Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device capable of estimating a region of a target object to be annotated, an information processing method, and an information processing program.SOLUTION: An information processing device comprises: an acquisition unit which acquires an image; an acceptance unit which accepts instruction contents related to a target object; an estimation unit which estimates a region of the target object recorded in the image acquired by the acquisition unit on the basis of the instruction contents accepted by the acceptance unit; and an output control unit which controls the output of the region of the target object estimated by the estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] There is a conventional device that supports image annotation. The device classifies multiple target regions of a target image that are candidates for annotation based on features that appear in the target image, and generates classification information. The device visualizes the classification information and displays it in a manner that allows it to be compared with the target image. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-131937 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the task of adding annotations is time-consuming, and there is a need to automatically identify the target to which annotations should be added.

[0005] The present disclosure provides an information processing device, an information processing method, and an information processing program capable of estimating a region of a target object to be annotated. [Means for solving the problem]

[0006] An information processing device of one embodiment includes an acquisition unit that acquires an image, a reception unit that receives instructions regarding a target object, an estimation unit that estimates the area of ​​the target object to be recorded in the image acquired by the acquisition unit based on the instructions received by the reception unit, and an output control unit that controls the area of ​​the target object estimated by the estimation unit to be output. [Effects of the Invention]

[0007] The information processing device, information processing method, and information processing program disclosed herein can estimate the area of ​​a target object to be annotated. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 10 is a diagram illustrating a screen displayed based on control of an information processing device according to an embodiment. [Figure 2] FIG. 1 is a block diagram illustrating an information processing device according to an embodiment. [Figure 3] FIG. 2 is a diagram for explaining an example of an image (image information). [Figure 4] 4 is a diagram for explaining an example of instruction contents (prompts) regarding the image (image information) illustrated in FIG. 3. FIG. [Figure 5] 1 is a flowchart illustrating an information processing method according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] An embodiment will be described below.

[0010] [Overview of information processing device 100] First, an overview of an information processing device 100 according to an embodiment will be described. FIG. 1 is a diagram for explaining a screen displayed under the control of an information processing device 100 according to an embodiment.

[0011] The information processing device 100 may be configured as an estimation device (identification device) or the like that estimates (identifies) the area of ​​an object (target object) that is recorded in the image 200 and corresponds to a character string of instruction content 141 (prompt) that has been received. The information processing device 100 may also be configured as an annotation device or the like that adds an annotation to the area of ​​the estimated object (target object). The information processing device 100 is not limited to the device described above as an example, and may be configured as various other devices. The information processing device 100 may be a computer such as a server, a desktop, a laptop, a tablet, or a smartphone.

[0012] The information processing device 100 acquires an image 200. The image 200 records one or more objects. 1, the information processing device 100 acquires an image 200 recording a road and a vehicle (for example, an ordinary car 201, a truck 202, or other object) traveling on the road, and displays the image 200 on the display unit 133. The information processing device 100 also displays an interface (for example, an input interface 140 using chat) for inputting instructions (prompts) to the information processing device 100 on the display unit 133.

[0013] The information processing device 100 receives, for example, a prompt 141 in the form of a character string (text) or the like via the input interface 140. The character string may have various contents, and may also be the contents of an object (target object) whose area is to be estimated. That is, as an example, when objects recorded in an image 200 include a passenger car 201 and a truck 202, and the area of ​​the passenger car 201 in the image 200 is to be estimated, the information processing device 100 receives the character string "passenger car" (prompt) 141.

[0014] The information processing device 100 estimates the area of ​​the target object recorded in the image 200 based on the instruction content 141 received as described above. The information processing device 100 estimates the area of ​​the object (target object) recorded in the image 200 and corresponding to the instruction content 141 by using various processes including, for example, well-known object recognition processes. As an example, when there are a passenger car 201 and a truck 202 as objects recorded in the image 200 and the area of ​​the passenger car 201 in the image 200 is to be estimated, the information processing device 100 receives the character string "passenger car" (instruction content) 141 and estimates (identifies) the area of ​​the passenger car 201 (target object) in the image 200. In this case, the information processing device 100 may identify an area of ​​the target object having a size equal to or greater than a threshold value based on the size of the estimated (identified) area of ​​the target object and a threshold value input via the input interface 140.

