Abstract: Methods and systems for reconstructing an object from observations of interaction of the object with a physical system.

Abstract: Many tasks in computer vision involve assigning a label, such as disparity for depth of field data to every pixel. Energy minimization may be used to accomplish this labeling. The present invention provides an efficient way of minimizing energy functions in assigning labels to pixels. Two methods of using graph cuts to compute a local minimum are described. The first move is an &agr;&bgr; swap. For a pair of labels, &agr;, &bgr;, this move swaps the labels between an arbitrary set of pixels labeled a and another arbitrary set of pixels labeled &bgr;. The first method generates a labeling such that there is no swap move that decreases the energy. The second move is the &agr;-expansion. For a label &agr;, this move assigns an arbitrary set of pixels with the label &agr;.

Abstract: A joint histogram is used for content-based data object comparison. For a data object which can be described by a plurality of features and values, a set of features describing the data object is selected. A joint histogram is a k-dimensional vector, such that each entry in the joint histogram contains the number of data points in the data object that are described by a k-tuple of feature values. The data object is analyzed according to the set of features and the results entered into the joint histogram. The joint histogram distinguishes the data object from other data objects. Joint histogram comparisons may be used to identify the similarity between two or more particular data objects.