Abstract: A system for establishing a model for cardiac ventricular hypertrophy (VH) screening includes a storage and a processor. The storage stores multiple pieces of subject data respectively associated with multiple subjects. Each of the pieces of subject data contains a basic physiological parameter group, an electrocardiographic parameter group, and an actual VH condition that corresponds to a left or right ventricle of the subject associated with the piece of subject data. The processor is electrically connected to the storage, splits the pieces of subject data into a training set and a test set, and establishes the model for VH screening based on the pieces of subject data in the training set by using machine learning techniques.

Abstract: The presently disclosed inventive concepts are directed to systems, computer program products, and methods for intelligent screen automation. The inventive computer program products include program instructions configured to cause a computer, upon execution thereof, to perform a method including: identifying, from among a plurality of elements within one or more images of a user interface, a first set of elements and a second set of elements, wherein the first set of elements and the second set of elements each independently comprise either or both of: textual elements, and non-textual elements; determining one or more logical relationships between the textual elements and the non-textual elements; building a hierarchy comprising some or all of the first set of elements and some or all of the second set of elements, wherein building the hierarchy forms a data structure representing functionality of the user interface; and outputting the data structure to a memory.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for extracting information, a device and a storage medium, relate to the field of image processing technology. The method may include: acquiring a location template corresponding to a category of a target document image; determining key point locations on the target document image; generating a transformation matrix based on the key point locations on the target document image and key point locations on the location template; determining locations of information corresponding to the target document image, based on locations of information on the location template and the transformation matrix; and extracting information at the locations of information corresponding to the target document image to obtain information in the target document image.

Abstract: Techniques for building and managing data models are provided. According to certain aspects, systems and methods may enable a user to input parameters associated with building one or more data models, including parameters associated with sampling, binning, and other factors. The systems and methods may automatically generate program code that corresponds to the inputted parameters and display the program code for review by the user. The systems and methods may build the data models and generate charts and plots depicting aspects of the data models. Additionally, the systems and methods may combine data models and select champion data models.

Abstract: The present disclosure relates to an information processing apparatus and a method that allows for suppression of a decrease in encoding efficiency. In comparison of voxel data resulting from quantization of point cloud data, a correlation degree of a distribution pattern between voxel data to be compared is calculated on the basis of a subjective feature of the distribution pattern of values of the voxel data. The present disclosure is applicable to an information processing apparatus, an image processing apparatus, an electronic device, an information processing method, a program, or the like, for example.

Abstract: A method for detecting fraudulent interactions may include receiving interaction data, including a first plurality of interactions with (first) fraud labels and a second plurality of interactions (without fraud labels). Second fraud label data for each of the second plurality of interactions may be generated with a first neural network (e.g., classifying whether each interaction is fraudulent or not). Generated interaction data and generated fraud label data may be generated with a second neural network. Discrimination data for each of the second plurality of interactions and generated interactions may be generated with a third neural network (e.g., classifying whether the respective interaction is real or not). Error data may be determined based on the discrimination data (e.g., whether the respective interaction is correctly classified). At least one of the neural networks may be trained based on the error data. A system and computer program product are also disclosed.

Abstract: Objectness indicating a degree of accuracy of a single object is accurately estimated. An edge detection unit 30 detects an edge for a depth image, an edge density/uniformity calculation unit 40 calculates an edge density on the periphery of a candidate region, an edge density inside the candidate region, and edge uniformity on the periphery of the candidate region. An objectness calculation unit 42 calculates the objectness of the candidate region based on the edge density on the periphery of the candidate region, the edge density inside the candidate region, and the edge uniformity on the periphery of the candidate region.

Abstract: A training method of a neural network for implementing image style transfer, an image processing method and an image processing device are disclosed. The training method includes: acquiring a first training input image and a second training input image; performing a style transfer process on the first training input image by the neural network to obtain a training output image; based on the first training input image, the second training input image and the training output image, calculating a loss value of parameters of the neural network through a loss function; and modifying the parameters of the neural network according to the loss value, where the loss function satisfies a predetermined condition, obtaining a trained neural network, where the loss function doesn't satisfy the predetermined condition, continuing to repeatedly perform above training process, the loss function including a weight-bias-ratio loss function.

Abstract: A camera control unit for processing a first video signal from an image sensor, the first video signal including a plurality of frames includes a frame replication device that replicates pixels of a respective frame of the first video signal to produce at least one replicated frame, an image processing controller that detects a noise level in the first digital video signal and that selects a modification level for the random modification of the respective pixel data based on the detected noise level, and a frame modification controller that modifies at least some of the respective pixels in the at least one replicated frame resulting in at least one modified replicated frame based on the selected modification level.

