Abstract: A method and program product includes assigning a first location of at least one embedded device. The embedded device includes at least one sensor and is associated with a one joint capable of movement. The first location is captured by the sensor. A second location of the at least one embedded device is assigned. The second location being captured by observation of the embedded device by at least one external sensor. A pose correspondence between the first location and the second location is established using a model of the joint. The sensor is calibrated by tracking a change in a pose captured by the external sensor and a change in a pose captured by the one sensor as the joint moves.

Abstract: A method and program product includes assigning a first location of at least one embedded device. The embedded device includes at least one sensor and is associated with a one joint capable of movement. The first location is captured by the sensor. A second location of the at least one embedded device is assigned. The second location being captured by observation of the embedded device by at least one external sensor. A pose correspondence between the first location and the second location is established using a model of the joint. The sensor is calibrated by tracking a change in a pose captured by the external sensor and a change in a pose captured by the one sensor as the joint moves.

Abstract: An array of microphones placed on a mobile robot provides multiple channels of audio signals. A received set of audio signals is called an audio segment, which is divided into multiple frames. A phase analysis is performed on a frame of the signals from each pair of microphones. If both microphones are in an active state during the frame, a candidate angle is generated for each such pair of microphones. The result is a list of candidate angles for the frame. This list is processed to select a final candidate angle for the frame. The list of candidate angles is tracked over time to assist in the process of selecting the final candidate angle for an audio segment.

Abstract: An array of microphones placed on a mobile robot provides multiple channels of audio signals. A received set of audio signals is called an audio segment, which is divided into multiple frames. A phase analysis is performed on a frame of the signals from each pair of microphones. If both microphones are in an active state during the frame, a candidate angle is generated for each such pair of microphones. The result is a list of candidate angles for the frame. This list is processed to select a final candidate angle for the frame. The list of candidate angles is tracked over time to assist in the process of selecting the final candidate angle for an audio segment.

Abstract: An array of microphones placed on a mobile robot provides multiple channels of audio signals. A received set of audio signals is called an audio segment, which is divided into multiple frames. A phase analysis is performed on a frame of the signals from each pair of microphones. If both microphones are in an active state during the frame, a candidate angle is generated for each such pair of microphones. The result is a list of candidate angles for the frame. This list is processed to select a final candidate angle for the frame. The list of candidate angles is tracked over time to assist in the process of selecting the final candidate angle for an audio segment.

Abstract: Tracking and following technique embodiments are presented that are generally employed to track and follow a person using a mobile robotic device having a color video camera and a depth video camera. A computer associated with the mobile robotic device is used to perform various actions. Namely, in a tracking mode, a face detection method and the output from the color video camera is used to detect potential persons in an environment. In addition, a motion detection method and the output from the depth video camera is also used to detect potential persons in the environment. Detection results obtained using the face and motion detection methods are then fused and used to determine the location of one or more persons in the environment. Then, in a following mode, a mobile robotic device following method is used to follow a person whose location was determined in the tracking mode.

Abstract: Face recognition training database generation technique embodiments are presented that generally involve collecting characterizations of a person's face that are captured over time and as the person moves through an environment, to create a training database of facial characterizations for that person. As the facial characterizations are captured over time, they are will represent the person's face as viewed from various angles and distances, different resolutions, and under different environmental conditions (e.g., lighting and haze conditions). Further, over a long period of time where facial characterizations of a person are collected periodically, these characterizations can represent an evolution in the appearance of the person. This produces a rich training resource for use in face recognition systems. In addition, since a person's face recognition training database can be established before it is needed by a face recognition system, once employed, the training will be quicker.

Abstract: Face recognition training database generation technique embodiments are presented that generally involve collecting characterizations of a person's face that are captured over time and as the person moves through an environment, to create a training database of facial characterizations for that person. As the facial characterizations are captured over time, they are will represent the person's face as viewed from various angles and distances, different resolutions, and under different environmental conditions (e.g., lighting and haze conditions). Further, over a long period of time where facial characterizations of a person are collected periodically, these characterizations can represent an evolution in the appearance of the person. This produces a rich training resource for use in face recognition systems. In addition, since a person's face recognition training database can be established before it is needed by a face recognition system, once employed, the training will be quicker.

Abstract: Face recognition training database generation technique embodiments are presented that generally involve collecting characterizations of a person's face that are captured over time and as the person moves through an environment, to create a training database of facial characterizations for that person. As the facial characterizations are captured over time, they are will represent the person's face as viewed from various angles and distances, different resolutions, and under different environmental conditions (e.g., lighting and haze conditions). Further, over a long period of time where facial characterizations of a person are collected periodically, these characterizations can represent an evolution in the appearance of the person. This produces a rich training resource for use in face recognition systems. In addition, since a person's face recognition training database can be established before it is needed by a face recognition system, once employed, the training will be quicker.

Abstract: Described is detecting touch on a compound curve surface that displays content for touch-based interaction. Touch may be detected by processing an infrared image to detect a shadow corresponding to the touch, and/or by detecting infrared reflection corresponding to the touch. Also described is providing a curved surface with capacitive sensing. Also described is the use of frustrated total internal reflection to detect touch.

Abstract: Tracking and following technique embodiments are presented that are generally employed to track and follow a person using a mobile robotic device having a color video camera and a depth video camera. A computer associated with the mobile robotic device is used to perform various actions. Namely, in a tracking mode, a face detection method and the output from the color video camera is used to detect potential persons in an environment. In addition, a motion detection method and the output from the depth video camera is also used to detect potential persons in the environment. Detection results obtained using the face and motion detection methods are then fused and used to determine the location of one or more persons in the environment. Then, in a following mode, a mobile robotic device following method is used to follow a person whose location was determined in the tracking mode.

Abstract: Face recognition training database generation technique embodiments are presented that generally involve collecting characterizations of a person's face that are captured over time and as the person moves through an environment, to create a training database of facial characterizations for that person. As the facial characterizations are captured over time, they are will represent the person's face as viewed from various angles and distances, different resolutions, and under different environmental conditions (e.g., lighting and haze conditions). Further, over a long period of time where facial characterizations of a person are collected periodically, these characterizations can represent an evolution in the appearance of the person. This produces a rich training resource for use in face recognition systems. In addition, since a person's face recognition training database can be established before it is needed by a face recognition system, once employed, the training will be quicker.

Abstract: An array of microphones placed on a mobile robot provides multiple channels of audio signals. A received set of audio signals is called an audio segment, which is divided into multiple frames. A phase analysis is performed on a frame of the signals from each pair of microphones. If both microphones are in an active state during the frame, a candidate angle is generated for each such pair of microphones. The result is a list of candidate angles for the frame. This list is processed to select a final candidate angle for the frame. The list of candidate angles is tracked over time to assist in the process of selecting the final candidate angle for an audio segment.

Abstract: A robot tracks objects using sensory data, and follows an object selected by a user. The object can be designated by a user from a set of objects recognized by the robot. The relative positions and orientations of the robot and object are determined. The position and orientation of the robot can be used so as to maintain a desired relationship between the object and the robot. Using the navigation system of the robot, during its movement, obstacles can be avoided. If the robot loses contact with the object being tracked, the robot can continue to navigate and search the environment until the object is reacquired.