Abstract: Disclosed herein is a system and method directed to object tracking using plurality of cameras. The system includes the plurality of cameras disposed around a playing surface in a mirrored configuration, and where the plurality of cameras are time-synchronized. The system further includes logic that, when executed by a processor, causes performance of operations including: obtaining a sequence of images from the plurality of cameras, continuously detecting an object in image pairs at successive points in time, wherein each image pair corresponds to a single point in time, continuously determining a location of the object within the playing space through triangulation of the object within each image pair, determining wall coordinates of a wall that the object is expected to contact based on the continuously determined location of the object and causing rendering of a visual graphic based on the wall coordinates.

Abstract: An apparatus for predicting performance of a wheel in a vehicle: includes a learning device that generates a latent space for a plurality of two-dimensional (2D) wheel images based on a convolutional autoencoder (CAE), extracts a predetermined number of the plurality of 2D wheel images from the latent space, and learns a dataset having the plurality of 2D wheel images and performance values corresponding to the plurality of 2D wheel images; and a controller that predicts performance for the plurality of 2D wheel images based on a performance prediction model obtained by the learning device.

Abstract: A camera module may include an image sensor including an optical device configured to rotate about at least one of an x-axis, a y-axis, and a z-axis perpendicular to each other, in response to a mode signal, and configured to generate a plurality of first images, each first image generated when the optical device is at a different position; and an image signal processor (ISP) configured to process the plurality of first images, wherein the ISP is further configured to obtain a plurality of parameters pre-stored according to the mode signal, correct the plurality of first images by using the plurality of parameters, and generate a second image by merging the corrected first images.

Abstract: Various implementations disclosed herein include devices, systems, and methods that determine a 3D location of an edge based on image and depth data. This involves determining a 2D location of a line segment corresponding to an edge of an object based on a light-intensity image, determining a 3D location of a plane based on depth values (e.g., based on sampling depth near the edge/on both sides of the edge and fitting a plane to the sampled points), and determining a 3D location of the line segment based on the plane (e.g., by projecting the line segment onto the plane). The devices, systems, and methods may involve classifying an edge as a particular edge type (e.g., fold, cliff, plane) and detecting the edge based on such classification.

Abstract: Embodiments of the present invention disclose techniques for outputting content aware video based on at least one a video application use case. The technique recognizes objects associated with the use case and performs enhancement of the objects based on content-aware rules, and composes at least some of the objects in an output frame based on on content-aware frame composition templates. Embodiments of the present invention also disclose systems for implementing the above techniques.

Abstract: Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for a wearable multimedia device and cloud computing platform with an application ecosystem for processing multimedia data captured by the wearable multimedia device. In an embodiment, a wearable multimedia device determines a plurality of points of interest and, for each of the points of interest, a heading of that point of interest relative to the device, and a distance between that point of interest and the device. The device presents a user interface including a plurality of user interface elements arranged according to one or more curved layers. Each of the user interface elements corresponds to a different one of the points of interest. A position of each of the user interface elements relative to each of the other user interface elements in the user interface is determined based on the first data and the second data.

Abstract: An information processing device according to the present invention includes a person extraction means that extracts a person in a captured image, an action extraction means that extracts an action of a person group including a plurality of persons other than a given person in the captured image, and an identification means that identifies a given person group based on a result of extraction of the action of the person group.

Abstract: Methods, systems, and apparatus for receiving a request for a damage propensity score for a parcel, receiving imaging data for the parcel, wherein the imaging data comprises street-view imaging data of the parcel, extracting, by a machine-learned model including multiple classifiers, characteristics of vulnerability features for the parcel from the imaging data, determining, by the machine-learned model and from the characteristics of the vulnerability features, a damage propensity score for the parcel, and providing a representation of the damage propensity score for display.

Abstract: Provided is a mobile terminal including a front case, a display disposed at a front surface of the front case, a rear case disposed to oppose a rear surface of the front case, and a finger scan sensor part coupled to the rear surface of the front case. The finger scan sensor part may be configured to sense at least a portion of light incident after passing through the display and the front case among light reflected by an external object. Other embodiments are implementable.

Abstract: An image processing apparatus applies predetermined image processing to an actual image captured by an image capturing device and recognizes a target within the captured image with use of the image which the predetermined image processing has been applied to. The image processing apparatus applies, to tones of the actual image, the predetermined image processing for reducing a difference from tones of a CG image which shows the same scene and which is presented through computer graphics.

Abstract: An apparatus for edge aware upscaling is described herein. The apparatus comprises a potential edge detector, a thin-edge detector, a one-directional edge detector, a correlation detector, and a corrector. The potential edge detector identifies potential edge pixels in an input image, and the thin-edge detector detects thin edges in the potential edge pixels of the input image. The one-directional edge detector detects one-directional edges in the potential edge pixels of the input image, and the correlation detector detects strongly correlated edges in the potential edge pixels of the input image. The corrector derives a target output value based on an edge type and classification of a corresponding input pixel as identified by a source map point.