Abstract: A processor performing postprocessing obtains an input image containing both bright and dark regions. The processor obtains a threshold between a first pixel value of the virtual production display and a second pixel value of the virtual production display. The processor modifies the region according to predetermined steps producing a pattern unlikely to occur within the input image, where the pattern corresponds to a difference between the original pixel value and the threshold. The processor can replace the region of the input image with the pattern to obtain a modified image. The virtual production display can present the modified image. A processor performing postprocessing detects the pattern within the modified image displayed on the virtual production display. The processor calculates the original pixel value of the region by reversing the predetermined steps. The processor replaces the pattern in the modified image with the original pixel value.

Abstract: Systems and methods of the present disclosure can include a computer-implemented method for efficient machine-learned model training. The method can include obtaining a plurality of training samples for a machine-learned model. The method can include, for one or more first training iterations, training, based at least in part on a first regularization magnitude configured to control a relative effect of one or more regularization techniques, the machine-learned model using one or more respective first training samples of the plurality of training samples. The method can include, for one or more second training iterations, training, based at least in part on a second regularization magnitude greater than the first regularization magnitude, the machine-learned model using one or more respective second training samples of the plurality of training samples.

Abstract: A system comprises an encoder configured to compress and encode data for a three-dimensional mesh using a video encoding technique. To compress the three-dimensional mesh, the encoder determines sub-meshes and for each sub-mesh: texture patches and geometry patches. Also the encoder determines patch connectivity information and patch texture coordinates for the texture patches and geometry patches. The texture patches and geometry patches are packed into video image frames and encoded using a video codec. Additionally, the encoder determines boundary stitching information for the sub-meshes. A decoder receives a bit stream as generated by the encoder and reconstructs the three-dimensional mesh.

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

Abstract: An electronic device according to certain embodiments may include a camera, a memory, and at least one processor, wherein the at least one processor is configured to: obtain an image through the camera; obtain an image of at least one object from the obtained image; determine whether the image of the at least one object corresponds to an image of an object stored in the memory; output about a request for information about the at least one object, based at least in part on the determination; and obtain the information about the at least one object, based at least in part on information received from an external source.

Abstract: A sample characterization system is disclosed. In embodiments, the sample characterization system includes a controller communicatively coupled to an inspection sub-system, the controller including one or more processors configured to execute a set of program instructions stored in memory, the set of program instructions configured to cause the one or more processors to: acquire one or more target image frames of a sample; generate a target tensor with the one or more acquired target image frames; perform a first set of one or more decomposition processes on the target tensor to generate one or more reference tensors including one or more reference image frames; identify one or more differences between the one or more target image frames and the one or more reference image frames; and determine one or more characteristics of the sample based on the one or more identified differences.

Abstract: An image processing method and apparatus using a line unit operation is disclosed. The image processing apparatus includes a receiver configured to receive an image, at least one first line buffer configured to output the received image to be an image line of a line unit, a first convolution operator configured to generate a feature map by performing a convolution operation based on the output image line, and a feature map processor configured to store the generated feature map as at least one line unit and process the feature map stored as the line unit to be output in a two-dimensional (2D) or higher dimensional form.

Abstract: A method and system for processing images for a search is provided, including: receiving a plurality of images selected from search results; for each image in the plurality of images, retrieving a feature vector associated with the image; selecting a subset of the feature vectors based on similarity of feature vectors associated with the images in the plurality of images; and performing a search for feature vectors in a database similar to the feature vectors in the subset of feature vectors.

Abstract: A method for developing visual data using source data, target data, and a hierarchical algorithm. According to a first aspect, there is provided a method for developing visual data from source data, target data and using a hierarchical algorithm, the method comprising the steps of: determining an alignment between the target data and the source data; and producing the visual data by transferring one or more features of the source data onto one or more features of the target data; wherein, the visual data is produced after the step of determining the alignment between the target data and the source data; and wherein the visual data is produced using the hierarchical algorithm.

Abstract: A device and a method for detecting a purpose of an article are provided. The device is configured to divide the article into a plurality of sentences and input the sentences to a feature identification model to generate a contextualized word vector corresponding to each of the sentences. The device further inputs the representation to a specific purpose detecting model to generate a distributed representation similarity of the article. When the distributed representation similarity of the article is greater than a threshold, the device determines that the article conforms to a specific purpose.

Abstract: The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.