The present method incorporates audio and visual cues from human gesticulation for automatic recognition. The methodology articulates a framework for co-analyzing gestures and prosodic elements of a person's speech. The methodology can be applied to a wide range of algorithms involving analysis of gesticulating individuals. The examples of interactive technology applications can range from information kiosks to personal computers. The video analysis of human activity provides a basis for the development of automated surveillance technologies in public places such as airports, shopping malls, and sporting events.
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is based on and claims priority to U.S. Provisional Application No. 60/413,998, filed Sep. 19, 2002, which is fully incorporated herein by reference.
A method and apparatus are provided for learning a model for the appearance of an object while tracking the position of the object in three dimensions. Under embodiments of the present invention, this is achieved by combining a particle filtering technique for tracking the object's position with an expectation-maximization technique for learning the appearance of the object. Two stereo cameras are used to generate data for the learning and tracking.
A document (or multiple documents) is analyzed to identify entities of interest within that document. This is accomplished by constructing n-gram or bi-gram models that correspond to different kinds of text entities, such as chemistry-related words and generic English words. The models can be constructed from training text selected to reflect a particular kind of text entity. The document is tokenized, and the tokens are run against the models to determine, for each token, which kind of text entity is most likely to be associated with that token. The entities of interest in the document can then be annotated accordingly.
