Abstract: An approach is provided for lane width estimation from incomplete lane marking detections of a road lane. The approach, for example, involves generating one or more perpendicular lines respectively from location centers of one or more first lane marking detections. A respective lane marking detection represents at least a portion of a boundary of the road lane as a line delimited by two location data points in accordance with detections by at least one sensor device onboard at least one vehicle. The approach also involves identifying second lane marking detections that each respectively intersect one of the one or more perpendicular lines. The approach further involves selecting one or more candidate lane widths based on one or more respective distances from the location centers to the second lane marking detections. The approach further involves determining an estimated lane width of the road lane based on the one or more candidate lane widths.

Abstract: An object orientation indicator for indicating the orientation of an object, such as the vertical positioning of an object to be inserted or being inserted into a surface. The object orientation indicator is configured to determine or indicate when an object being inserted into a surface parallel or non-parallel to the ground is vertically level, i.e. perpendicular to the ground. The object orientation indicator may also be configured to determine or indicate if a preexisting vertical object inserted into a surface parallel or non-parallel to the ground remains in its originally inserted orientation. The object orientation indicator may include a housing assembly and a display panel.

Abstract: A trusted graph data node classification method includes: (1) inputting a topological graph and node features, and calculating a discrete Ricci curvature of the discrete topological graph; (2) preprocessing the curvature and the node features; (3) mapping the curvature, reconstructing original features, and performing a semi-supervised training on graph data containing adversarial examples; and (4) performing a classification on unlabeled nodes. The new method uses a discrete curvature to extract topological information, and uses a residual network to reconstruct node feature vectors without knowing the technical details of the adversarial examples, and without using a large number of adversarial examples for adversarial training. Hence, the system effectively defends against attacks from adversarial examples on the graph data, outperforms the existing mainstream models in terms of accuracy when used in data without adversarial examples, and is thus a trusted node classification system.

Abstract: A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting locations of utility assets. One of the methods includes receiving an input image of an area in a first geographical region; generating, from the input image and using a generative adversarial network, a corresponding reference image; and generating, by an object detection model and from the reference image, an output that identifies respective locations of one or more utility assets with reference to the input image.

Abstract: The present disclosure discloses a method, apparatus and system for customer group analysis, and a storage medium. The method includes: obtaining video images of a customer group passing by a display apparatus; recognizing and tracking head images of the customer group in the video images, and determining behavior characteristics of individuals in the customer group; and performing statistical analysis on the behavior characteristics of the customer group corresponding to the display apparatus, so as to update a quantity of individuals corresponding to each behavior characteristic.

Abstract: Embodiments disclosed herein utilize biometric verification from wearable devices to approve access for shared spaces. Requests to occupy the shared spaces are received and conditional reservations are made pending health checks of the requesters. Prior to occupation, fresh biometric measurements are captured via the user's wearable device, which include physiological signals validating both ongoing good health as well as identity match to the original requester. When recent biometric verification indicates both healthy status and unchanged identity, access to the reserved space is approved. These embodiments securely manage reservations while protecting the health of other occupants through conditional access related to the requester's current health condition.

Abstract: Systems and methods for determining whether input medical images are out-of-distribution of training images on which a machine learning based medical imaging analysis network is trained are provided. One or more input medical images of a patient are received. One or more reconstructed images of the one or more input medical images are generated using a machine learning based reconstruction network. It is determined whether the one or more input medical images are out-of-distribution from training images on which a machine learning based medical imaging analysis network is trained based on the one or more input medical images and the one or more reconstructed images. The determination of whether the one or more input medical images are out-of-distribution from the training images is output.

Abstract: As described herein, various embodiments of the present invention disclose techniques that improve efficiency of performing image-based machine learning operations on large images while limiting accuracy drawbacks of partial processing of those large images by using composite-tiled image embeddings for composite-tiled images generated by merging multiple tiled images that are generated using multiple tiling mechanisms. For example, in some embodiments, given an input image that comprises R image regions, each tiled image comprises N selected image regions of the R image regions that are selected in accordance with a tiling mechanism (where N<R). In this way, given T tiling mechansisms, T tiled images are generated, and the T tiled images are merged to generate a composite-tiled image. Accordingly, by using T tiling mechansisms, various embodiments enable reducing the size of feature data provided to an image processing machine learning model by selecting non-holistic subsets of an input image.

Abstract: A system includes a camera capturing images in a first field of view of a sports ball bouncing and rolling, a storage arrangement, and a processing arrangement. The storage arrangement includes a three-dimensional 3D model of a part of a sports play area and the processing arrangement is configured to: detect a pixel location of a ball in an image; determine, based on intrinsic and extrinsic calibration parameters, a camera-ball line comprising a straight line passing through the camera in the direction of the sports ball and to determine, based on the 3D model, an intersection point of the camera-ball line with the 3D model. The processor outputs the intersection point as a 3D position of the ball in the image.