[0015] The information processing device 100 outputs the region of the target object estimated (identified) as described above. For example, the information processing device 100 displays the region of the target object in the image 200 in a color different from that of other regions. In the example shown in FIG. 1, the region of the target object (a standard-sized car 201) in the image 200 is shaded, and other regions (e.g., a truck 202, etc.) are not shaded, so that the region of the target object (a standard-sized car 201) is displayed in a different manner from other regions (e.g., a truck 202, etc.).

[0016] Furthermore, the information processing device 100 annotates the region of the target object identified in the image 200 based on the instruction content 141 (prompt) of the character string received as described above. That is, in the example shown in Fig. 1, the information processing device 100 may identify the region of the target object (standard-sized car 201) marked with diagonal lines based on "standard-sized car" input as the character string (instruction content 141 (prompt)), and may annotate the region of the target object (standard-sized car 201) with "standard-sized car." Note that the objects to which annotations are added are not limited to the passenger car 201 and the truck 202, but may be various other objects.

[0017] [Details of the information processing device 100] Next, the information processing device 100 according to an embodiment will be described in detail. FIG. 2 is a block diagram illustrating the information processing device 100 according to an embodiment. FIG. 3 is a diagram for explaining an example of an image 200 (image information). FIG. 4 is a diagram for explaining an example of instruction contents (prompts) regarding the image 200 (image information) illustrated in FIG.

[0018] The information processing device 100 includes, for example, an input unit 121, a communication unit 131, a storage unit 132, a display unit 133, and a control unit 110. The communication unit 131, the storage unit 132, and the display unit 133 may be an embodiment of an output unit. The control unit 110 includes, for example, an acquisition unit 111, a reception unit 112, an estimation unit 113, an output control unit 114, a learning unit 115, and an AI unit 116. The control unit 110 may be configured, for example, by an arithmetic processing unit of the information processing device 100. The control unit 110 (for example, an arithmetic processing unit) may realize the functions of each unit (for example, the acquisition unit 111, the reception unit 112, the estimation unit 113, the output control unit 114, the learning unit 115, and the AI ​​unit 116) by, for example, appropriately reading and executing various programs stored in the storage unit 132. That is, the functions of each unit may be realized by computer implementation.

[0019] The input unit 121 may be, for example, a graphical user interface (GUI), etc. Alternatively, the input unit 121 may be, for example, a keyboard and a mouse, etc.

[0020] The communication unit 131 is, for example, a communication interface that can transmit and receive various information to and from a device (external device) (not shown) outside the information processing device 100.

[0021] The storage unit 132 may store, for example, various information and programs. Examples of the storage unit 132 may be a memory, a solid state drive, a hard disk drive, etc. Note that the storage unit 132 may be, for example, a storage area or a server on a cloud.

[0022] The display unit 133 is a display capable of displaying, for example, various characters, symbols, images, and the like.

[0023] The acquisition unit 111 acquires an image 200 (image information). The acquisition unit 111 acquires, for example, an image 200 (image information) stored in the storage unit 132. Furthermore, the acquisition unit 111 acquires the image 200 (image information) from an external device (not shown), for example, via the communication unit 131. The external device may be, for example, a server or a user terminal. The user terminal is a terminal used by a user of the information processing device 100, and may be a desktop, laptop, tablet, smartphone, or the like. In addition, for example, when an external memory (not shown) in which the image 200 (image information) is stored is connected to an interface (not shown) of the information processing device 100, the acquisition unit 111 may acquire the image 200 (image information) from the external memory. Image 200 may be a still image or a video image, and may capture one or more objects. As an example, as shown in FIG. 3, the acquisition unit 111 may acquire an image 200 (image information) that records a road and a vehicle (for example, an object such as a passenger car 201 and a truck 202) traveling on the road.

[0024] The receiving unit 112 receives, for example, instruction content 141 (prompt) regarding the target object via the input interface 140. The receiving unit 112 may receive, for example, instruction content 141 (prompt) in the form of a character string (text) regarding the target object. The character string (text) may be of various types, and may be, for example, the content of an object (target object) whose area is to be estimated. The input interface 140 may be, for example, an interface that receives instruction content 141 (prompt) using chat.

