Abstract: There is presented a system and method for providing real-time object recognition to a remote user. The system comprises a portable communication device including a camera, at least one client-server host device remote from and accessible by the portable communication device over a network, and a recognition database accessible by the client-server host device or devices. A recognition application residing on the client-server host device or devices is capable of utilizing the recognition database to provide real-time object recognition of visual imagery captured using the portable communication device to the remote user of the portable communication device. In one embodiment, a sighted assistant shares an augmented reality panorama with a visually impaired user of the portable communication device where the panorama is constructed from sensor data from the device.

Abstract: Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.

Abstract: Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.

Abstract: Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.

Abstract: Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.

Abstract: Systems and methods of quickly recognizing or differentiating many objects are presented. Contemplated systems include an object model database storing recognition models associated with known modeled objects. The object identifiers can be indexed in the object model database based on recognition features derived from key frames of the modeled object. Such objects are recognized by a recognition engine at a later time. The recognition engine can construct a recognition strategy based on a current context where the recognition strategy includes rules for executing one or more recognition algorithms on a digital representation of a scene. The recognition engine can recognize an object from the object model database, and then attempt to identify key frame bundles that are contextually relevant, which can then be used to track the object or to query a content database for content information.

Abstract: There is presented a system and method for providing real-time object recognition to a remote user. The system comprises a portable communication device including a camera, at least one client-server host device remote from and accessible by the portable communication device over a network, and a recognition database accessible by the client-server host device or devices. A recognition application residing on the client-server host device or devices is capable of utilizing the recognition database to provide real-time object recognition of visual imagery captured using the portable communication device to the remote user of the portable communication device. In one embodiment, a sighted assistant shares an augmented reality panorama with a visually impaired user of the portable communication device where the panorama is constructed from sensor data from the device.

Abstract: There is presented a system and method for providing real-time object recognition to a remote user. The system comprises a portable communication device including a camera, at least one client-server host device remote from and accessible by the portable communication device over a network, and a recognition database accessible by the client-server host device or devices. A recognition application residing on the client-server host device or devices is capable of utilizing the recognition database to provide real-time object recognition of visual imagery captured using the portable communication device to the remote user of the portable communication device. In one embodiment, a sighted assistant shares an augmented reality panorama with a visually impaired user of the portable communication device where the panorama is constructed from sensor data from the device.

Abstract: There is presented a system and method for providing real-time object recognition to a remote user. The system comprises a portable communication device including a camera, at least one client-server host device remote from and accessible by the portable communication device over a network, and a recognition database accessible by the client-server host device or devices. A recognition application residing on the client-server host device or devices is capable of utilizing the recognition database to provide real-time object recognition of visual imagery captured using the portable communication device to the remote user of the portable communication device. In one embodiment, a sighted assistant shares an augmented reality panorama with a visually impaired user of the portable communication device where the panorama is constructed from sensor data from the device.

Abstract: The present invention is directed to a method and related system for determining a feature location in multiple dimensions including depth. The method includes providing left and right camera images of the feature and locating the feature in the left camera image and in the right camera image using bunch graph matching. The feature location is determined in multiple dimensions including depth based on the feature locations in the left camera image and the right camera image.

Abstract: The rich media communication system of the present invention provides a user with a three-dimensional communication space or theater having rich media functions. The user may be represented in the theater as a segmented video image or as an avatar. The user is also able to communicate by presenting images, videos, audio files, or text within the theater. The system may include tools for allowing lowered cost of animation, improved collaboration between users, presentation of episodic content, web casts, newscasts, infotainment, advertising, music clips, video conferencing, customer support, distance learning, advertising, social spaces, and interactive game shows and content.

Abstract: The rich media communication system of the present invention provides a user with a three-dimensional communication space or theater having rich media functions. The user may be represented in the theater as a segmented video image or as an avatar. The user is also able to communicate by presenting images, videos, audio files, or text within the theater. The system may include tools for allowing lowered cost of animation, improved collaboration between users, presentation of episodic content, web casts, newscasts, infotainment, advertising, music clips, video conferencing, customer support, distance learning, advertising, social spaces, and interactive game shows and content.

Abstract: The present invention is directed to a method and related system for determining a feature location in multiple dimensions including depth. The method includes providing left and right camera images of the feature and locating the feature in the left camera image and in the right camera image using bunch graph matching. The feature location is determined in multiple dimensions including depth based on the feature locations in the left camera image and the right camera image.