Abstract: Various technologies pertaining to dynamically identifying travel segments to be taken by a traveler traveling in a region are described herein, where observations about travel segments in the region are sparse and subject to alteration. A computer-implemented graph can be loaded into a memory, where the computer-implemented graph is representative of the region. The computer-implemented graph includes nodes that represent locations in the region and edges that represent travel segments of the region, where the edges have costs assigned thereto, and further where there is a defined statistical relationship between the costs. When an observation about a travel path is received, using the computer-implemented graph, inferences can be made about costs of traversing other travel paths in the region.

Abstract: Various technologies pertaining to dynamically identifying travel segments to be taken by a traveler traveling in a region are described herein, where observations about travel segments in the region are sparse and subject to alteration. A computer-implemented graph can be loaded into a memory, where the computer-implemented graph is representative of the region. The computer-implemented graph includes nodes that represent locations in the region and edges that represent travel segments of the region, where the edges have costs assigned thereto, and further where there is a defined statistical relationship between the costs. When an observation about a travel path is received, using the computer-implemented graph, inferences can be made about costs of traversing other travel paths in the region.

Abstract: Disclosed is a computer-assisted method for analyzing the surroundings of a display device. At least two different patterns are displayed successively on a display device connected to a computer. A camera is also connected to the computer and is directed towards objects in front of the display device. Each time a pattern is displayed on the display device, at least one image of the surroundings is captured. The captured images are combined to create a difference image. The difference image is analyzed to interpret the objects in the surroundings. For example, calculations may be performed to segment the foreground from the background of the surroundings or to estimate the range (distance) of the objects from the display device. An apparatus for performing this computer-assisted method is also disclosed.

Abstract: Disclosed is a computer-assisted method for analyzing the surroundings of a display device. At least two different patterns are displayed successively on a display device connected to a computer. A camera is also connected to the computer and is directed towards objects in front of the display device. Each time a pattern is displayed on the display device, at least one image of the surroundings is captured. The captured images are combined to create a difference image. The difference image is analyzed to interpret the objects in the surroundings. For example, calculations may be performed to segment the foreground from the background of the surroundings or to estimate the range (distance) of the objects from the display device. An apparatus for performing this computer-assisted method is also disclosed.

Abstract: A method for filling in missing attribute values in a data set array of cases and attribute positions includes the steps of, for a first case which includes an empty attribute position, determining a first set of K nearest neighbor cases which have known values for the corresponding attribute position as the empty attribute position in the first case; determining a filled in attribute value from the first set of K nearest neighbor cases for the empty attribute position of the first case; determining a second set of K nearest neighbor cases to the first case which have known values for the corresponding attribute position as the empty attribute position in the first case; and revising the filled in attribute value of the first case with an attribute value determined from the second set of K nearest neighbor cases. The revision process is typically repeated until the filled in values stop improving or until a preset limit on the number of iterations is reached.