Abstract:
Audio/video programming content is made available to a receiver from a content provider, and meta data is made available to the receiver from a meta data provider. The meta data corresponds to the programming content, and identifies, for each of multiple portions of the programming content, an indicator of a likelihood that the portion is an exciting portion of the content. In one implementation, the meta data includes probabilities that segments of a baseball program are exciting, and is generated by analyzing the audio data of the baseball program for both excited speech and baseball hits. The meta data can then be used to generate a summary for the baseball program.
Abstract:
A method of acquiring a set of images useable to 3D model a physical object includes imaging the physical object with a camera, and displaying with the camera a current view of the physical object as imaged by the camera from a current perspective. The method further includes displaying with the camera a visual cue overlaying the current view and indicating perspectives from which the physical object is to be imaged to acquire the set of images.
Abstract:
Systems and methods for detecting people or speakers in an automated fashion are disclosed. A pool of features including more than one type of input (like audio input and video input) may be identified and used with a learning algorithm to generate a classifier that identifies people or speakers. The resulting classifier may be evaluated to detect people or speakers.
Abstract:
Systems and methods for detecting people or speakers in an automated fashion are disclosed. A pool of features including more than one type of input (like audio input and video input) may be identified and used with a learning algorithm to generate a classifier that identifies people or speakers. The resulting classifier may be evaluated to detect people or speakers.
Abstract:
Discussion evaluation may be provided. First, an assignment page including an evaluation link may be displayed and a user initiated input corresponding to the evaluation link may be received. Next, an evaluation view may be displayed in response to the received user initiated input. The displayed evaluation view may comprise an evaluation assistant data section and a raw discussion data section. Evaluation data may then be received in response to the displayed evaluation view.
Abstract:
Estimation of available bandwidth on a network uses packet pairs and spatially filtering. Packet pairs are transmitted over the network. The dispersion of the packet pairs is used to generate samples of the available bandwidth, which are then classified into bins to generate a histogram. The bins can have uniform bin widths, and the histogram data can be aged so that older samples are given less weight in the estimation. The histogram data is then spatially filtered. Kernel density algorithms can be used to spatially filter the histogram data. The network available bandwidth is estimated using the spatially filtered histogram data. Alternatively, the spatially filtered histogram data can be temporally filtered before the available bandwidth is estimated.
Abstract:
Systems and methods for detecting people or speakers in an automated fashion are disclosed. A pool of features including more than one type of input (like audio input and video input) may be identified and used with a learning algorithm to generate a classifier that identifies people or speakers. The resulting classifier may be evaluated to detect people or speakers.
Abstract:
A method for orchestrating various applications is described herein. A request to store a context information regarding a document may be received. An application in which the document is modified may be determined. The context information may be requested from the application. The context information may be stored. A request to recall the context information may be received. The context information may be displayed on a computer screen.
Abstract:
A system and process for muting the audio transmission from a location of a participant engaged in a multi-party, computer network-based teleconference when that participant is working on a keyboard, is presented. The audio is muted as it is assumed the participant is doing something other than actively participation in the meeting when typing on the keyboard. If left un-muted the sound of typing would distract the other participant in the teleconference.
Abstract:
The concurrent multiple instance learning technique described encodes the inter-dependency between instances (e.g. regions in an image) in order to predict a label for a future instance, and, if desired the label for an image determined from the label of these instances. The technique, in one embodiment, uses a concurrent tensor to model the semantic linkage between instances in a set of images. Based on the concurrent tensor, rank-1 supersymmetric non-negative tensor factorization (SNTF) can be applied to estimate the probability of each instance being relevant to a target category. In one embodiment, the technique formulates the label prediction processes in a regularization framework, which avoids overfitting, and significantly improves a learning machine's generalization capability, similar to that in SVMs. The technique, in one embodiment, uses Reproducing Kernel Hilbert Space (RKHS) to extend predicted labels to the whole feature space based on the generalized representer theorem.