Abstract: Disclosed is a super-resolution flow field reconstruction method, including: superimposing a plurality of frames of contrast-enhanced ultrasound images that are in one time interval and each of which includes images of a plurality of microbubbles, to obtain a superimposed image including a plurality of microbubble trajectories of the plurality of microbubbles; performing straight line fitting on the plurality of microbubble trajectories, to obtain a plurality of microbubble trajectory straight lines of the plurality of microbubbles, respectively; determining directions and velocities of instantaneous movements of the plurality of microbubbles in the time interval; and reconstructing a super-resolution flow field.

Abstract: An imaging analysis device according to the present invention includes: an analysis execution unit (1) configured to perform analysis by a predetermined analysis method for each of a plurality of measurement points set in a measurement area on a sample and collect imaging data; a reference image acquiring unit (2) configured to acquire a reference image for the measurement area; a regression analysis executer (36) configured to, with respect to the imaging data and the reference image obtained for the same sample, perform predetermined regression analysis calculation with the imaging data as an explanatory variable and data constituting the reference image as a target variable and acquire a regression model; and a predicted image creator (37) configured to apply, to the regression model, imaging data obtained by the analysis execution unit using a sample different from the sample used in the regression analysis executer, and create a predicted image based on a pseudo regression analysis result.

Abstract: An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.

Abstract: Disclosed are devices, systems and methods for processing an image. In one aspect a method includes receiving an image from a sensor array including an x-y array of pixels, each pixel in the x-y array of pixels having a value selected from one of three primary colors, based on a corresponding x-y value in a mask pattern. The method may further include generating a preprocessed image by performing preprocessing on the image. The method may further include performing perception on the preprocessed image to determine one or more outlines of physical objects.

Abstract: A system, device and method of imaging using a real-time, AI-enabled analysis device coupled to an imaging device during an image scan of a subject includes: receiving data corresponding to a plurality of image frames from the imaging device and user input identifying a region of interest (ROI) in a first image frame; providing data corresponding to the first image frame, including the identified ROI and data corresponding to the remaining image frames to the AI-enabled data processing system; accepting a plurality of valid image frames from the plurality of image frames based on a predefined set of computer vision rules and a minimum accepted frame threshold; calculating, frame by frame, an ROI function value of the plurality of valid image frames; determining whether a predetermined ROI function value has been reached; and alerting an operator of the imaging device that the predetermined ROI function value has been reached.

Abstract: A method for estimating a region associated with a deformity on a surface portion of the skin of a mammal, the method comprising capturing a three-dimensional image of the surface portion to produce first data, the first data comprising depth data, and generating second data from the first data. The second data comprises curvature data. The method further comprises combining the first data and the second data to produce third data, and performing region growing on the third data.

Abstract: The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including generating an imaging data set having both scan data and digitized biopsy data from a patient with small cell lung cancer (SCLC). Scan derived features are extracted from the scan data and biopsy derived features are extracted from the digitized biopsy data. A radiomic-pathomic risk score (RPRS) is calculated from one or more of the scan derived features and one or more of the biopsy derived features. The RPRS is indicative of a prognosis of the patient.

Abstract: An image sensor package, a system, and a method for counting fine particles by using a virtual grid line are provided. The image sensor package includes an image sensor array, a grid pattern layer formed on an outer area of the image sensor array and including a plurality of protruding patterns spaced apart from each other while protruding toward the central area of the image sensor array to form a virtual grid line, a dam pattern layer formed on the grid pattern layer, having a specific height, and configured to form a channel or a chamber for receiving the fine particles to be counted, and a cover glass formed on the dam pattern layer.

Abstract: Disclosed herein is a swappable modular-based radiofrequency (RF) frontend that is reconfigurable to form transmitting (TX) and receiving (RX) phased array systems for diverse applications. Such swappable RF frontend may be used with unique spatial and spectral optical processing of complex RF signals over an ultra-wide frequency band. The swappable RF front end may be used in conjunction with an optically upconverted imaging receiver and/or in conjunction with optically addressed phased array technologies transmitters.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for detecting and classifying an exposure defect in an image using neural networks trained via a limited amount of labeled training images. An image may be applied to a first neural network to determine whether the images includes an exposure defect. Detected defective image may be applied to a second neural network to determine an exposure defect classification for the image. The exposure defect classification can includes severe underexposure, medium underexposure, mild underexposure, mild overexposure, medium overexposure, severe overexposure, and/or the like. The image may be presented to a user along with the exposure defect classification.