Abstract: An example method is performed at a device with a display and a biometric sensor. While the device is in a locked state, the method includes displaying a log-in user interface that is associated with logging in to a first and second user account. While displaying the log-in user interface, the method includes, receiving biometric information, and in response to receiving the biometric information: when the biometric information is consistent with biometric information for the first user account and the first user account does not have an active session, displaying a prompt to input a log-in credential for the first user account; and when the biometric information is consistent with biometric information for the second user account and the second user account does not have an active session on the device, displaying a prompt to input a log-in credential for the second user account.

Abstract: An example method is performed at a device with a display and a biometric sensor. While the device is in a locked state, the method includes displaying a log-in user interface that is associated with logging in to a first and second user account. While displaying the log-in user interface, the method includes, receiving biometric information, and in response to receiving the biometric information: when the biometric information is consistent with biometric information for the first user account and the first user account does not have an active session, displaying a prompt to input a log-in credential for the first user account; and when the biometric information is consistent with biometric information for the second user account and the second user account does not have an active session on the device, displaying a prompt to input a log-in credential for the second user account.

Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes.

Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes.

Abstract: A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes.

Abstract: A system, method and article of manufacture are provided for executing a setup protocol. A camera and a visual display device are coupled to a computer. Images are generated by the camera upon activation of the camera. A series of setup tests are then conducted on the images generated by the camera to determine whether the camera and surrounding environmental elements satisfy predetermined criteria of an intended computer vision application for optimal running of the intended computer vision application on the computer. The series of setup tests comprises at least one setup test selected from a library of setup tests.

Abstract: A system, method and article of manufacture are provided for tracking a head portion of a person image in video images. Upon receiving video images, a first head tracking operation is executed for generating a first confidence value. Such first confidence value is representative of a confidence that a head portion of a person image in the video images is correctly located. Also executed is a second head tracking operation for generating a second confidence value representative of a confidence that the head portion of the person image in the video images is correctly located. The first confidence value and the second confidence value are then outputted. Subsequently, the depiction of the head portion of the person image in the video images is based on the first confidence value and the second confidence value.

Abstract: The invention identifies a blank segment (i.e., a segment that does not correspond to recorded visual content) in a set of visual recording data. The invention identifies a blank segment using a blank frame detector that is adapted to evaluate a frame of visual recording data to determine whether the frame of visual recording data is a blank frame, and a blank segment detector that is adapted to evaluate a characteristic of a group of frames of visual recording data to determine whether the group of frames of visual recording data is a blank segment comprising a plurality of blank frames. Identification of a blank segment can be used to, for example, decide whether to terminate digitization of a set of analog visual recording data.

Abstract: The invention identifies a blank segment (i.e., a segment that does not correspond to recorded visual content) in a set of visual recording data. The invention identifies a blank segment using a blank frame detector that is adapted to evaluate a frame of visual recording data to determine whether the frame of visual recording data is a blank frame, and a blank segment detector that is adapted to evaluate a characteristic of a group of frames of visual recording data to determine whether the group of frames of visual recording data is a blank segment comprising a plurality of blank frames. Identification of a blank segment can be used to, for example, decide whether to terminate digitization of a set of analog visual recording data.

Abstract: A visual recording can be automatically edited in a manner that creates a summary of the visual recording in which only content that is of unacceptably low quality and/or very little or no interest is eliminated, thereby enabling the original unsummarized visual recording to be discarded if desired. Additionally, a summary of a visual recording can be automatically produced in accordance with a format template that imposes a certain overall style to the visual recording summary. Further, a viewer of a visual recording can be enabled to provide input which can affect the visual recording content included in an automatically generated summary of the visual recording.

Abstract: A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes.

Abstract: A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Abstract: A background subtraction apparatus of the present invention includes a key point locator for locating key points on a known object type, a boundary point locator for locating boundary points of the known object that make up the edges of the known object, and an edge processor for processing the edges to provide a clean-edged extraction of the known object from a background image. Preferably, the key point locator includes an alignment detector for detecting alignment of an image of the known object type with a skeleton image. Still more preferably, the skeleton image is an exoskeleton image and the known object type is a human being.

Abstract: A background subtraction apparatus of the present invention includes a key point locator for locating key points on a known object type, a boundary point locator for locating boundary points of the known object that make up the edges of the known object, and an edge processor for processing the edges to provide a clean-edged extraction of the known object from a background image. Preferably, the key point locator includes an alignment detector for detecting alignment of an image of the known object type with a skeleton image. Still more preferably, the skeleton image is an exoskeleton image and the known object type is a human being.