Abstract: In a method for determining the geographic position and orientation of a vehicle, an image of the vehicle's surroundings is recorded by at least one camera of the vehicle, wherein the recorded image at least partially comprises regions of the vehicle's surroundings on the ground level. Classification information is generated for the individual pixels of the recorded image and indicates an assignment to one of several given object classes, wherein based on this assignment, a semantic segmentation of the image is performed. Ground texture transitions based on the semantic segmentation of the image are detected. The detected ground texture transitions are projected onto the ground level of the vehicle's surroundings. The deviation between the ground texture transitions projected onto the ground level of the vehicle's surroundings and ground texture transitions in a global reference map is minimized. The current position and orientation of the vehicle in space is output based on the minimized deviation.

Abstract: A user terminal apparatus is provided. The user terminal apparatus includes a camera configured to obtain a captured image; a storage configured to store a food intake history; a processor configured to extract a food image from the captured image and determine a food type of a food item included in the captured image based on feature information of the extracted food image and the previously stored food intake history; and a display configured to display relevant information about the determined food type.

Abstract: A method of secure classification of input data by a convolutional neural network (CNN), including (a) determination, by application of the CNN to the input data, of a first classification vector associating with each of a plurality of potential classes a representative integer score of the probability of the input data belonging to the potential class, the first vector corresponding to one possible vector, each possible vector of the first set associating with each of the plurality of potential classes an integer score; (b) construction, from the first vector, of a second classification vector of the input data, such that the second vector also belongs to the first space of possible vectors and has a distance with the first vector according to a given distance function equal to a non-zero reference distance; and return of the second vector as result of the secure classification.

Abstract: Various methods and systems are provided for x-ray imaging. In one embodiment, a method for an image pasting examination comprises acquiring, via an optical camera and/or depth camera, image data of a subject, controlling an x-ray source and an x-ray detector according to the image data to acquire a plurality of x-ray images of the subject, and stitching the plurality of x-ray images into a single x-ray image. In this way, optimal exposure techniques may be used for individual acquisitions in an image pasting examination such that the optimal dose is utilized, stitching quality is improved, and registration failures are avoided.

Abstract: A computing system includes at least one processor, and a memory communicatively coupled to the at least one processor. The processor is configured to receive at least two successive radar images of precipitation data, generate a motion vector field using the at least two successive radar images, forecast linear prediction imagery of future precipitation using the motion vector field, and generate corrected output imagery corresponding to the forecasted linear prediction imagery of the future precipitation corrected by a first neural network. In addition, the processor is further configured to receive, by a second neural network, the linear prediction imagery, and one of observed imagery and the corrected output imagery, and distinguish, by the second neural network, between the corrected output imagery and the observed imagery to produce conditioned output imagery. The processor is also configured to display the conditioned output imagery on a display.

Abstract: Embodiments of the present disclosure provide a method, device, and computer program product for error evaluation. A method for error evaluation comprises in accordance with a determination that an error occurs in a data protection system, obtaining context information related to an operation of the data protection system; determining, based on the context information and using a trained deep learning model, a type of the error in the data protection system from a plurality of predetermined types, the deep learning model being trained based on training context information and a label on a ground-truth type of an error associated with the training context information; and providing the determined type of the error in the data protection system. In this way, it is possible to achieve automatic classification of errors in the data protection system, thereby improving the efficiency in error classification and saving the operation costs.

Abstract: Methods, systems and computer program products are provided for prefetching information and/or (pre)allocating computing resources based on predicting classification labels with temporal data. A trained temporal classification model forecasts events (e.g., too numerous for individual modeling) by predicting classification labels indicating whether events will occur, or a number of occurrences of the events, during each of a plurality of future time intervals. Time-series datasets, indicating whether events occurred, or a number of occurrences of the events, during each of a plurality of past time intervals, are transformed into temporal classification datasets. Classifications may be based, at least in part, on extracted features, such as data seasonality, temporal representation, statistical and/or real-time features. Classification labels are used to determine whether to take one or more actions, such as, for example, prefetching information or (pre)allocating a computing resource.

Abstract: A system and method leverages understanding of complex dribbling video clips by representing a video sequence with a single Dribble Energy Image (DEI) that is informative for dribbling styles recognition. To overcome the shortage of labelled data, a dataset of soccer video clips employs Mask-RCNN to segment out dribbling players and OpenPose to obtain joints information of dribbling players. To solve issues caused by camera motions in highlight soccer videos, the system registers a video sequence to generate a single image representation DEI and dribbling styles classification.

Abstract: A method for operating an environment sensor system of a vehicle, in particular of an autonomous motor vehicle, and an environment sensor system. The method includes the steps: producing first image data from a first image of a vehicle environment with the aid of a first sensor, performing a setpoint-actual comparison of the first image data with the second image data, and recognizing an operability of the environment sensor system based on the setpoint-actual comparison, the second image data including standard image data and/or image information from a second image of the vehicle environment, and the operability being recognized with the aid of a first artificial intelligence.

Abstract: An electronic device having a neural network framework for estimating optical flow is provided. The electronic device is connected to an image acquiring unit, which acquires images to be analyzed. The electronic device includes a storage unit, a feature extraction unit, an optical flow estimation unit and a refining unit. The storage unit stores a feature extraction module. The feature extraction unit is connected to the image acquiring unit and the storage unit. The optical flow estimation unit is connected to the feature extraction unit to generate an estimated optical flow. The refining unit is connected to the optical flow estimation unit to input the estimated optical flow to a refining module to obtain an estimated optical flow result. A method for estimating optical flow is also provided to reduce the number of training parameters required for estimating optical flow, thereby reducing a training time and improving training stability.

