Embodiments of the invention are directed to automatically capturing user interface screenshots for use in documentation of a software product. Aspects include identifying a user interface window of the software product and creating a degree-of-completion graph for the user interface window. Aspects also include capturing a plurality of screenshots of the user interface window during use of the software product and calculating a degree-of-completion percentage for each of the plurality of screenshots. Aspects further include identifying a subset of the plurality of screenshots to be included in the software product documentation based on the degree-of-completion percentage.

A computing device for training an artificial neural network model includes: a model analyzer configured to receive a first artificial neural network model and split the first artificial neural network model into a plurality of layers; a training logic configured to calculate first sensitivity data varying as the first artificial neural network model is pruned, calculate a target sensitivity corresponding to a target pruning rate based on the first sensitivity data, calculate second sensitivity data varying as each of the plurality of layers is pruned, and output, based on the second sensitivity data, an optimal pruning rate of each of the plurality of layers, the optimal pruning rate corresponding to the target pruning rate; and a model updater configured to prune the first artificial neural network model based on the optimal pruning rate to obtain a second artificial neural network model, and output the second artificial neural network model.

An image processing apparatus, an image processing method, a program, and a recording medium storing the program capable of extracting, from an input first image group, an image similar to an image extracted from a reference image group are provided. A first image group including a plurality of images of a user is transmitted to an order reception server. A second image group similar to the first image group is searched from a plurality of reference image groups stored in the order reception server. An image similar to an image previously extracted from the second image group is extracted from the first image group. An album is generated by arranging the image extracted from the first image group in a layout similar to a layout of an album generated from the second image group.

Methods, systems, and computer-readable storage media for receiving input data including a set of entities of a first type and a set of entities of a second type, providing a set of features based on entities of the first type, the set of features including features expected to be included in entities of the second type, filtering entities of the second type based on the set of features to provide a sub-set of entities of the second type, and generating an output by processing the set of entities of the first type and the sub-set of entities of the second type through a ML model, the output comprising a set of matching pairs, each matching pair in the set of matching pairs comprising an entity of the set of entities of the first type and at least one entity of the sub-set of entities of the second type.

Systems and methods for generating assessments based on construction site images are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. Further, at least one electronic record associated with the construction site may be obtained. The image data and the at least one electronic record may be analyzed to generate at least one assessment related to the construction site. For example, the image data may be analyzed to identify at least one discrepancy between the at least one electronic record and the construction site, and the identified at least one discrepancy may be used in the generation of the at least one assessment.

A information processing apparatus (2000) includes a determination unit (2160) and a deletion unit (2180). The determination unit (2160) determines whether feature information to be determined satisfies a predetermined condition. When feature information to be determined is determined to satisfy the predetermined condition, the deletion unit (2180) deletes the feature information to be determined from the storage apparatus (120).

Methods of autonomous diagnostic verification and detection of defects in the optical components of a vision-based inspection system are provided. The method includes illuminating a light panel with a first light intensity pattern, capturing a first image of the first light intensity pattern with a sensor, illuminating the light panel with a second light intensity pattern different than the first light intensity pattern, capturing a second image of the second light intensity pattern with the sensor, comparing the first image and the second image to generate a comparison of images, and identifying defects in the light panel or the sensor based upon the comparison of images. Systems adapted to carry out the methods are provided as are other aspects.

Systems, methods, and techniques to efficiently and effectively verifying and calibrating a machine learning model. The method can include training a machine learning model by at least processing training data with the machine learning model. The method can further include manipulating a first data set of the training data and applying the manipulated first data set to the machine learning model to thereby determine a first matching rate. In addition, the method can include applying the manipulated first data set to a rule engine to thereby determine a second matching rate and determining a difference between the first matching rate and the second matching rate. The method can further include determining whether the difference is within a predefined threshold range and providing an error indication if the determined difference is outside of the predefined threshold range.

A method and an apparatus for processing an image are provided. The method may include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequence subsets divided according to similarity measurements between image sequences, each image sequence subset including a basic image sequence and other image sequence, wherein a first similarity measurement corresponding to the basic image sequence is greater than or equal to a first similarity measurement corresponding to the other image sequence; creating an original three-dimensional model using the basic image sequence; and creating a final three-dimensional model using the other image sequence based on the original three-dimensional model.

An artificial intelligence (AI) robot includes a body for defining an exterior appearance and containing a medicine to be discharged according to a medication schedule, a support, an image capture unit for capturing an image within a traveling zone to create image information, and a controller for discharging the medicine to a user according to the medication schedule, reading image data of the user to determine whether the user has taken the medicine, and reading image data and biometric data of the user after the medicine-taking to determine whether there is abnormality in the user. The AI robot identifies a user and discharges a medicine matched with the user, so as to prevent errors. The AI robot detects a user's reaction after medicine-taking through a sensor, and performs deep learning, etc. to learn the user's reaction, to determine an emergency situation, etc. and cope with a result of the determination.