Abstract: In a computer-implemented method of augmenting a dataset used in facial expression analysis, a first facial image and a second facial image are added to a training/testing dataset and mapped to two respective points in a continuous dimensional emotion space. The position of a third point in the continuous dimensional emotion space between the first two points is determined. Augmentation is achieved when a labelled facial image is derived from the third point based on its position relative to the first and second facial expression.

Abstract: A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

Abstract: An information processing device (10) acquires a plurality of ledger sheets having the same layout, compares the contents of each column at the same position each of the acquired plurality ledger sheets having the same layout, discriminates the type of each column according to the comparison result, and stores the information on the type of each column in a storage unit (14). Moreover, the information processing device (10) acquires position information of a processing target ledger sheet, compares information on the type of a column and the content of each column using information on a registered style with respect to the acquired ledger sheet, discriminates the type of each column of the processing target ledger sheet according to the comparison result, and specifies style candidates of the processing target ledger sheet on the basis of the discrimination result.

Abstract: An imaging processing method that acquires first and second overlapping image data sets by performing first and second measurements on an overlapping location at first and second times, wherein the first and second times are different times; determines whether the first and second overlapping image data sets have substantially a same image quality; and generating and outputting, a first weighted overlapping combined image by combining (a) first weighted image data generated by applying a first weight to an overlapping frequency range of the overlapping image data set having a higher image quality and (b) second weighted image data generated by applying a second weight to the overlapping frequency range of the overlapping image data set having a lower image quality, wherein the first weight is larger than the second weight.

Abstract: A method for generating a plurality of sets of training image data for training a machine learning model includes: (a) acquiring object image data representing an object image; (b) dividing the object image into T number of partial object images by dividing a region of the object image into T number of partial regions corresponding to respective ones of T number of partial color ranges; (c) generating a plurality of sets of color-modified object image data representing respective ones of a plurality of color-modified object images by performing an adjustment process on the object image data, the adjustment process including a color modification process to modify colors of at least one of the T number of partial object images; and (d) generating the plurality of sets of training image data using one or more sets of background image data and the plurality of sets of color-modified object image data.

Abstract: An imaging processing device includes an image acquisition module configured to acquire an image obtained by photographing the sky with a camera having a known orientation or slant; a time stamp acquisition module configured to acquire a photographing date and time of the image; a photographing position acquisition module configured to acquire position information of a photographing position of the image; a sun position determination module configured to determine a photographed sun position in the image; a reference sun position acquisition module configured to calculate a reference sun position indicating a sun position determined based on the photographing date and time and the position information; and a camera information identification module configured to determine any one of unknown orientation and slant of the camera based on any one of the known orientation and slant of the camera, the photographed sun position, and the reference sun position.

Abstract: A computer-implemented method is disclosed for segmenting a binarized document. The method includes extracting connected components from the binarized document and discriminating (for at least one of the connected components) whether it is a text component based on a homogeneity level value. The homogeneity level value is representative of the level of homogeneity within the local region of the connected component. The local region includes the connected component and at least one adjacent connected component. The homogeneity level value is based on at least one value representative of at least one image characteristic parameter determined for the connected component and on at least one value representative of the image characteristic parameter of the at least one adjacent connected component.

Abstract: Provided is an in-vehicle sensor system capable of maintaining an equivalent level of precision (accuracy) of data to be output before and after replacement of a surrounding sensor.

Abstract: The disclosure herein describes preparing and using a cross-attention model for action recognition using pre-trained encoders and novel class fine-tuning. Training video data is transformed into augmented training video segments, which are used to train an appearance encoder and an action encoder. The appearance encoder is trained to encode video segments based on spatial semantics and the action encoder is trained to encode video segments based on spatio-temporal semantics. A set of hard-mined training episodes are generated using the trained encoders. The cross-attention module is then trained for action-appearance aligned classification using the hard-mined training episodes. Then, support video segments are obtained, wherein each support video segment is associated with video classes. The cross-attention module is fine-tuned using the obtained support video segments and the associated video classes.

Abstract: One embodiment provides a method for clustering documents based upon a structure of each of the documents, including: receiving, at a device utilizing the machine-learning model, at least one document, each including a plurality of characters and having a structure; converting, for each of the at least one document, each of the plurality of characters to one of a plurality of character representations, wherein the converting includes identifying an attribute of a character and selecting a character representation corresponding to the attribute; producing at least one array for each of the one or more documents, wherein the at least one array includes the plurality of characters converted to the character representations; and clustering the at least one document into document clusters having similar structures by grouping the at least one arrays into groups of arrays having similarities, wherein each document cluster include documents corresponding to the arrays within one of the groups of arrays.

Abstract: A method and system for automatic technical assistance is provided. The technical assistant is first trained using the user profile of the users who will be accessing the technical assistant, and the workflow details. The technical assistant continuously monitors user screen and checks if the user is on the correct screen for the required workflow. Based on user confirmation an RPA bot resolves the error for the user. In an embodiment, the bot completes the workflow for the user. The bot may also provide guidance content to the user to help the user realize the error.