Abstract: An information processing device includes a processor. The processor obtains an input image, inputs the input image to a machine learning model that executes classification likelihood calculation processing to obtain, for each of candidate objects in the input image, likelihoods of belonging to the plurality of classes, executes first determination on whether or not each of the candidate objects is classified as a first class of the plurality of classes using a likelihood of belonging to the first class that is a likelihood having a negative correlation with likelihoods of belonging to other classes, executes second determination on whether or not each of the candidate objects that have been determined in the first determination as a non-first class is classified as the other classes, and outputting a result of classifying the candidate objects included in the input image using a result of the second determination.

Abstract: Devices and techniques are generally described for estimating three-dimensional pose data. In some examples, a first machine learning network may generate first three-dimensional (3D) data representing input 2D data. In various examples, a first 2D projection of the first 3D data may be generated. A determination may be made that the first 2D projection conforms to a distribution of natural 2D data. A second machine learning network may generate parameters of a 3D model based at least in part on the input 2D data and based at least in part on the first 3D data. In some examples, second 3D data may be generated using the parameters of the 3D model.

Abstract: An electronic device having a biometric sensor is provided. The electronic device includes a first region including a plurality of first pixels arranged in a first manner and a second region including a plurality of second pixels arranged in a second manner, a biometric sensor disposed in at least a part of the first region, and a processor electrically coupled with the display and the biometric sensor, and configured to receive a user input through the first region, and control the biometric sensor to detect biometric information corresponding to the user input.

Abstract: Systems and methods to localize objects for mapping applications may comprise a vehicle having an imaging device, a location sensor, and an edge processor. Using imaging data from the imaging device, location data from the location sensor, and bounding box data associated with objects, three-dimensional models of environments may be reconstructed using structure from motion algorithms and/or direct triangulation algorithms. After aligning the reconstructions to real-world environments based on the location data, objects may be accurately localized relative to real-world environments.

Abstract: An object tracking device includes an image buffer configured to store a plurality of images included in a video, a detection part configured to detect an object position by executing a detection process with respect to an object reflected in the plurality of images, a tracking part configured to execute a tracking process with respect to a tracking-image string representing a scope of images ranging from an image producing an object-detection result and an image producing a next object-detection process, and an integration part configured to calculate an integrative object position by integrating a detection result and a tracking result. Herein, the scope of images is determined based on an execution timing or a termination timing of the detection process, thus executing the detection process and the tracking process in parallel.

Abstract: A field-of-view at a first modeled target location of a first target disposed on a specimen can be configured, which can include moving the stage relative to the detector. The first modeled target location is determined by summing a first design target location and a navigational error provided by an online model. A first image of the field-of-view is grabbed using the detector. The field-of-view at a second modeled target location of a second target disposed on the specimen is configured. Concurrent with configuring the field-of-view at the second modeled target location, using a processor, the position of a first actual target location is determined using the first image. The online model is updated with a difference between the first design target location and the first actual target location.

Abstract: An electronic device is provided. The electronic device includes: a display panel, a display controller, and a host. The display controller is configured to control displaying of the display panel. The host is electrically connected to the display controller. The host executes an application to render a display image, and transmits the display image to the display controller through an image-transmission channel between the host and the display controller. The display controller detects whether pixels in a specific area of the display image satisfy a predetermined condition. In response to the pixels in the specific area satisfying the predetermined condition, the display controller triggers a virtual input signal, and transmits the virtual input signal to the host through a data-transmission channel between the host and the display controller, so that the host executes a specific operation corresponding to the virtual input signal.

Abstract: Aspects of the present disclosure describe systems, methods and structures for an efficient multi-person posetracking method that advantageously achieves state-of-the-art performance on PoseTrack datasets by only using keypoint information in a tracking step without optical flow or convolution routines. As a consequence, our method has fewer parameters and FLOPs and achieves faster FPS. Our method benefits from our parameter-free tracking method that outperforms commonly used bounding box propagation in top-down methods. Finally, we disclose tokenization and embedding multi-person pose keypoint information in the transformer architecture that can be re-used for other pose tasks such as pose-based action recognition.

Abstract: The present disclosure provides an image processing method, device and apparatus, and a computer-readable storage medium. The image processing method includes: obtaining an image and detecting multiple targets in the image; obtaining categories of multiple detected targets and first parameters of a detection frame corresponding to each detected target, and ranking all detected targets according to the first parameters in a descending order to obtain a dataset; obtaining a target with a highest first parameter in the dataset as a first detected target, and detecting whether the first parameter of a first detection frame corresponding to the first detected target satisfies a preset category condition of a category to which the first detected target belongs; detecting whether the first detection frame corresponding to the first detected target overlaps with a detection frame corresponding to a respective one of the plurality of detected targets except the first detected target.

Abstract: An image matching device that performs matching between a first image and a second image includes: a frequency characteristic acquisition unit configured to acquire a frequency characteristic of the first image and a frequency characteristic of the second image; a frequency characteristic synthesizing unit configured to synthesize the frequency characteristic of the first image and the frequency characteristic of the second image to generate a synthesized frequency characteristic; a determination unit configured to perform frequency transformation on the synthesized frequency characteristic to calculate a correlation coefficient map whose resolution coincides with a target resolution, and perform matching between the first image and the second image based on a matching score calculated from the correlation coefficient map; and a regulation unit configured to regulate the target resolution based on the matching score.