Abstract: A system and method for license plate recognition is provided in the present disclosure. The method may include obtaining an image including a license plate mounted on a vehicle, and identifying at least one feature point associated with the vehicle. The method may also include determining an approximate angle range within which a tilt angle of the license plate is located based on the at least one feature point, and determining the tilt angle of the license plate within the approximate angle range. The method may further include performing a tilt correction on the license plate based on the tilt angle of the license plate.

Abstract: An object of the present invention is to achieve both suppression of data amount of an image processing system that learns a collation image to be used for image identification using a discriminator and improvement of identification performance of the discriminator. In order to achieve the above object, there is proposed an image processing system including a discriminator that identifies an image using a collation image, the image processing system further including a machine learning engine that performs machine learning of collation image data required for image identification. The machine learning engine searches for a successfully identified image using an image for which identification has been failed, and adds information, obtained based on a partial image of the image for which identification has been failed and which has been selected by an input device to the successfully identified image obtained by the search to generate corrected collation image data.

Abstract: A map update system includes a data transmission device mounted on a vehicle and a map server which stores map data. The data transmission device generates sensor data representing a road environment of surroundings of the vehicle in a predetermined position, calculates a matching degree between the road environment of the surroundings of the vehicle and a road environment in the predetermined position represented by the map data, and causes a communication circuit mounted on the vehicle to transmit the sensor data and information representing the matching degree to the map server. The map server transmits the map data by utilizing sensor data, among the sensor data received via a communication device, having a matching degree less than a matching degree threshold with a higher priority than sensor data having a matching degree or greater than the matching degree threshold.

Abstract: Systems and methods for image processing are described. Embodiments identify a training set including a first image that includes a ground truth blur classification and second image that includes a ground truth blur map, generate a first embedded representation of the first image and a second embedded representation of the second image using an image encoder, predict a blur classification of the first image based on the first embedded representation using a classification layer, predict a blur map of the second image based on the second embedded representation using a map decoder, compute a classification loss based on the predicted blur classification and the ground truth blur classification, train the image encoder and the classification layer based on the classification loss, compute a map loss based on the blur map and the ground truth blur map, and train the image encoder and the map decoder.

Abstract: The pose of a wide-angle image is determined by dewarping regions of the wide-angle image, determining estimated poses of the dewarped regions of the wide-angle image and deriving a pose of the wide-angle image from the estimated poses of the of the dewarped regions. The estimated poses of the dewarped regions may be determined by comparing features in the dewarped regions with features in prior dewarped regions from one or more prior wide-angle images, as well as by comparing features in the dewarped regions with features in a point cloud.

Abstract: An apparatus is disclosed which is configured to perform or control obtaining or causing obtaining at least one image of at least a part of an object; obtaining or causing obtaining information representative of a distance between at least one feature of the object present in the image and at least one reference feature of the object present in the image; generating or causing generating a string of characters based on the obtained information, the string of characters comprising at least a first portion representative of the distance between the at least one feature of the object present in the image and the at least one reference feature of the object present in the image; associating or causing associating information representing the string of characters with process information of the object.

Abstract: A valuation system to identify interior features of a property, identify exterior features related to interior features using aerial images, and to generate property valuations based on the identified features is provided. The valuation system identifies, using a computer vision module, interior features based on interior image data (e.g., photos) of a property, and further identifies exterior features associated with any of the interior features based on an aerial photo of the property. For example, trees and buildings (e.g., exterior features) adjacent to a window (e.g., an interior feature) can be identified by a computer vision module through the combination of interior and exterior image data. In other words, the identification of property features by the computer vision module can be enriched by correlating interior image data (e.g., photos, video walkthroughs) to exterior image data (e.g., satellite photos, aerial photos).

Abstract: Methods, systems, and apparatuses related to autonomous vehicle object detection are described. A method can include receiving, by an autonomous vehicle, an indication that the autonomous vehicle has entered a network coverage zone generated by a base station and performing an operation to reallocate computing resources between a plurality of different types of memory devices associated with the autonomous vehicle in response to receiving the indication. The method can further include capturing, by the autonomous vehicle, data corresponding to an unknown object disposed within a sight line of the autonomous vehicle and performing, using the reallocated computing resources, an operation involving the data corresponding to the unknown object to classify the unknown object.

Abstract: A weight estimation unit estimates, from a person's image in which a person is included, a weight of a human body part of the person in accordance with an existence probability of the human body part of the person. A feature creation unit creates a feature for verification based on a weight of the person's image and a weight estimated by the weight estimation unit. A verification feature storage unit stores a feature of the person who is the object of verification as a verification feature. A person verification unit compares the feature for verification created by the feature creation unit and the verification feature stored in the verification feature storage unit.