Abstract: A computer-implemented method of refining a high definition (HD) map of a parking lot for autonomous vehicle parking (AVP) is disclosed. The method including: a vehicle navigating in the parking lot using the HD map; the vehicle using one or more sensors to scan for objects in the parking lot; the vehicle augmenting the HD map using a simultaneous localization & mapping SLAM method; adding objects detected by the one or more sensors to the HD map; and contributing towards improving a learning score of the HD map.

Abstract: A system for use in a vehicle, the system comprising one or more sensors, one or more processors operatively coupled to the one or more sensors, and a memory including instructions, which when executed by the one or more processors, cause the one or more processors to perform a method. The method comprising capturing a current point cloud with the one or more sensors, determining an estimated location and an estimated heading of the vehicle, selecting one or more point clouds based on the estimated location and heading of the vehicle, simplifying the current point cloud and the one or more point clouds, correlating the current point cloud to the one or more point clouds, and determining an updated estimate of the location of the vehicle based on correlation between the current point cloud and the one or more point clouds.

Abstract: The present disclosure relates to refining maps for vehicles, for example using feature extraction to identify parking areas and their contents from high resolution satellite or drone images. Such maps can be useful for autonomous valet parking (AVP) features of autonomous vehicles.

Abstract: The present disclosure relates to refining maps for vehicles, for example using feature extraction to identify parking areas and their contents from high resolution satellite or drone images. Such maps can be useful for autonomous valet parking (AVP) features of autonomous vehicles.

Abstract: A system for use in a vehicle, the system comprising one or more sensors, one or more processors operatively coupled to the one or more sensors, and a memory including instructions, which when executed by the one or more processors, cause the one or more processors to perform a method. The method comprising capturing a current point cloud with the one or more sensors, determining an estimated location and an estimated heading of the vehicle, selecting one or more point clouds based on the estimated location and heading of the vehicle, simplifying the current point cloud and the one or more point clouds, correlating the current point cloud to the one or more point clouds, and determining an updated estimate of the location of the vehicle based on correlation between the current point cloud and the one or more point clouds.

Abstract: A method of aiding the diagnosis of otitis media in a patient includes obtaining image data in a processor apparatus of a computing device, the image data being associated with at least one electronic image of a tympanic membrane of the patient, calculating, a plurality of image features, each image feature being calculated based on at least a portion of the image data, classifying the at least one electronic image as a particular type of otitis media using the plurality of image features, and outputting an indication of the particular type of otitis media. Also, a system for implementing such a method that includes an output device and a computing device.

Abstract: A scene is segmented into objects based on light field data for the scene, including based on image pixel values (e.g., intensity) and disparity map(s). In one aspect, the light field data is used to estimate one or more disparity maps for the scene taken from different viewpoints. The scene is then segmented into a plurality of regions that correspond to objects in the scene. Unlike other approaches, the regions can be variable-depth. In one approach, the regions are defined by boundaries. The boundaries are determined by varying the boundary to optimize an objective function for the region defined by the boundary. The objective function is based in part on a similarity function that measures a similarity of image pixel values for pixels within the boundary and also measures a similarity of disparities for pixels within the boundary.

Abstract: A scene is segmented into objects based on light field data for the scene, including based on image pixel values (e.g., intensity) and disparity map(s). In one aspect, the light field data is used to estimate one or more disparity maps for the scene taken from different viewpoints. The scene is then segmented into a plurality of regions that correspond to objects in the scene. Unlike other approaches, the regions can be variable-depth. In one approach, the regions are defined by boundaries. The boundaries are determined by varying the boundary to optimize an objective function for the region defined by the boundary. The objective function is based in part on a similarity function that measures a similarity of image pixel values for pixels within the boundary and also measures a similarity of disparities for pixels within the boundary.

Abstract: A method of aiding the diagnosis of otitis media in a patient includes obtaining image data in a processor apparatus of a computing device, the image data being associated with at least one electronic image of a tympanic membrane of the patient, calculating, a plurality of image features, each image feature being calculated based on at least a portion of the image data, classifying the at least one electronic image as a particular type of otitis media using the plurality of image features, and outputting an indication of the particular type of otitis media. Also, a system for implementing such a method that includes an output device and a computing device.

Abstract: A method of aiding the diagnosis of otitis media in a patient includes obtaining image data in a processor apparatus of a computing device, the image data being associated with at least one electronic image of a tympanic membrane of the patient, calculating a plurality of image features, each image feature being calculated based on at least a portion of the image data, classifying the at least one electronic image as a particular type of otitis media using the plurality of image features, and outputting an indication of the particular type of otitis media. Also, a system for implementing such a method that includes an output device and a computing device.

Abstract: A method of aiding the diagnosis of otitis media in a patient includes obtaining image data in a processor apparatus of a computing device, the image data being associated with at least one electronic image of a tympanic membrane of the patient, calculating a plurality of image features, each image feature being calculated based on at least a portion of the image data, classifying the at least one electronic image as a particular type of otitis media using the plurality of image features, and outputting an indication of the particular type of otitis media. Also, a system for implementing such a method that includes an output device and a computing device.