[0025] 4, for example, when objects recorded in an image 200 include a standard-sized car 201 and a truck 202, and the area of ​​the standard-sized car 201 in the image 200 is to be estimated, the information processing device 100 accepts a character string "standard-sized car" (instruction content) 141. In response to "standard-sized car" being input into the input interface 140, the information processing device 100 may display content 142 such as "I will infer" on the display unit 133 (input interface 140) as a chat reply for estimating the area of ​​the standard-sized car 201.

[0026] Similarly, as an example, when objects recorded in image 200 include a passenger car 201 and a truck 202 and the area of ​​truck 202 in image 200 is to be estimated, information processing device 100 accepts "truck" (instruction content) as a character string. Similarly, as an example, if the objects recorded in image 200 are a passenger car 201 and a truck 202, and the area of ​​both the passenger car 201 and the area of ​​the truck 202 in image 200 are to be estimated, the information processing device 100 may accept the character strings "passenger car" and "truck" (multiple instruction contents).

[0027] The estimation unit 113 estimates the area of ​​the target object recorded in the image 200 acquired by the acquisition unit 111, based on the instruction content 141 received by the reception unit 112. The estimation unit 113 estimates the area of ​​the object (target object) corresponding to the instruction content 141, recorded in the image 200, by using various processes including, for example, a known object recognition process. Using the example described above, that is, when the objects recorded in image 200 are a passenger car 201 and a truck 202 and the area of ​​passenger car 201 in image 200 is to be estimated, when estimation unit 113 receives the character string "passenger car" (instruction content) 141 (see Figure 4), it estimates (identifies) the area of ​​passenger car 201 (target object) in image 200. Similarly, as an example, when the objects recorded in image 200 are a passenger car 201 and a truck 202 and the area of ​​truck 202 in image 200 is to be estimated, when estimation unit 113 receives the character string "truck" (instruction content), it estimates (identifies) the area of ​​truck 202 (target object) in image 200. Similarly, as an example, if the objects recorded in image 200 are a passenger car 201 and a truck 202, and both the area of ​​passenger car 201 and the area of ​​truck 202 in image 200 are to be estimated, when information processing device 100 receives the character strings "passenger car" and "truck" (multiple instruction contents), it may estimate (identify) the areas of passenger car 201 and truck 202 (multiple target objects) in image 200. If the estimation is successful, the estimation unit 113 may display a chat reply with content 144 such as "Success" on the input interface 140. If the estimation is not successful, for example, the estimation unit 113 may display a chat reply with content such as "Please re-enter the prompt."

[0028] The estimation unit 113 may estimate at least one of a pixel region of the target object in the image 200 and a contour boundary of the target object as the region of the target object. That is, the estimation unit 113 may estimate at least one of a group of pixel regions and contour boundaries of the target object. The pixel region of the target object may be a surface region inside the contour of the target object. The contour boundary of the target object can be rephrased as the contour of the target object.

[0029] The estimation unit 113 may assign a value according to the size of the region of the target object. For example, the estimation unit 113 may assign a value of 100 to the largest region among the regions of the multiple target objects estimated as described above, assign a value of 0 to the smallest region, and assign a value greater than 0 and less than 100 to each region from the smallest region to the largest region according to the size of the region. Note that the values ​​(range of values) are not limited to the range from 0 to 100 described above, and various values ​​(various ranges of values) may be assigned.

[0030] As an example of specific processing performed by the estimation unit 113, the estimation unit 113 may first detect a rectangular area of ​​each object (target object) in the image 200 corresponding to the instruction content 141 (prompt) to generate a rectangular area image, and then perform contour approximation on the object in each rectangular area image to obtain the coordinates of the contour. In this case, the estimation unit 113 may generate the rectangular area image by, for example, processing using AI or the like. As an example of processing using AI or the like, the estimation unit 113 may, when a "reliability score" specifying the reliability of the object (target object), be input, identify the object (target object) to be estimated according to the reliability score, and generate an image of a rectangular area around the identified object (target object). As an example, the reliability score may be a value ranging from 0 to 1. Furthermore, the estimation unit 113 may surround the object (object contour) with a polygon based on the acquired coordinates and obtain the coordinate points of the polygon. The estimation unit 113 may obtain the coordinate points of the polygon for each of multiple objects as described above. That is, the estimation unit 113 may use the coordinate points of the polygon to estimate at least one of the contour (contour region) of the object and the interior of the object (pixel region of the object) inside the contour (contour region). Next, the estimation unit 113 scores the size of each of the polygons (areas enclosed by the coordinate points of the polygons) of each of the multiple objects based on the size of the largest polygon and the size of the smallest polygon. That is, as an example, the estimation unit 113 assigns a value between 0 and 100 to each of the multiple polygons depending on their size, assigning a value of 100 to the largest polygon and a value of 0 to the smallest polygon.

