, etc. The present device 10 can be connected to an external network (the above-mentioned communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0019] An example of the memory 102 is a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operation programs, such as the program of the present disclosure, stored in the storage device 104 described below, and the central processing unit 101 receives data from the memory 102 and executes the program. The main memory is, for example, a RAM (random access memory). The memory 102 may also be, for example, a ROM (read only memory).

[0020] The storage device 104 is also called an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operating program including the program of the present disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive that reads and writes from the recording medium. The recording medium is not particularly limited, and may be, for example, an internal or external type, such as a hard disk (HD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

[0021] In the present device 10, the memory 102 and the storage device 104 can store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used when the present device 10 executes processing. In this case, the memory 102 and the storage device 104 may store, for example, the above-mentioned information of the user of the present device. Note that at least a part of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be distributed and stored in a plurality of terminals using blockchain technology or the like. In addition, when the present device 10 includes the storage unit, for example, the memory 102 and the storage device 104 function as the storage unit. The storage unit can store, for example, a cooking item image, container characteristic information, a storage position, a cooking item type, an identification database described later, and the like.

[0022] The device 10 further includes, for example, an input device 105 and an output device 106. The input device 105 may be, for example, a pointing device such as a touch panel, a track pad, or a mouse; a keyboard; an imaging means such as a camera or a scanner; a card reader such as an IC card reader or a magnetic card reader; and an audio input means such as a microphone. The output device 106 may be, for example, a display device such as an LED display or a liquid crystal display; an audio output device such as a speaker; a printer; or the like. In the first embodiment, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated device, such as a touch panel display.

[0023] Next, an example of the cooking product identification support method of this embodiment will be described with reference to the flowchart of Fig. 3. The cooking product identification support method of this embodiment is implemented as follows, for example, using the device 10 of Fig. 1 or Fig. 2. Note that the cooking product identification support method of this embodiment is not limited to use of the device 10 of Fig. 1 or Fig. 2.

[0024] The image acquisition unit 11 acquires a cooked item image in which a cooked item is captured (S1, image acquisition step). The cooked item image may be, for example, a still image or a video. When the cooked item image is a video, the video may include, for example, audio data described below. The image acquisition unit 11 may acquire the cooked item image from, for example, an imaging device (for example, a camera, a video camera, an information processing terminal with a camera function (for example, a smartphone, a tablet terminal, a notebook computer, etc.)) that captures the cooked item, or may acquire the cooked item image from a recording medium (for example, various storage devices, server devices, databases, etc.) that stores the cooked item image, or may acquire the cooked item image captured by an input device (for example, an imaging device) 105 of the device 10. The image acquisition unit 11 may record the acquired cooked item image in a storage unit of the device 10.

[0025] The cooking product identification unit 12 acquires container characteristic information of the container in which the cooking product is contained based on the cooking product image (S2A), and identifies the type of the cooking product based on the container characteristic information (S2B) (S2, cooking product identification step). The container characteristic information is not particularly limited as long as it is information about the appearance of the container, and examples of the information include the color, shape, size, presence or absence of partitions, presence or absence of patterns, and type of patterns of the container; the shape of the partitions of the container, the number of partitions, the size of the partitions, and the color of the partitions. For example, the cooking product identification unit 12 can first acquire the container characteristic information of the container in which the cooking product is contained based on the cooking product image by using a known image analysis method. The known image analysis method is not particularly limited, and may be, for example, a method using machine learning or a method not using machine learning. As a specific example of the known image analysis method, for example, a known image analysis library such as OpenCV can be used to detect the container included in the cooking product image, and the container characteristic information of the detected container can be acquired. Furthermore, the cooked product identification unit 12 may acquire the container characteristic information by, for example, image analysis using a machine-learned model (also referred to as a container characteristic model) that has learned the relationship between the cooked product image and the container characteristic information contained in the cooked product image. In this case, the cooked product identification unit 12 can acquire the container characteristic information by, for example, inputting the cooked product image into the machine-learned model. The machine-learned model may be a component of the present device 10, or may be a component external to the present device 10. In the latter case, the cooked product identification unit 12 can acquire the container characteristic information by, for example, transmitting the acquired cooked product image to the machine-learned model, which is the external component, and receiving a determination result by the machine-learned model.

