Abstract: There is provided a system and method for automated sequence determination using pattern-directed aligned pattern clustering. The method includes: determining a set of seed patterns; generating an address table mapping the determined sets of seed patterns to occurrences of sequences of seed patterns in the protein nucleotide sequence; determining a breakpoint gap between two respective seed patterns in an occurrence of one of the sequences in the address table; for each sequence of seed patterns in the address table, where there is a breakpoint gap, merging the respective seed patterns in the sequence surrounding the breakpoint gap with the breakpoint gap into an extended seed pattern, otherwise merging the respective seed patterns in the sequence into an extended seed pattern; and determining rare mutant patterns from the extended seed patterns by comparing extended seed patterns.

Abstract: The present invention relates to a method of intelligent 2D and 3D object and scene modeling, transformation and manipulation and more particularly this invention relates to the field of computer modeling, virtual reality, animation and 3D Web streaming. The method uses attributed hypergraph representations (AHR) for modeling, transforming and manipulating objects. From one or more 2D views of a 3D object or scene, range information is first computed and then a triangular mesh model is constructed. The data structure is designed to handle the transformations on the representation corresponding to movements and deformations of the object. In an attributed hypergraph, the attributes associated with the hyperedges and the vertices facilitates modeling of various shapes with geometrical, physical or behavior features.

Abstract: The present invention relates to a method of intelligent 2D and 3D object and scene modeling, transformation and manipulation and more particularly this invention relates to the field of computer modeling, virtual reality, animation and 3D Web streaming. The method uses attributed hypergraph representations (AHR) for modeling, transforming and manipulating objects. From one or more 2D views of a 3D object or scene, range information is first computed and then a triangular mesh model is constructed. The data structure is designed to handle the transformations on the representation corresponding to movements and deformations of the object. In an attributed hypergraph, the attributes associated with the hyperedges and the vertices facilitates modeling of various shapes with geometrical, physical or behavior features.

Abstract: A 3D vision system utilizes a CCD camera to first generate a two dimensional image of the object. A computer analyzes the two dimensional image to determine areas of interest, such as high contrast areas which indicate sharp discontinuities. A laser range scanner, controlled by the computer, then scans the object at a variable resolution, increasing its resolution in the areas of interest to generate a computer model.

Abstract: Automatic discovery of qualitative and quantitative patterns inherent in data sets is accomplished by use of a unified framework which employs adjusted residual analysis in statistics to test the significance of the pattern candidates generated from data sets. This framework consists of a search engine for different order patterns, a mechanism to avoid exhaustive search by eliminating impossible pattern candidates, an attributed hypergraph (AHG) based knowledge representation language and an inference engine which measures the weight of evidence of each pattern for classification and prediction. If a pattern candidate passes the statistical significance test of adjusted residual, it is regarded as a pattern and represented by an attributed hyperedge in AHG. In the task of classification and/or prediction, the weights of evidence are calculated and compared to draw the conclusion.