[0031] In addition, when the receiving unit 112 receives the instruction content 141, the estimation unit 113 may generate a different thread (i.e., page, etc.) for each instruction content 141 (for each type of target object) and generate an area of ​​one target object in each thread (each page).

[0032] The output control unit 114 controls the output unit to output the area of ​​the target object estimated by the estimation unit 113. Here, the output unit may be, for example, a communication unit 131, a storage unit 132, a display unit 133, etc.

[0033] As an example, when the estimation unit 113 estimates the areas of multiple target objects (e.g., passenger car 201 (or truck 202)) in image 200, the output control unit 114 may control the output unit to output in a manner that identifies the area of ​​the estimated passenger car 201 (or truck 202) (target object) in image 200.

[0034] As another example, when the estimation unit 113 estimates the areas of each of multiple target objects (e.g., passenger car 201 and truck 202) in image 200, the output control unit 114 may control the output unit so that when a character string (either "passenger car" or "truck") is input via the input interface 140, the output unit outputs the area of ​​the target object corresponding to the input character string, i.e., when "passenger car" (or "truck") is input as a character string for identifying the target object, the output unit outputs the area of ​​passenger car 201 (or truck 202) (target object) in image 200.

[0035] As another example, when the estimation unit 113 estimates the area of ​​a target object (e.g., passenger car 201) in the image 200 and then estimates the area of ​​a target object (e.g., truck 202) in the same image 200, the output control unit 114 may control the output unit to create a thread (e.g., a screen of passenger car 201 and a screen of truck 202) for each estimated target object (e.g., for each passenger car 201 and truck 202) and output one (one) or both (multiple) of the multiple threads. In this case, for example, when a character string (e.g., one of "passenger car" and "truck") is input via the input interface 140, or when one of the threads is selected via the input interface 140, the output control unit 114 may control the output unit to output the area of ​​the target object (e.g., passenger car 201 or truck 202) corresponding to the input character string or the selected thread. That is, the output control unit 114 may switch between displaying and hiding the multiple threads via the input interface 140.

[0036] The output control unit 114 may perform control to output a region of the target object having a size equal to or larger than an input value (threshold value). The output control unit 114 may perform control to identify a region of the target object having a value (region size) equal to or larger than a threshold value based on the value (size of the region of the target object) assigned by the estimation unit 113 and the threshold value input via the input interface 140, and to output the identified region of the target object. As a specific example, the output control unit 114 may compare the size of the polygon of each object (each target object) with a threshold value input via the input interface 140, and perform filtering to hide each object (each target object) corresponding to a polygon whose size is less than the threshold value.

[0037] As an example, as shown in FIG. 4, the output control unit 114 may display a slider 143 on the input interface 140, which specifies a threshold value in the range of 0 to 100. When the slider 143 is moved in response to an operation of the input unit 121, the output control unit 114 may change the threshold value in accordance with the position of the slider 143. Based on the threshold value and the size of the area of ​​the object (target object) (a standard-sized car 201 in the example shown in FIG. 4), the output control unit 114 may display the area of ​​the object (target object) (a standard-sized car 201 in the example shown in FIG. 4) that is equal to or greater than the threshold value in a manner different from other areas on the display unit 133. In the example shown in FIG. 4, the output control unit 114 shades the area of ​​the target object (a standard-sized car 201) in the image 200 and does not shade other areas (e.g., a truck 202 and a standard-sized car not shown in FIG. 4 that is less than the threshold value), thereby displaying the area of ​​the target object (a standard-sized car 201) in a manner different from other areas (e.g., the truck 202, etc.).