[0026] The container feature model includes, for example, an input layer for inputting a cooked product image, an output layer for outputting the container feature information, and at least one intermediate layer provided between the input layer and the output layer. The container feature model may be a program module that is a part of artificial intelligence software. An example of the multi-layered network is a neural network. An example of the neural network is a convolution neural network (CNN), but is not limited to CNN, and may be a trained model constructed with other learning algorithms such as a neural network other than CNN, a support vector machine (SVM), a Bayesian network, or a regression tree.

[0027] The container feature model may be, for example, a trained model generated in advance. The trained model may also be a trained model (derived model) that is retrained using teacher data and a trained model that has already been generated. Furthermore, the trained model may be a trained model obtained by transfer learning using a trained model generated using teacher data, or a trained model generated by model compression of a trained model generated using teacher data.

[0028] Next, the cooked product identification unit 12 can identify the type of cooked product by using, for example, the acquired container characteristic information and an identification database. The identification database is, for example, a database in which the type of cooked product is linked to each piece of container characteristic information. The identification database may be, for example, the memory unit of the device 10, or a recording medium external to the device 10. The cooked product identification unit 12 can identify, for example, the type of cooked product linked to the acquired container characteristic information in the identification database as the type of cooked product included in the cooked product image.

[0029] For example, even if the type of cooked food is the same, the appearance of each cooked food made at a different cooking site may be significantly different, and a classifier trained using images from an existing library may not be able to achieve the required accuracy in classification. In addition, even if the cooked food is the same, the appearance of the cooked food itself may be slightly different for each cooked food, and the image recognition may not be accurate. Therefore, the present disclosure focuses on the characteristics of the container in which the cooked food is placed, links the container characteristic information with the type of cooked food, and can identify the type of cooked food based on the container characteristic information acquired based on the cooked food image.

[0030] The cooking product identification unit 12 may estimate, for example, the storage position of at least one element constituting the cooking product in the container based on the cooking product image, and identify the type of the cooking product based on the container characteristic information and the storage position. The element is, for example, a member constituting the cooking product. The element may be, for example, food or non-food. When the cooking product is a lunch box, specific examples of the element include various types of food such as staple food, main dish, and side dish; decorations such as loose leaf; seasonings such as soy sauce and sauce (and their containers); small containers independent of the lunch box container (for example, cups, etc.); and the like. The cooking product identification unit 12 may, for example, first detect at least one element constituting the cooking product based on the cooking product image by a known image analysis method. The known image analysis method is not particularly limited, and may be, for example, one that utilizes machine learning or one that does not utilize machine learning. A specific example of the known image analysis method is, for example, one that uses a known image analysis library such as OpenCV to detect the cooking product elements included in the cooking product image. Furthermore, the cooking item identification unit 12 may detect the cooking item elements contained in the cooking item image by, for example, image analysis using a machine-learned model (also referred to as a container feature model, for example) that has learned the relationship between the cooking item image and the cooking item elements contained in the cooking item image. In this case, the cooking item identification unit 12 can detect the cooking item elements contained in the cooking item image by, for example, inputting the cooking item image into the machine-learned model. The machine-learned model may be a configuration of the present device 10, or may be a configuration external to the present device 10. In the latter case, the cooking item identification unit 12 can detect the cooking item elements contained in the cooking item image by, for example, transmitting the acquired cooking item image to the machine-learned model, which is the external configuration, and receiving a determination result by the machine-learned model.

[0031] The container feature model includes, for example, an input layer for inputting a cooked item image, an output layer for outputting an element of the cooked item included in the cooked item image, and at least one intermediate layer provided between the input layer and the output layer. The container feature model may be a program module that is a part of artificial intelligence software. An example of the multi-layered network is a neural network. An example of the neural network is a convolution neural network (CNN), but is not limited to CNN, and may be a trained model constructed by other learning algorithms such as a neural network other than CNN, a support vector machine (SVM), a Bayesian network, or a regression tree.

[0032] The container feature model may be, for example, a trained model generated in advance. The trained model may also be a trained model (derived model) that is retrained using teacher data and a trained model that has already been generated. Furthermore, the trained model may be a trained model obtained by transfer learning using a trained model generated using teacher data, or a trained model generated by model compression of a trained model generated using teacher data.

