Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously provide traffic monitoring, and traffic management which improves the safety and efficiency of a roadway.

Screen response validation of robot execution for robotic process automation (RPA) is disclosed. Whether text, screen changes, images, and/or other expected visual actions occur in an application executing on a computing system that an RPA robot is interacting with may be recognized. Where the robot has been typing may be determined and the physical position on the screen based on the current resolution of where one or more characters, images, windows, etc. appeared may be provided. The physical position of these elements, or the lack thereof, may allow determination of which field(s) the robot is typing in and what the associated application is for the purpose of validation that the application and computing system are responding as intended. When the expected screen changes do not occur, the robot can stop and throw an exception, go back and attempt the intended interaction again, restart the workflow, or take another suitable action.

A system may be configured to collect geospatial features (in vector form) such that a software application is operable to edit an object represented by at least one vector. Some embodiments may: generate, via a trained machine learning model, a pixel map based on an aerial or satellite image; convert the pixel map into vector form; and store the vectors. This conversion may include a raster phase and a vector phase. A system may be configured to obtain another image, generate another pixel map based on the other image, convert the other pixel map into vector form, and compare the vectors to identify changes between the images. Some implementations may cause identification, based on a similarity with converted vectors, of a more trustworthy set of vectors for subsequent data source conflation.

A driver assistance system detects lane markings in a perspective image of a road in front of a vehicle. The driver assistance system extracts a plurality of features, in particular lane markings, from the perspective image for generating a set of feature coordinates, in particular lane marking coordinates. The system generates a plurality of pairs of feature coordinates, each pair defining a straight line, and estimates a lane curvature on the basis of a subset of the pairs of feature coordinates. For each pair a straight line defined by the pair intersects a predefined target portion of the perspective image, the predefined target portion including a plurality of possible positions of a vanishing point.

In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.

The present disclosure relates to a method and apparatus for automatic detection of anatomical sites from tomographic images. The method includes: receiving 3D images obtained by a CT or an MRI system, transforming the images to the DICOM standard patient-based coordinate system, pre-processing the images to have normalized intensity values based on their modality, performing body segmentation, cropping the images to remove excess areas outside the body, and detecting different anatomical sites including head and neck, thorax, abdomen, male pelvis and female pelvis, wherein the step of detecting different anatomical sites comprises: performing slice-level analyses on 2D axial slices to detect the head and neck region using dimensional measurement thresholds based on human anatomy, calculating lung ratios on axial slices to find if lungs are present, determining whether 3D images with lungs present span over the thoracic region, abdomen region, or both, conducting 2D connectivity analyses on axial slices to detect the pelvis region if two separate leg regions are found and differentiating detected pelvis regions as either male pelvis or female pelvis regions based on human anatomy.

A computer implemented method for assessing the growth of germinative units, the method comprising the steps of: processing an image of a sample comprising germinative units to identify at least one germinative growth, which is a growth germinating from a germinative unit, present in the image and determine the length of the at least one identified growth; and calculating an image average length of the at least one determined length.

A data processing method includes: receiving first image data transmitted by a first client, and storing the first image data to a receive queue, the first image data being obtained by the first client during running of a cloud application and includes an object; performing image recognition processing on the first image data in the receive queue, and storing, to the receive queue during image recognition processing of the first image data, second image data obtained and transmitted by the first client, to obtain an updated receive queue; and transmitting, when a first object region containing the object in the first image data is extracted through image recognition processing, first object image data in the first object region to a target cloud application server, and simultaneously performing image recognition processing on the second image data with a latest receiving timestamp in the updated receive queue.

An object tracking system includes a first sensor, a second sensor, and a tracking system. The first sensor is configured to capture a first frame of a global plane for at least a first portion of a space. The second sensor is configured to capture a second frame of at least a second portion of the space. The tracking system is configured to determine the object is within an overlap region with the second sensor based on a first pixel location. The tracking system is further configured to determine a first coordinate in the global plane for the object, to determine a second pixel location in the second frame for the object based on the first coordinate, and to store the second pixel location with an object identifier a tracking list associated with the second sensor.

A method for generating a skeleton in an image of a cavity of an organ of a body includes receiving a map of the cavity, the map including surface voxels and interior voxels. A subset of the interior voxels is generated, of candidate locations to be on the skeleton. The subset is pruned by removing outlier candidate locations. Using a geometrical model including a statistical model, the candidate locations remaining in the pruned subset are spatially compressed. The compressed candidate locations are connected to produce one or more centerlines of the skeleton. At least the skeleton is displayed to user.