[0038] The output control unit 114 may perform control to add an annotation based on the instruction content 141 to the region of the target object and output the result. Furthermore, the output control unit 114 may add an annotation to the region of the target object identified in the image 200 based on the character string (instruction content 141 (prompt)) received by the receiving unit 112 as described above. The output control unit 114 may perform control to add an annotation based on the instruction content 141 (prompt) to the region of the target object whose size is equal to or larger than the value (threshold value) input via the input interface 140 and output the result. As an example, in the case shown in Figure 4, the output control unit 114 may identify the area of ​​the target object (standard car 201) by adding a diagonal line based on the character string (instruction content 141 (prompt)) ``standard car'', and may add an annotation of ``standard car'' to the area of ​​the target object (standard car 201).

[0039] As an example of the above-mentioned output, the output control unit 114 may control the communication unit 131 to transmit information on at least one of the region of the target object and the group of annotated images 200 to an external device (not shown). The external device may be, for example, a server, a user terminal, or the like. As an example of output, the output control unit 114 may control the storage unit 132 to store at least one piece of information on the region of the target object and the group of images 200 to which annotations have been added. As another example of output, the output control unit 114 may control the display unit 133 to display at least one of the region of the target object and the group of annotated images 200. In this case, the output control unit 114 may control the display unit 133 to display the region of the target object in the image 200 in a manner different from other regions (for example, a different color, a different line thickness surrounding the outline, or blinking or brightening the region of the target object) excluding the region of the target object.

[0040] The learning unit 115 may generate a trained model by learning, for example, an image or the like to which an annotation corresponding to a character string (instruction content 141 (prompt)) has been added by the output control unit 114. That is, when an annotation has been added to a region of a target object, the learning unit 115 may learn the region of the target object to generate a trained model. When using the example described above, i.e., when there is an area annotated with passenger car 201 and an area annotated with truck 202 as objects recorded in an image, the learning unit 115 may learn from images annotated with these annotations to generate a learned model.

[0041] The AI ​​unit 116 may input a target to the trained model generated by the learning unit 115 and estimate a target object in the target. An example of the target may be an image (image information) such as a still image or a video. That is, when the AI ​​unit 116 inputs an image (target) to be estimated to the trained model, it becomes possible to estimate a target object (estimated object) recorded in the image (target). As a specific example, if the learning unit 115 generates a trained model that has learned the area of ​​passenger car 201 annotated with passenger car 201 and the area of ​​truck 202 annotated with truck 202, the AI ​​unit 116 may estimate passenger car 201 or truck 202 (target object) in the image (object) based on the image (object) and the trained model.

[0042] [Information processing method] Next, an information processing method according to an embodiment will be described. FIG. 5 is a flowchart illustrating an information processing method according to an embodiment.

[0043] In step ST101, the acquisition unit 111 acquires the image 200 (image information).

[0044] In step ST102, the receiving unit 112 receives an instruction content (prompt) 141 for the target object. The receiving unit 112 may receive the instruction content 141 (prompt) for the target object in the form of a character string (text).

[0045] In step ST103, the estimation unit 113 estimates the area of ​​the target object recorded in the image 200 acquired in step ST102, based on the instruction content 141 received in step ST101. The estimation unit 113 may estimate, as the area of ​​the target object, at least one of the pixel area of ​​the target object and the outline boundary of the target object in the image 200. The estimation unit 113 may assign a value according to the size of the area of ​​the target object.

[0046] In step ST104, the output control section 114 controls the output section to output the region of the target object estimated in step ST103. The output control unit 114 may control the output of an area of ​​the target object that is larger than or equal to the input threshold value (value) based on the value assigned in step ST103 (a value corresponding to the size of the area of ​​the target object) and the threshold value (value) input via the input interface 140. The output control unit 114 may annotate the region of the target object based on the instruction content 141 (prompt such as a character string) received in step ST102.

[0047] When an annotation is added to the region of the target object in response to the processing of step ST104, the learning unit 115 may learn the region of the target object and generate a learned model. The AI ​​unit 116 may input a target object to the trained model generated by the training unit 115 and estimate a target object in the target object.