[0033] Next, the cooking product identification unit 12 estimates, for example, the placement position of the detected element in a container. For example, the cooking product identification unit 12 identifies the shape of the container based on the container characteristic information of the detected container, and can estimate that the place in the cooking product image where the element is detected in the shape of the container is the placement position of the element in the container.

[0034] Next, the cooking product identification unit 12 can identify the type of cooking product by using, for example, the acquired container characteristic information, the storage position, and an identification database. The identification database is, for example, a database in which the storage position of an element is linked to the type of cooking product corresponding to each piece of container characteristic information. The identification database may be, for example, the storage unit of the present device 10, or a recording medium outside the present device 10. The cooking product identification unit 12 can identify, for example, the type of cooking product linked to the acquired container characteristic information and the arrangement position of the element in the identification database as the type of cooking product included in the cooking product image. According to this process, for example, even if the same container is shared by multiple types of cooking products, the cooking products can be identified.

[0035] The cooked product identification assistance device of the present disclosure can acquire a cooked product image by an image acquisition unit, acquire container characteristic information of a container in which the cooked product is stored based on the cooked product image by a cooked product identification unit, and identify the type of the cooked product based on the container characteristic information. Thus, according to the present disclosure, cooked products can be easily identified.

[0036] [Embodiment 2] The second embodiment is another example of a cooking product identification assistance device of the present disclosure.

[0037] FIG. 4 is a block diagram showing an example of the configuration of the cooking product identification support device 10A. As shown in FIG. 4, the cooking product identification support device 10A includes a label output unit 13 in addition to the configuration of the cooking product identification support device 10 of the first embodiment. The hardware configuration of the cooking product identification support device 10A is the same as that of the cooking product identification support device 10 of FIG. 2, except that the central processing unit 101 has the configuration of the cooking product identification support device 10A of FIG. 4 instead of the configuration of the cooking product identification support device 10 of FIG. 1. The process of the label output unit 13 will be described below. The process of the label output unit 13 can be inserted at any position in the flowchart of FIG. 3 described in the first embodiment, but it is preferable to insert it after S2 as shown in FIG. 5.

[0038] First, steps S1 and S2 are executed in the same manner as in the first embodiment.

[0039] The label output unit 13 outputs, for example, label information of the identified object (S11, label output step). The label information is, for example, information for identifying the label of the object. The label information may include, for example, information for printing the label of the object. Specifically, the label information may include, for example, information such as the product name, manufacturing date, ingredients, expiration date, best before date, and manufacturing location. The label output unit 13 can output the label information to, for example, a printer for printing a label.

[0040] The cooking product identification support device of this embodiment can output label information of the identified cooking product by, for example, a label output unit. Therefore, the cooking product identification support device of this embodiment can print a label of the identified object, for example. When a pre-printed label is attached to a finished product at a manufacturing site for cooking products such as boxed lunches, the attached label may differ from the label that should have been attached. The cooking product identification support device of this embodiment can print a label corresponding to the identified cooking product. Therefore, the cooking product identification support device of this embodiment can also reduce, for example, mistakes in attaching labels to cooking products.

[0041] [Embodiment 3] The third embodiment is another example of the cooking product identification assistance device of the present disclosure.

[0042] FIG. 6 is a block diagram showing an example of the configuration of the cooking product identification support device 10B. As shown in FIG. 6, the cooking product identification support device 10B includes a cooking product counting unit 14 in addition to the configuration of the cooking product identification support device 10 of the first embodiment. The hardware configuration of the cooking product identification support device 10B is the same as that of the cooking product identification support device 10 of FIG. 2, except that the central processing unit 101 has the configuration of the cooking product identification support device 10B of FIG. 6 instead of the configuration of the cooking product identification support device 10 of FIG. 1. The processing of the cooking product counting unit 14 will be described below. The processing of the cooking product counting unit 14 can be inserted at any position in the flowchart of FIG. 3 described in the first embodiment, for example, but is preferably inserted after S2 as shown in FIG. 7.

[0043] First, steps S1 and S2 are executed in the same manner as in the first embodiment.

