Abstract: A work piece shape estimation device that includes an image information obtaining unit that obtains image information by sensing multiple randomly accumulated work pieces having an identical shape; an edge detection processor that performs an edge detection on the image information obtained by the image information obtaining processor; a separating processor that separates the work pieces into partial images based on the image information obtained by the edge detection processor; a work piece categorization processor that categorizes the separated partial images of the work pieces; and an estimated work piece shape generation processor that generates an estimated shape of the work piece by complementing an information of the partial images of the work pieces categorized by the work piece categorization processor.

Abstract: A system, method, and apparatus for mark recognition in an image of an original document are provided. The method/system takes as input an image of an original document in which at least one designated field is provided for accepting a mark applied by a user (which may or may not have been marked). A region of interest (RoI) is extracted from the image, roughly corresponding to the designated field. A center of gravity (CoG) of the RoI is determined, based on a distribution of black pixels in the RoI. Thereafter, for one or more iterations, the RoI is partitioned into sub-RoIs, based on the determined CoG, where at a subsequent iteration, sub-RoIs generated at the prior iteration serve as the RoI partitioned. Data is extracted from the RoI and sub-RoIs at one or more of the iterations, which allows a representation of the entire RoI to be generated which is useful in classifying the designated field, e.g., as positive (marked) or negative (not marked).

Abstract: An image processing system segments input image data into a plurality of regions, and obtains features of at least one region. The system refers to correspondence information to obtain a colorless pattern that is associated with the features of the at least one region of the input image data, and generates colorless image data using the obtained colorless pattern.

Abstract: First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grammars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.

Abstract: A system and method of mapping a persistent feature change of image data at a geographic location includes selecting a plurality of satellite images from a geographic location on different dates, producing a plurality of two-date satellite change images from pairs of the satellite images, comparing the plurality of satellite change images, and detecting a persistent feature change of image data in the compared satellite change images. The system may be implemented by selecting a plurality of satellite images from a database from different dates. A change detection module measures a change between pairs of the satellite images to produce a plurality of two-date satellite change images. A change confirmation module compares the two-date satellite change images and to confirm a persistent feature change.