An autonomous/assisted driving system (ADS) obtains a top view representation of labels of a traffic environment in Cartesian coordinates from sample data. The system obtains a transformation matrix for transforming Cartesian coordinates in an observation coordinate system of a perspective of an environmental sensor of the ADS. The transformation matrix is applied to the top-view representation of the labels to obtain a perspective representation of the traffic environment in the observation coordinate system.

A method of identifying and scaling a region of interest on a display device is presented. The region of interest is detected based on the rate of change between frames. A size of the display area of the display device and aspect ratio are determined. The detected region of interest is scaled to fit the display area in full screen mode based on the display area and the aspect ratio. The region of interest may be, for example, an active video region. In full screen mode, any static or slow-moving images are hidden.

A method of identifying and scaling a region of interest on a display device is presented. The region of interest is detected based on the rate of change between frames. A size of the display area of the display device and aspect ratio are determined. The detected region of interest is scaled to fit the display area in full screen mode based on the display area and the aspect ratio. The region of interest may be, for example, an active video region. In full screen mode, any static or slow-moving images are hidden.

Embodiments provide a method of using image-based tube top circle detection that includes extracting, from one of a series of images of a tube tray, a region of interest (ROI) patch having a target tube top circle and boundaries constrained by two dimensional (2D) projections of different types of tube top circle centers. The method also includes calculating an edge gradient magnitude map of the ROI patch and constructing a three dimensional (3D) map of a circle parameter space, each location in the 3D map corresponding to a circle parameter having a center location and a diameter. The method further includes accumulating weighted votes in the 3D map from edge points in the edge gradient magnitude map along edge point gradient directions, determining locations in the 3D map as circle candidates based on the accumulated votes and selecting a target tube top circle based on the greatest accumulated votes.

Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior. Following distance over time is determined, and simplified following distance data is generated for use at a management device.

Exemplary methods, systems, apparatus, and computer programs are disclosed for an unmanned aerial system (UAS) inspection system that includes an unmanned aerial system and analysis system for exterior building envelopes and energy performance evaluation and simulation. The UAS can autonomously and systematically collect data for a building's exterior using a payload comprising (i) nondestructive testing (NDT) sensors configured for imaging (visible, infrared, or more) the building and (ii) one or more multi-spectral sensors (LiDAR, ultrasound, radar, or more). The acquired sensor data are provided to an analysis system comprising computer vision (CV) and signal processing modules configured to analyze the acquired data to i) identify building objects (doors, windows, rooftop units, and others) ii) characterize envelope properties (components, heat resistivity, or others) and 3) identify initial thermal anomalies (thermal bridges, physical defects, or infiltration/exfiltration) in a processing pipeline.

Methods and apparatuses are described for automated cross-browser user interface testing. A computing device captures (i) a first image file corresponding to a first current user interface view of a web application on a first testing platform and (ii) a second image file corresponding to a second current user interface view of a web application on a second testing platform. The computing device prepares the image files, and compares the prepared image files using a structural similarity index measure. The computing device determines that the prepared first image file and the prepared second image file represent a common user interface view when the structural similarity index measure is within a predetermined range. The computing device highlights corresponding regions that visually diverge from each other in each of the prepared image files and transmits a notification message comprising the highlighted image files.

Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior. Following distance over time is determined, and simplified following distance data is generated for use at a management device.

A detection apparatus and method for parking space detection and an image processing device where the detection method includes: performing conversion on a side-view image that is photographed on the parking space and is acquired from a camera, to obtain a top-view image including said parking space; acquiring an edge image including a plurality of edges based on gradient information of said top-view image; performing conversion on said edge image and obtains a voting vector according to said gradient information, and determining marking lines according to peak values of said voting vector; and determining one or more parking spaces based on a plurality of said marking lines.

Provided are a device for a digital assay of targets according to an exemplary embodiment of the present disclosure and a method using the same. The digital assay method of targets according to the exemplary embodiment of the present disclosure includes acquiring an image for a plurality of microdroplets, predicting at least one region based on the image for the plurality of microdroplets using an artificial neural network-based prediction model configured to segment at least one region among positive microdroplets, negative microdroplets, and atypical microdroplets, with the image for the plurality of microdroplets as an input, determining a number for the plurality of microdroplets based on the at least one region, and providing quantitative data of targets based on the number for the plurality of microdroplets.