[0044] The cooked item counting unit 14, for example, counts the identified cooked items by type (S21, cooked item counting step). The cooked item counting unit 14 can perform the counting by, for example, using a known image analysis method. The image analysis method is, for example, as described above. Specifically, the cooked item counting unit 14 can count the cooked items by type by, for example, generating a bounding box in the cooked item image for each type of cooked item identified in S2B and counting the number of generated bounding boxes.

[0045] The device 10B may output, for example, the counting result. The output may be output to the output device 106 of the device 10, or may be output to the outside of the device 10B.

[0046] When the present device 10B includes, for example, the label output unit 13 of the second embodiment, the label output unit 13 may output, for example, label information for each type and for the counting results.

[0047] The cooking product identification support device of this embodiment can, for example, count the identified cooking products by type using a cooking product counting unit. When cooking products are simply counted by image recognition processing, for example, if an image contains a cooking product with ingredients forgotten to be served, multiple cooking products may be mistakenly counted as one cooking product. According to the cooking product identification support device of this embodiment, since it is possible to count each cooking product whose type is identified by the cooking product identification unit, it is possible to reduce the possibility of miscounting and count cooking products with higher accuracy.

[0048] [Embodiment 4] The program of the present embodiment is a program for causing a computer to execute each step of the cooking product identification support method described above. Specifically, the program of the present embodiment is a program for causing a computer to execute the image acquisition unit and the cooking product identification procedure.

[0049] The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product; The cooking product identification step includes acquiring container characteristic information of a container in which the cooking product is accommodated based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information.

[0050] The program of the present embodiment can also be said to be a program that causes a computer to function as an image acquisition procedure and a cooking product identification procedure.

[0051] The program of the present embodiment may be implemented by using the description of the cooking product identification support device and cooking product identification support method of the present disclosure. For example, the "procedure" in each of the steps may be replaced with "processing". The program of the present embodiment may be recorded in a computer-readable recording medium. The recording medium may be a non-transitory computer-readable storage medium. The recording medium may be a non-transitory computer-readable storage medium. The recording medium is not particularly limited, and may be, for example, a random access memory (RAM), a read-only memory (ROM), a hard disk (HD), a flash memory (e.g., a solid state drive (SSD), a USB flash memory, an SD / SDHC card, etc.), an optical disk (e.g., a CD-R / CD-RW, a DVD-R / DVD-RW, a BD-R / BD-RE, etc.), a magneto-optical disk (MO), a floppy disk (FD), etc. Furthermore, the cooking item identification assistance program of this embodiment (e.g., also referred to as a programming product or a cooking item identification assistance program product) may be in a form distributed from an external computer, for example. The "distribution" may be, for example, distribution via a communication network, or distribution via a device connected by wire. The cooking item identification assistance program of this embodiment may be installed and executed on the device to which it is distributed, or may be executed without being installed.

[0052] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-mentioned embodiments. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be appropriately combined with other embodiments.