[0048] [Functions and circuits] Next, the functions and circuits of the information processing device 100 will be described. Each unit of the information processing device 100 may be realized as a function of a computer's arithmetic processing unit, etc. That is, the acquisition unit 111, reception unit 112, estimation unit 113, output control unit 114, learning unit 115, and AI unit 116 (control unit 110) of the information processing device 100 may be realized as an acquisition function, a reception function, an estimation function, an output control function, a learning function, and an AI function (control function), respectively, by a computer's arithmetic processing unit, etc. The information processing program can cause a computer to realize each of the above-mentioned functions. The information processing program may be recorded on a computer-readable non-transitory storage medium, such as a memory, a solid-state drive, a hard disk drive, or an optical disk. The storage medium may also be referred to as a non-transitory computer-readable medium that stores the information processing program. The information processing program may also be transmitted online. Furthermore, as described above, each unit of the information processing device 100 may be realized by an arithmetic processing device of a computer or the like. The arithmetic processing device or the like is configured by, for example, an integrated circuit or the like. Therefore, each unit of the information processing device 100 may be realized as a circuit that constitutes the arithmetic processing device or the like. That is, the acquisition unit 111, the reception unit 112, the estimation unit 113, the output control unit 114, the learning unit 115, and the AI ​​unit 116 (control unit 110) of the information processing device 100 may be realized as an acquisition circuit, a reception circuit, an estimation circuit, an output control circuit, a learning circuit, and an AI circuit (control circuit) that constitute the arithmetic processing device of a computer or the like. The input unit 121, communication unit 131, storage unit 132, and display unit 133 (output unit) of the information processing device 100 may be realized as, for example, an input function including the functions of an arithmetic processing device, and a communication function, storage function, and display function (output function). The input unit 121, communication unit 131, storage unit 132, and display unit 133 (output unit) of the information processing device 100 may be realized as, for example, an input circuit, a communication circuit, a storage circuit, and a display circuit (output circuit) by being configured using integrated circuits, etc. The input unit 121, communication unit 131, storage unit 132, and display unit 133 (output unit) of the information processing device 100 may be realized as, for example, an input unit device, a communication device, a storage device, and a display device (output device) by being configured using a plurality of devices.

[0049] The information processing device 100 can combine one or any combination of the above-mentioned multiple units. In this disclosure, the term "information" is used, but the term "information" can be replaced with "data" and the term "data" can be replaced with "information."

[0050] [Aspects and Effects of the Present Embodiment] Next, one aspect of this embodiment and the effects of each aspect will be described. Note that each aspect described below is an example at the time of filing, and this embodiment is not limited to the aspects described below. In other words, this embodiment is not limited to the aspects described below, and may be realized by appropriately combining the above-mentioned parts. Furthermore, a lower-level aspect may be able to cite any of the higher-level aspects. The effects of the present embodiment described below are merely examples, and the effects of each aspect are not limited to those described below. Each aspect may, for example, achieve at least one of the effects described below.

[0051] (Aspect 1) An information processing device of one embodiment includes an acquisition unit that acquires an image, a reception unit that receives instructions regarding a target object, an estimation unit that estimates the area of ​​the target object to be recorded in the image acquired by the acquisition unit based on the instructions received by the reception unit, and an output control unit that controls the area of ​​the target object estimated by the estimation unit to be output. As a result, when the information processing device receives a command (prompt) in the form of a character string (text), it can output (for example, display) the area of ​​the target object in the image according to the command (prompt). For example, when there are regions of multiple types of target objects in an image, the information processing device can output (for example, display) the region of one type of target object by switching the output content (display content). The information processing device can add an annotation corresponding to a character string (text) to a region of a target object to be output.

[0052] (Aspect 2) In the information processing device of one aspect, the estimation unit may estimate, as the region of the target object, at least one of a pixel region of the target object in the image and a contour boundary of the target object. This allows the information processing device to annotate the target object in the image based on at least one of a group of pixel regions and a group of contour boundaries.

[0053] (Aspect 3) In one embodiment of the information processing device, the estimation unit may assign a value according to the size of the area of ​​the target object, and the output control unit may control the output to output an area of ​​the target object that is larger than the input value. This allows the information processing device to estimate that areas whose size is less than the input value (threshold value) are noise, and prevents the information processing device from annotating the wrong target object area (noise).

[0054] (Aspect 4) In the information processing device of one aspect, the receiving unit may receive instructions about the target object in text. This allows the information processing device to annotate a region of the target object that corresponds to the content of the text (character string) based on the instruction content (prompt) of the text (character string). That is, the information processing device can annotate a region of the target object that corresponds to the content of the text (character string).

