Abstract: Systems and methods for constructing and managing a unique road sign knowledge graph across various countries and regions is disclosed. The system utilizes machine learning methods to assist humans when comparing a new sign template with a plurality of stored sign templates to reduce or eliminate redundancy in the road sign knowledge graph. Such a machine learning method and system is also used in providing visual attributes of road signs such as sign shapes, colors, symbols, and the like. If the machine learning determines that the input road sign template is not found in the road sign knowledge graph, the input sign template can be added to the road sign knowledge graph. The road sign knowledge graph can be maintained to add signs templates that are not already in the knowledge graph but are found in real-world by integrating human annotator's feedback during ground truth generation for machine learning.

Abstract: Disclosed herein are representative embodiments of methods, apparatus, and systems used to deliver a prosthetic heart valve to a deficient valve. In one embodiment, for instance, a support structure and an expandable prosthetic valve are advanced through the aortic arch of a patient using a delivery system. The support structure is delivered to a position on or adjacent to the surface of the outflow side of the aortic valve (the support structure defining a support-structure interior). The expandable prosthetic valve is delivered into the aortic valve and into the support-structure interior. The expandable prosthetic heart valve is expanded while the expandable prosthetic heart valve is in the support-structure interior and while the support structure is at the position on or adjacent to the surface of the outflow side of the aortic valve, thereby causing one or more native leaflets of the aortic valve to be frictionally secured between the support structure and the expanded prosthetic heart valve.

Abstract: Methods and systems of building a knowledge graph based on event-based ontology of a scene and vehicle trajectory in the scene. Image data corresponding to a plurality of scenes captured by one or more cameras is received. Event-based ontology data corresponding to events occurring in the plurality of scenes is received. Via an object-tracking machine-learning model, the system determines (i) a presence of a plurality of vehicles in the image data, and (ii) a plurality of vehicle trajectories, each vehicle trajectory associated with a respective one of the vehicles. Using a clustering model, the vehicle trajectories are clustered. A knowledge graph is augmented based on the clustered vehicle trajectories and the event-based ontology.

Abstract: Methods and system of building and augmenting a knowledge graph regarding ontology of events occurring in images. Image data corresponding to a plurality of scenes captured by one or more cameras is received. A knowledge graph is built with event-based ontology data corresponding to events occurring in the plurality of scenes. One or more of the scenes is displayed to a plurality of crowdsourcing workers which provide natural-language input including event-based semantic annotations corresponding to the scene. Using natural language processing on the input, triples are generated. The knowledge graph is augmented with the generated triples to yield an augmented knowledge graph for use in determining event-based ontology associated with the plurality of scenes.

Abstract: Systems and method for machine-learning assisted road sign content prediction and machine learning training is disclosed. A sign detector model processes images or video with road signs. A visual attribute prediction model extracts visual attributes of the sign in the image. The visual attribute prediction model can communicate with a knowledge graph reasoner to validate the visual attribute prediction model by applying various rules to the output of the visual attribute prediction model. A plurality of potential sign candidates are retrieved that match the visual attributes of the image subject to the visual attribute prediction model, and the rules help to reduce the list of potential sign candidates and improve accuracy of the model.

Abstract: A method of road sign classification utilizing a knowledge graph, including detecting and selecting a representation of a sign across a plurality of frames, outputting a prompt initiating a request for a classification associated with the representation of the sign, classifying one or more images including the sign, querying the knowledge graph to obtain a plurality of road sign classes with at least one same attribute as the sign, and classifying the sign across the plurality of frames in response to a confidence level exceeding a threshold.

Abstract: A computer-implement method includes receiving one or more images from one or more sensors, outputting the one or more images at a display, outputting an automatic segmentation line of the one or more portions of the image in response to an object identified in the one or more images, and in response to one or more inputs received at the system associated with a correction, outputting a correction line on the display associated with the object, wherein the correction line automatically adjust one or more vertices associated with an incorrect portion of the automatic segmentation, wherein the one or more vertices are adjusted in response to the one or more inputs.

Abstract: Systems and method for machine-learning assisted road sign content prediction and machine learning training is disclosed. A sign detector model processes images or video with road signs. A visual attribute prediction model extracts visual attributes of the sign in the image. The visual attribute prediction model can communicate with a knowledge graph reasoner to validate the visual attribute prediction model by applying various rules to the output of the visual attribute prediction model. A plurality of potential sign candidates are retrieved that match the visual attributes of the image subject to the visual attribute prediction model, and the rules help to reduce the list of potential sign candidates and improve accuracy of the model.

Abstract: Systems and methods for constructing and managing a unique road sign knowledge graph across various countries and regions is disclosed. The system utilizes machine learning methods to assist humans when comparing a new sign template with a plurality of stored sign templates to reduce or eliminate redundancy in the road sign knowledge graph. Such a machine learning method and system is also used in providing visual attributes of road signs such as sign shapes, colors, symbols, and the like. If the machine learning determines that the input road sign template is not found in the road sign knowledge graph, the input sign template can be added to the road sign knowledge graph. The road sign knowledge graph can be maintained to add signs templates that are not already in the knowledge graph but are found in real-world by integrating human annotator's feedback during ground truth generation for machine learning.

Abstract: Semi-crowdsourced expert-in-the-loop information capture is provided. Solution search criteria for a repair problem are obtained from one or more of expert user, machine algorithm, or crowd worker. A search is conducted according to the solution search criteria to identify search results. The search results are filtered according to relevance and likelihood of containing confirmed solutions using a confirmed-solution classifier. The search results are provided to crowd workers for analysis to find and extract confirmed solutions. If the solutions do not exist in a knowledge base, the extracted confirmed solutions are provided for expert review and adding the extracted confirmed solutions, as reviewed, to the knowledge base. Otherwise if the solutions already exist in the knowledge base, the extracted confirmed solutions are used to retrain the confirmed-solution classifier.

Abstract: A method of road sign classification utilizing a knowledge graph, including detecting and selecting a representation of a sign across a plurality of frames, outputting a prompt initiating a request for a classification associated with the representation of the sign, classifying one or more images including the sign, querying the knowledge graph to obtain a plurality of road sign classes with at least one same attribute as the sign, and classifying the sign across the plurality of frames in response to a confidence level exceeding a threshold.