Abstract: A set of pre-annotated medical images is received, and the received set is processed by automatically: training an AI-based uncertainty model using the received set as training data; processing medical images of the received set by determining classified segments and/or uncertainty regions in the medical images using the trained AI-based uncertainty model; selecting at least a part of the processed medical images including classified segments and/or uncertainty regions based on the processing result; and presenting the selected part of processed medical images to a human expert. Furthermore, a modified received set including additional annotations created by the human expert is received.

Abstract: Systems and methods are provided for detecting and classifying driving behaviors, as well as techniques for improving such systems and methods, which may include identifying geographic locations where anomalous rates of non-compliant driving behaviors are observed, such as in the vicinity of certain traffic signs, traffic lights, and road markings.

Abstract: A method of image processing includes receiving a set of images from a sensor, dynamically determining respective cell resolutions of respective cells in a bird's-eye-view (BEV) grid based on image content in the set of images, wherein at least two of the cells have different cell resolutions, and generating BEV image content based on the respective cell resolutions of the respective cells.

Abstract: System apparatus, article of manufacture, method and/or computer program embodiments are provided for detecting and monitoring compliance matters. An example method may include obtaining a first set of image data corresponding to an environment from one or more image sensors; processing the first set of image data using a first machine learning algorithm to yield annotated image data that identifies at least one object within the environment; processing the annotated image data using a second machine learning algorithm to determine one or more attributes associated with the at least one object within the environment; and generating, based on the one or more attributes, an evidence package corresponding to the at least one object within the environment, the evidence package being configured to facilitate a determination of one or more action items associated with the at least one object.

Abstract: An eyeball tracking method and a virtual reality device are disclosed. The eyeball tracking method is applied to the virtual reality device, the virtual reality device comprises at least two cameras, and shooting areas of the cameras are partially overlapped. The eyeball tracking method comprises the steps of: acquiring images captured by at least two cameras at a same moment; superimposing overlapping areas in the images to obtain a target image including an eyeball area; and performing eyeball tracking according to the target image. The accuracy of eyeball tracking can be improved.

Abstract: A method is for verifying an implemented neural network and includes providing a plurality of validation images, feeding the plurality of validation images to an original neural network, feeding the plurality of validation images to the implemented neural network, and determining an activation coverage of the original neural network using all of the validation images of the provided plurality of validation images. The method further includes comparing the determined activation coverage with a target coverage, and comparing respective output images of the original neural network resulting from the plurality of validation images, with respective output images of the implemented neural network resulting from the plurality of validation images, in order to verify the implemented neural network.

Abstract: A system and method for authenticating an image is given. The method encompasses capturing a first image comprising at least one first human object and a second ID image; detecting at least one face of one first human object and processing the first human object based on face attribute checks, face quality checks, and face manipulation checks; performing a liveness check of the identification document to crop and align the region of interest; detecting a face of the at least one second human object; performing ID quality checks and ID tampering checks on the region of interest of the identification document; determining the image face feature vector and an ID card face feature vector; determining a match score between the image face feature vector and the ID card face feature vector and a status indicating one of authentication success, authentication failure, or a requirement for manual review of the image.

Abstract: A Method for processing RGBIR (Red Green Blue Infrared) multi-frame HDR (High Dynamic Range) with luma includes capturing a plurality of RGBIR raw images with different exposure times by at least one camera, generating a plurality of luma images using a color filter array luma (CFAL) filter according to the plurality of RGBIR raw images, performing IR color correction to generate a plurality of corrected RGBIR raw images according to the RGBIR raw images, performing HDR fusion to generate a HDR RGBIR raw image according to the plurality of luma images and the plurality of corrected RGBIR raw images, generating gradients according to the HDR RGBIR raw image and the plurality of luma images, and performing interpolation to generate a result image according to at least the gradients and the HDR RGBIR raw image.

Abstract: Provided are apparatuses, a non-transitory computer-readable medium or media, for predicting of neurodevelopmental disorder using a fundus image of a subject. In certain aspects, disclosed a method including the steps of: receiving the fundus image; processing the fundus image to classify one or more features contained in the fundus image using a machine learning model; and predicting, based on a classification, whether the fundus image is indicative of presence of neurodevelopmental disorder in the subject, wherein the machine learning model has been trained on stored fundus images obtained from at least two group of subjects who have previously been diagnosed as having the neurodevelopmental disorder, the machine learning model is trained by processes comprising; extracting a first embedding vector and a second embedding vector from a first fundus image and a second fundus image, respectively; and maximizing a similarity between the first embedding vector and the second embedding vector.

Abstract: Provided is an apparatus for recognizing an object that includes an object inference module configured to process an original image captured by a camera module and generate an image of a size to be input to a machine learning inference model, wherein the object inference module includes the machine learning inference model, and outputs a result of recognition and classification of an object inferred through the machine learning inference model, and the machine learning inference model processes an input image to infer an object included in the input image.