[0055] (Aspect 5) In the information processing device of one aspect, the output control unit may perform control so as to add an annotation based on the instruction content to the region of the target object and output it. This allows the information processing device to generate a trained model by learning, for example, the annotated region of the target object in the image. That is, the information processing device can automatically annotate the target object in the image and generate ground truth data. That is, when the information processing device receives an upload of an image and further receives instructions (prompts) about the target object via the input unit, it can automatically annotate the target object in the image. Because the information processing device can automatically annotate, it can significantly reduce the work time and work man-hours (work costs) compared to manually identifying the target object in the image and annotating it.

[0056] (Aspect 6) An information processing device according to one aspect may include a learning unit that, when an annotation is added to a region of a target object, learns the region of the target object and generates a learned model. This allows the information processing device to automatically perform learning based on the correct answer data and generate a learned model.

[0057] (Aspect 7) An information processing device according to one embodiment may include an AI unit that inputs an object to a trained model generated by the learning unit and estimates a target object among the objects. This allows the information processing device to make inferences about various target objects learned when generating the trained model.

[0058] (Aspect 8) In one aspect of the information processing method, a computer executes an acquisition step of acquiring an image, a reception step of receiving instructions regarding a target object, an estimation step of estimating the area of ​​the target object to be recorded in the image acquired by the acquisition step based on the instructions received by the reception step, and an output control step of controlling the output of the area of ​​the target object estimated by the estimation step. As a result, the information processing method can achieve the same effects as the information processing device of the above-described aspect.

[0059] (Aspect 9) One embodiment of the information processing program enables a computer to realize an acquisition function for acquiring an image, a reception function for receiving instructions regarding a target object, an estimation function for estimating the area of ​​the target object to be recorded in the image acquired by the acquisition function based on the instructions received by the reception function, and an output control function for controlling the output of the area of ​​the target object estimated by the estimation function. As a result, the information processing program can achieve the same effects as the information processing device of the above-described aspect. [Explanation of symbols]

[0060] 100 Information processing device 110 control section 111 Acquisition Department 112 Reception 113 Estimation Department 114 Output control section 115 Learning Department 116 AI Department 121 Input section 131 Communications Department 132 Storage section 133 Display section 140 Input Interface 141 Prompts 142 Chat Responses 143 Slider 144 Chat Responses 200 images 201 Ordinary car 202 trucks

Claims

1. an acquisition unit that acquires an image; a reception unit that receives instructions regarding a target object; an estimation unit that estimates an area of ​​a target object recorded in the image acquired by the acquisition unit based on the instruction content accepted by the acceptance unit; an output control unit that controls the area of ​​the target object estimated by the estimation unit to be output; An information processing device comprising:

2. The estimation unit estimates at least one of a pixel region of the target object in the image and a contour boundary of the target object as the region of the target object. The information processing device according to claim 1 .

3. the estimation unit assigns a value according to the size of the region of the target object; The output control unit controls to output a region of the target object having a size equal to or larger than the input value. The information processing device according to claim 1 .

4. The receiving unit receives a text instruction about the target object. The information processing device according to claim 1 .

5. The output control unit controls to add annotations based on the instruction content to the region of the target object and output the annotations. The information processing device according to claim 1 .

6. a learning unit that, when an annotation is added to the region of the target object, learns the region of the target object and generates a learned model; The information processing device according to any one of claims 1 to 5.

7. An AI unit that inputs an object to the trained model generated by the training unit and estimates a target object from the object The information processing device according to claim 6 .

8. The computer an acquisition step of acquiring an image; a receiving step of receiving instructions regarding the target object; an estimation step of estimating an area of ​​the target object recorded in the image acquired in the acquisition step based on the instruction content accepted in the acceptance step; an output control step of controlling to output the area of ​​the target object estimated by the estimation step; An information processing method that performs the above.

9. On the computer, an acquisition function for acquiring an image; a reception function for receiving instructions regarding the target object; an estimation function that estimates an area of ​​a target object recorded in an image acquired by the acquisition function based on the instruction content accepted by the acceptance function; an output control function that controls to output the area of ​​the target object estimated by the estimation function; An information processing program that makes this possible.

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