Abstract: The disclosure relates to a system, method, and computer readable medium for processing images to detect text. Illustratively, the method includes subdividing received images with a partitioner to generate one or more sub-images of respective objects in the received images, and subdividing the generated one or more sub-images with a text detector to generate, for each sub-image, one or more text box sub-images. The method includes processing the generated text box sub-images with a text box simplifier to generate simplified text box images having less information than the text box sub-images, and determining text from the simplified text box images.

Abstract: The disclosure relates to a method, system, and memory for automating vehicle ingress or egress. Illustratively, the method includes receiving images of a vehicle from imaging devices, and processing the images with an automation system. The processing includes determining one or more properties associated with the vehicle, and receiving a data file based on an interaction between a driver and an input terminal in communication with the automation system. The method includes generating a check-in event, and validating the check-in event based on properties associated with the vehicle. The method includes determining a destination of the vehicle based on the validated check-in event, and generating a guidance data file that comprises path guidance from a location of the vehicle to the determined destination, the guidance data file generating visual elements to indicate the destination once executed by a device associated with the vehicle.

Abstract: A method for pose estimation includes receiving an image containing an object, a first pose of the object in the image, and 3D boundary features of a model corresponding to the object; computing a first pose confidence of the first pose based on the image, the 3D boundary features, and the first pose; perturbing the first pose to obtain a second pose; computing a second pose confidence of the second pose based on the image, the 3D boundary features, and the second pose; determining if the second pose confidence is greater than the first pose confidence, and; and outputting the second pose and the second pose confidence if the second pose confidence is greater than the first pose confidence.

Abstract: Training a pose detection algorithm and deriving an object pose using a trained pose detection algorithm. Specifying a first view of a 3D model with respect to a virtual camera, generating a depth map by projecting the 3D model based on the first view, generating a second view by perturbing the first view, selecting reference 2D locations in an overlapping area of the 3D model between the first view and the second view. Transforming the reference 2D locations into a 3D point set corresponding to the second view. For each reference 2D location, calculating a depth difference between the first view and the second view at the reference 2D location. Associating the perturbation between the second view and the first view with the calculated depth differences, and training an algorithm model to identify a pose of an object corresponding to the 3D model.

Abstract: Training a pose detection algorithm and deriving an object pose using a trained pose detection algorithm. Specifying a first view of a 3D model with respect to a virtual camera, generating a depth map by projecting the 3D model based on the first view, generating a second view by perturbing the first view, selecting reference 2D locations in an overlapping area of the 3D model between the first view and the second view. Transforming the reference 2D locations into a 3D point set corresponding to the second view. For each reference 2D location, calculating a depth difference between the first view and the second view at the reference 2D location. Associating the perturbation between the second view and the first view with the calculated depth differences, and training an algorithm model to identify a pose of an object corresponding to the 3D model.

Abstract: A method for pose estimation includes receiving an image containing an object, a first pose of the object in the image, and 3D boundary features of a model corresponding to the object; computing a first pose confidence of the first pose based on the image, the 3D boundary features, and the first pose; perturbing the first pose to obtain a second pose; computing a second pose confidence of the second pose based on the image, the 3D boundary features, and the second pose; determining if the second pose confidence is greater than the first pose confidence, and; and outputting the second pose and the second pose confidence if the second pose confidence is greater than the first pose confidence.