Abstract: System and techniques are described herein for processing images to detect expressions of a subject. In one illustrative example, a method of recognizing facial expressions in one or more images includes obtaining, by a computing device, a first image of a person; obtaining expression information based on the first image and an anchor image associated with the person; and determining an expression classification associated with the first image based on the expression information.

Abstract: A method for operating a technical system and a technical system. The method includes providing for at least one class at least one class attribute comprising a description for members of the class, providing features characterizing a digital image, determining a class of the at least one class that classifies the digital image depending on the features, and determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image. The at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image. The method further includes operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute.

Abstract: An image processing method including: obtaining a target feature and a reference feature, the target feature is a feature obtained by performing feature extraction on a target object in a target image, and the reference feature is a feature obtained by performing feature extraction on a reference object in a reference image; obtaining a migration matrix, the migration matrix is used for controlling the reference feature to be fused with the target feature at a designated weight; fusing the target feature and the reference feature according to the migration matrix to obtain a fused feature; and generating a migration result image according to the fused feature and the target image. An image processing apparatus, an electronic device, a storage medium and a program product are also disclosed.

Abstract: Apparatuses, systems, and techniques to track object movements in a video. In at least one embodiment, a rate, at which one or more neural networks is to identify one or more objects within the video, may be adjusted based, at least in part on one or more user-selected portions of a video frame.

Abstract: A reconstruction method for spectral image includes: obtaining a measurement image of an imaging object, and reconstructing and obtaining a spectral image of the imaging object according to the measurement image and a pre-calibrated sensing matrix. The spectral image includes spectral information at different position points of the imaging object. Through the method, defects of the time-consuming spectral image reconstruction method and the low resolution of the obtained spectral image in the related art can be overcame and the spectral image of the target imaging object can be obtained by reconstructing quickly and the obtained spectral image has high spatial resolution and no mosaic.

Abstract: A system for judging or determining position or posture, and/or a time on a line crossed by at least one competitor or racer in a sporting competition, includes at least an apparatus for taking a photograph when an intermediate line or a finishing line of the competition is crossed, the image of the photofinish type taken defining an input of the system, and a processing unit connected to the photographic apparatus to combine the photofinish image received with a processing of an algorithm using artificial intelligence for automatically placing one or more cursors on the photofinish image.

Abstract: Provided are a method for segmenting sign language into morphemes, a method for predicting morpheme positions, and a method for augmenting data. A system for analyzing sign language according to an embodiment of the present invention comprises: a recognition unit which recognizes key points of a speaker from a sign language video; and a prediction unit which inputs the recognized key points into an artificial intelligence model, segments the sign language into morphemes, and predicts position information of the segmented morphemes. Accordingly, by recognizing the morphemes of the sign language video frame by frame on the basis of a skeletal model and thereby segmenting the sign language into morphemes and predicting morpheme positions, it is possible to lay the foundations for accurate sign language translation.

Abstract: Methods, apparatus, and computer program products for multimodal artificial intelligence-based communication systems are provided herein. A computer-implemented method includes generating, using a first set of one or more artificial intelligence techniques, identifying information for one or more objects detected in image data associated with at least one user query; generating at least one updated version of the at least one user query by processing, using a second set of one or more artificial intelligence techniques, at least a portion of the at least one user query in conjunction with at least a portion of the identifying information for the one or more objects; generating at least one response to the at least one updated version of the at least one user query; and performing one or more automated actions based at least in part on the at least one response.

Abstract: According to one aspect, the subject matter described herein includes a method for extracting text from unstructured documents. The method includes receiving a page of an unstructured document; extracting, from the page, a glyph identifier and a glyph position for each glyph on the page; and generating an adjacency graph based on the glyph positions for each glyph on the page, each node in the graph corresponding to a glyph and comprising glyph information that includes at least the glyph identifier and the glyph position for the respective glyph. The method further includes processing the adjacency graph by a machine learning model to classify edges and nodes in the adjacency graph, then grouping the glyphs according to their edge and node classifications to produce text output.

Abstract: An apparatus for estimating a depth is introduced. The apparatus may comprise a camera configured to capture an image may comprise an object, and a processor configured to perform, based on a deep learning model, a deep learning process associated with the image, obtain, based on the deep learning process associated with the image, a first depth value, obtain a partial image by masking a partial region of the image, perform, based on the deep learning model, a deep learning process associated with the partial image, obtain, based on the deep learning process associated with the partial image, a second depth value, train the deep learning model to reduce a deviation between the first depth value and the second depth value, and estimate, based on the trained deep learning model, a depth of the object.