Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.

Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.

Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.

Abstract: A computer-implemented image processing method includes accessing image data representing a plurality of images of a plurality of vehicles and reading a data model representing a predefined vehicle attribute. The method further includes processing a portion of the image data to detect a vehicle feature of at least one of the vehicles represented in at least one of the images based on the predefined vehicle attribute, and processing the portion of the image data to generate vehicle data representing the detected vehicle feature.

Abstract: An architecture to enable delay tolerant content-centric and host-centric communications in MANETs comprised of smart phones is disclosed. This architecture allows cross network communications and Internet sharing, assuming that at least one of the smart phones has internet connectivity. The architecture includes a routing mechanism for content-centric communications, as well as routing algorithms for host-centric communications. In one embodiment, a combination of OLSR and a modified variant of PRoPHET is used to create preferred routing paths for content-centric communication. In another embodiment, variations of store and forward, such as binary spray and wait, are used for host-centric communications. These communications between mobile devices utilize wireless communications, such as IEEE802.11.

Abstract: An architecture to enable delay tolerant content-centric and host-centric communications in MANETs comprised of smart phones is disclosed. This architecture allows cross network communications and Internet sharing, assuming that at least one of the smart phones has internet connectivity. The architecture includes a routing mechanism for content-centric communications, as well as routing algorithms for host-centric communications. In one embodiment, a combination of OLSR and a modified variant of PRoPHET is used to create preferred routing paths for content-centric communication. In another embodiment, variations of store and forward, such as binary spray and wait, are used for host-centric communications. These communications between mobile devices utilize wireless communications, such as IEEE802.11.