Abstract: Provided are an artificial intelligence (AI) system using a machine learning algorithm such as deep learning, and applications thereof. A method, performed by an electronic apparatus, of processing images includes obtaining a plurality of training image sets corresponding to a plurality of types of target objects, wherein training images in the training image sets are labeled with feature points forming a preset structure, generating a first artificial intelligence (AI) model for determining a standard structure based on the labeled feature points, by using the training images in the training image sets, identifying a face in a training image transformed based on the standard structure, and training a second AI model for verifying the first AI model, based on an image regressed from the transformed training image, and the training image before being transformed.

Abstract: In order for a supplier of connectors to reduce personnel costs and to guarantee customers a consistently high quality standard promptly and reliably, even for global data traffic, a method for identifying connectors, comprising the following steps, is proposed: a. automatic identification of components (10, 2, 3, 3?, 3?) of an industrial connector (1), from at least one image file; b. analysis of the geometric relationships and/or functional connections between the components (10, 2, 3, 3?, 3?); c. extraction of individual characteristics of the components (10, 2, 3, 3?, 3?) from the image file using information obtained in step b.

Abstract: In some implementations, a device may detect edges in an image, and may identify, based on the edges, a rectangle that bounds a document in the image. The device may detect lines in the image, and may identify edge candidate lines by discarding one or more of the lines. The device may identify intersection points where lines, included in the edge candidate lines, intersect with one another. The device may identify corner candidate points by discarding one or more points included in the intersection points, and may identify a corner point included in the corner candidate points. The corner point may be a point, included in the corner candidate points, that is closest to one corner of the bounding rectangle. The device may perform perspective correction on the image of the document based on identifying the corner point.

Abstract: An image classification system for tracking a net sport is illustrated. The system includes a visual device configured to capture video footage of a match. The computing device is communicatively connected to the visual device and is configured to receive the video footage of the match from the visual device, translate video footage of the match from the visual device into a data representation by logging an event with a timestamp, and display data representation to a user.

Abstract: The disclosure provides example visual localization methods. One method includes that a terminal device obtains an image of a building. The terminal device generates a descriptor based on the image. The descriptor includes information about a horizontal viewing angle between a first vertical feature line and a second vertical feature line in the image. The first vertical feature line indicates a first facade intersection line of the building, and the second vertical feature line indicates a second facade intersection line of the building. The terminal device performs matching in a preset descriptor database based on the descriptor, to obtain localization information of a photographing place of the image.

Abstract: An attribute-based point cloud strip division method. The method comprises: first, performing spatial division of a certain depth on a point cloud to obtain a plurality of local point clouds; and then, sorting the attribute values in the local point clouds, and on the basis of such, further performing point cloud strip division so as to obtain point cloud strips that have low geometric overhead and a uniform number of points. By means of comprehensively using the spatial position and attribute information of the point clouds, the points having similar attributes and related spatial positions are gathered as much as possible in one strip during strip division, which is convenient for making full use of the redundancy of the attribute information between adjacent points, and improving the performance of point cloud attribute compression.

Abstract: One aspect provides operating a mobile pipe inspection platform to obtain two or more types of sensor data for the interior of a pipe; analyzing, using a processor, the two or more types of sensor data using a trained model, where the trained model is trained using a dataset including training sensor data of pipe interiors; the analyzing including performing: identifying, using a processor, a pipe feature location using a first type of the two or more types of sensor data; and classifying, using a processor, an identified pipe feature using a second type of the two or more types of sensor data; and thereafter producing, using a processor, an output including an indication of the classified pipe feature. Other aspects are described and claimed.

Abstract: The present disclosure relates to an artificial intelligence-based oral CT automatic color conversion device and a method for driving same. The artificial intelligence-based oral CT automatic color conversion device according to an embodiment of the present invention comprises: a storage unit which stores a color conversion image related to the bone density of an alveolar bone, which is previously prepared by a user; and a control unit which, when an oral CT input image of a patient is received, converts color of the received oral CT input image on the basis of a predetermined parameter value, and adjusts the predetermined parameter value by using an error value resulting from the converted oral CT input image and the (pre)stored color conversion image to automatically convert the color of an oral CT input image to be input later.

Abstract: A system including an acquisition unit configured to acquire, from a user via a communication device associated with the user, target object data including a feature of a target object selected by the user, an analysis unit configured to analyze whether the target object data that has been acquired by the acquisition unit includes, as the feature, at least one of data of a proper noun or data of a character string related to the target object, and whether the target object data includes data of a color related to the target object, and an estimation unit configured to estimate a distance from the target object to the user, based on an analysis result of the analysis unit, wherein the estimation unit estimates the distance from the target object to the user such that the distance from the target object to the user in a case where the target object data includes at least one of the data of the proper noun or the data of the character string is shorter than the distance from the target object to the user in a ca