An information processing apparatus of the present invention comprises acquires a captured image; detects a plurality of targets included in the captured image, and extracts a plurality of features for each of the detected plurality of targets; acquires an impurity for each extracted feature, the impurity indicating a degree to which a predetermined target is inseparable from among the plurality of targets in a case where a user is asked a question for presuming the predetermined target from among the plurality of targets based on each feature; and generates the question to reduce a number of questions for minimizing the impurity based on the extracted features and the impurity for each of the features.

A method for refining label information, which is performed by at least one computing device is disclosed. The method includes acquiring a pathology slide image including a plurality of patches, inferring a plurality of label information items for the plurality of patches included in the acquired pathology slide image using a machine learning model, applying the inferred plurality of label information items to the pathology slide image, and providing the pathology slide image applied with the inferred plurality of label information items to an annotator terminal.

Provided is a ledger recognition system which can enhance recognition accuracy of a handwritten character filled out by a user thus capable of reducing a manual work in a correction operation. A ledger recognition system includes: a headquarter server configured to recognize handwritten characters described in a ledger by a user; a system terminal including an image scanner for reading the handwritten characters filled out in the ledger by the user; and a public telecommunication network which allows the headquarter server and the system terminal to be communicably connected with each other. The headquarter server includes a handwritten character recognition unit where the handwritten character recognition unit receives the image data of the ledger read by the image scanner from the system terminal, recognizes the handwritten characters written by the user in the image data of the received ledger in accordance with at least two types of OCR recognition programs having different algorithms, determines the handwritten characters described in the ledger with respect to a part of the handwritten characters where recognition results in accordance with the OCR recognition programs agree with each other, and sets a part of the handwritten characters where the recognition results by the OCR recognition programs do not agree with each other as an object of correction processing.

It reduces labor and time to generate training data for the training model. An identification information assignment apparatus includes an acquirer configured to acquire a plurality of pieces of image data, an assigner configured to assign identification information to image data selected from the plurality of pieces of image data by using a learning model after learning, and an updater configured to update the learned model using the image data to which the identification information is assigned, wherein the assigner assigns identification information to a rest of the image data acquired by the acquirer using the learned model that has been updated.

In example implementations, a method is provided. The method may be executed by a processor. The method includes receiving an image. A person from a plurality of people within the image is identified. The person is segmented from the image. A background image of the image is replaced with a clean background image to remove the plurality of people from the image except the person that is identified.

According to one embodiment, an image processing apparatus includes a memory and a hardware processor in communication with the memory. The hardware processor is acquire an image obtained by photographing at least one detection target, detect the detection target from the image obtained, and instruct re-photographing of the detection target based on a result of the detection target.

An inspection apparatus includes one or more controllers having one or more processors and one or more memories, the one or more controllers configured to function as a receiving unit configured to receive an image to be registered as a reference image, a reception unit configured to receive an inspection setting for the received reference image, a storage unit configured to store an inspection setting in a storage device, and an inspection unit configured to, if a scan image obtained by scanning of a printed matter is received, inspect the scan image based on the inspection setting, wherein, in a case where a predetermined setting is received by the reception unit, an inspection setting associated with the predetermined setting stored by the storage unit is reflected in the reference image.

Systems, methods, and devices are disclosed for training a model. Media data is separated into one or more clusters, each cluster based on a feature from a first model. The media data of each cluster is sampled and, based on an analysis of the sampled media data, an accuracy of the media data of each cluster is determined. The accuracy is associated with the feature from the first model. Based on a subset dataset of the media data being outside a threshold accuracy, the subset dataset is automatically forwarded to a crowd source service. Verification of the subset dataset is received from the crowd source service, and the verified subset dataset is added to the first model.

Provided is the technique that allows an operator or the like to easily grasp a defect contained in a mold. A display control device is configured to carry out: a determination process in which a feature amount of each of defects contained in a mold is determined with reference to an image obtained by image capture of a mold; and a display process in which the image is displayed on a display such that each of the defects in the image is emphasized by a method which is determined according to the determined feature amount.

Examples of the disclosure relate to monitoring of facial characteristics. In examples of the disclosure there may be provided an apparatus that is configured to determine that a communications device is positioned close to an ear of a user of the communication device during a communication session wherein the communications device comprises at least one display. The apparatus may also be configured to use sensors in or under the display of the communications device to monitor one or more facial characteristics of the user while the communication device is close to the user's ear and to identifying an emotional context of the user based on the monitored one or more facial characteristics of the user.