Lockheed Martin Corporation

Martin Marietta Corporation

Teranex, Inc.

Albert R Sanders

Barry A Kahn

James F Curtis

Kevin A Kreeger

Holger Findling

Patricia J Rowland

A prescreen filtering method and algorithm based upon Attributed Relational Graph (ARG) techniques are employed in a node-by-node matching to individually determine a best alignment of the sets of minutia of a pair of search and file fingerprints. First, the directional components of each and every different pair of minutia from the two sets are compared to ensure that they are within a given angular tolerance, which is selected to allow for a reasonable variation in possible rotational misalignments. After this directional alignment tolerance criteria has been passed, a Distance Matrix (DM) score is generated for each passed &lsqb;u,v&rsqb; minutia pair by performing a virtual alignment of the &lsqb;u,v&rsqb; minutia pair in location and direction, and then by comparing the distances and relative orientations between each of four pairs of minutia neighbors in each of the four local quadrants, which represents how well each different &lsqb;u,v&rsqb; minutia pair match each other. The size of a set of well matched minutia pairs, relative to the sizes of other sets of well matched minutia pairs for all other file fingerprints in the repository file, determines if a particular file fingerprint is passed along to the more complete and rigorous matching functions of an Automated Fingerprint Identification System (AFIS).

Fingerprint matching system with ARG-based prescreener

A progressive video frame is detected in a sequence of video fields, wherein the sequence of video fields includes a target video field. Detection includes generating one or more metrics by comparing the target video field with an immediately preceding and/or immediately succeeding video field. The metrics are compared with one or more threshold values. The immediately preceding and/or immediately succeeding video field is determined to have been derived from a same progressive video frame as the target video field if the one or more metrics are less than their respective threshold values. The metrics in this case may be indicative of a quantity of interlace artifacts. Alternatively, metrics indicative of an amount of represented motion may be derived by comparing a target video field with each of an immediately preceding and immediately succeeding video field. The relative values of the metrics with respect to one another is then used to indicate whether one or both of the immediately preceding and immediately succeeding video fields is a progressive match for the target video field.

Detection of progressive frames in a video field sequence

Methods and apparatus providing model-based feature tracking for location assessment of a sensor platform, or tracking of a feature are disclosed wherein a series of predictive models are developed to predict a location of features in subsequently acquired sensor data. Feature location data acquired by a sensor in a previous platform position assessment is used to build a model corresponding to anticipated feature locations in data acquired for a present platform position assessment.

Model-based feature tracking system

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Orlando, FL, United States of America

Albert R Sanders