[0053] <Additional Notes> A part or all of the above-described embodiments can be described as follows, but is not limited to the following. (Appendix 1) The cooking device includes an image acquisition unit and a cooking product identification unit, The image acquisition unit acquires a cooked item image obtained by capturing an image of the cooked item, The cooked product identification unit obtains container characteristic information of a container in which the cooked product is contained based on the cooked product image, and identifies the type of the cooked product based on the container characteristic information. (Appendix 2) The cooking product identification unit includes: estimating a location of at least one element of the food product in the container based on the food product image; A cooking item identification assistance device as described in Appendix 1, which identifies the type of cooking item based on the container characteristic information and the storage position. (Appendix 3) A label output unit is included. 3. The cooking item identification assistance device according to claim 1 or 2, wherein the label output unit outputs label information of the identified cooking item. (Appendix 4) Including a food counting unit, 4. The cooking product identification support device according to claim 1, wherein the cooking product counting unit counts the identified cooking products by type. (Appendix 5) The method includes an image acquisition step and a cooking product identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product, The cooking product identifying step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; A cooking product identification support method, wherein each of the steps is executed by a computer. (Appendix 6) The cooking product identification step includes: estimating a location of at least one element of the food product in the container based on the food product image; A cooking item identification support method as described in Appendix 5, which identifies the type of cooking item based on the container characteristic information and the storage position. (Appendix 7) A label output process is included. 7. The cooking item identification support method according to claim 5, wherein the label output step outputs label information of the identified cooking item. (Appendix 8) Including a cooking product counting process; The cooking item identification support method according to any one of appendices 5 to 7, wherein the cooking item counting step counts the identified cooking items by type. (Appendix 9) An image acquisition step and a food item identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product; The cooking product identification step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; A cooking product identification support program for causing a computer to execute each of the above steps. (Appendix 10) The cooking product identification procedure includes: estimating a location of at least one element of the food product in the container based on the food product image; A cooking item identification assistance program according to claim 9, which identifies the type of cooking item based on the container characteristic information and the storage position. (Appendix 11) Includes label output procedures, 11. The cooking item identification support program according to claim 9 or 10, wherein the label output step outputs label information of the identified cooking item. (Appendix 12) Includes a food counting procedure, 12. The cooking item identification support program according to any one of appendices 9 to 11, wherein the cooking item counting step counts the identified cooking items by type. (Appendix 13) An image acquisition step and a food item identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product; The cooking product identification step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; A computer-readable recording medium having recorded thereon a cooking product identification assistance program for causing a computer to execute each of the above procedures. (Appendix 14) The cooking product identification procedure includes: estimating a location of at least one element of the food product in the container based on the food product image; The recording medium of claim 13, further comprising: a container characteristic information storage section for storing the food product; (Appendix 15) Includes label output procedures, 15. The recording medium according to claim 13 or 14, wherein the label output step outputs label information of the identified food item. (Appendix 16) Includes a food counting procedure, 16. The recording medium according to any one of appendices 13 to 15, wherein the step of counting the cooked items includes counting the identified cooked items by type. [Industrial Applicability]

[0054] According to the present disclosure, cooked products can be easily identified. Therefore, the present disclosure is widely useful in the food industry field. [Explanation of symbols]

[0055] 10. Cooking product identification assistance device 11 Image acquisition section 12 Cooked food identification section 13 Label output section 14 Cooked Food Counting Section 101 Central Processing Unit 102 Memory 103 Bus 104 Storage device 105 Input Device 106 Output Device 107 Communication Devices

Claims

1. The cooking device includes an image acquisition unit and a cooking product identification unit, The image acquisition unit acquires a cooked item image obtained by capturing an image of the cooked item, The cooked product identification unit obtains container characteristic information of a container in which the cooked product is contained based on the cooked product image, and identifies the type of the cooked product based on the container characteristic information.

2. The cooking product identification unit includes: estimating a location of at least one element of the food product in the container based on the food product image; The cooking product identification assistance device according to claim 1 , further comprising: identifying the type of the cooking product based on the container characteristic information and the storage position.

3. A label output unit is included. The cooking item identification assistance device according to claim 1 , wherein the label output unit outputs label information of the identified cooking item.

4. Including a food counting unit, The cooking product identification assisting device according to claim 1 , wherein the cooking product counting unit counts the identified cooking products by type.

5. The method includes an image acquisition step and a cooking product identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product, The cooking product identifying step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; The cooking product identification support method, in which each of the above steps is executed by a computer.

6. The cooking product identification step includes: estimating a location of at least one element of the food product in the container based on the food product image; The method for assisting in identifying a cooked product according to claim 5 , further comprising identifying a type of the cooked product based on the container characteristic information and the storage position.

7. A label output process is included.

7. The cooking product identification support method according to claim 5, wherein the label output step outputs label information of the identified cooking product.

8. Includes a cooking product counting process, The cooking product identification support method according to claim 5 or 6, wherein the cooking product counting step counts the identified cooking products by type.

9. An image acquisition step and a food item identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product; The cooking product identification step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; A cooking product identification support program for causing a computer to execute each of the above steps.

10. An image acquisition step and a food item identification step, The image acquisition step acquires an image of a cooked product by capturing an image of the cooked product; The cooking product identification step includes acquiring container characteristic information of a container in which the cooking product is contained based on the cooking product image, and identifying a type of the cooking product based on the container characteristic information; A computer-readable recording medium having recorded thereon a cooking product identification assistance program for causing a computer to execute each of the above procedures.

Images