James A. Shine

Details der Publikationsliste

Zeitraum

1998 - 2009

Anzahl

10

Co-Autoren

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1 Mixed-Drove Spatio-Temporal Co-occurrence Pattern Mining (2009)

Mete Celik, Student Member, Shashi Shekhar, James P. Rogers, James A. Shine

Abstract—Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity....

Related Work MDCOP Mining Problem Proposed MDCOP Mining Algorithms Evaluation Conclusion and Future Work (2009)

Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, Jin Soung Yoo

Detecting IEDs, suspicious vehicles Detecting patterns of enemy troop movement (manpack stinger, tank, and truck)

An Analysis of Toponymic Homonyms in Gazetteers: Country-Level Duplicate Names in the National Geospatial-Intelligence Agency’s Geographic Names Data Base (2008)

Caldwell, Douglas R., Shine, James A.

Place names are the most common way we identify geographic features. When place names are unambiguous, they can georeference features, locating them uniquely on the globe. The problem with place...

Mining at most top-k mixed-drove spatio-temporal co-occurrence patterns: A summary of results (2007)

Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, James M. Kang

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many...

Mining at most top-k mixed-drove spatio-temporal co-occurrence patterns: A summary of results (2007)

Shashi Shekhar, James P. Rogers, James A. Shine, James M. Kang

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many...

Mixed-drove spatio-temporal co-occurrence pattern mining: A summary of results (2006)

Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, Jin Soung Yoo

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many...

Mixed-drove spatio-temporal co-occurrence pattern mining: A summary of results (2006)

Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, Jin Soung Yoo

Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many...

Sustained emerging spatio-temporal co-occurrence pattern mining: A summary of results (2006)

Shashi Shekhar, James P. Rogers, James A. Shine

Sustained emerging spatio-temporal co-occurrence patterns (SECOPs) represent subsets of object-types that are increasingly located together in space and time. Discovering SECOPs is important due to...

Correlation on Noisy Images. (2002)

Shine, James A., Margerum, Eugene A.

A computer program can simulate star-shaped images and a correlation between two different images on 128 by 128 matrices. The Fourier transform, in a time-saving algorithm, is used to carry out the...

An Analysis of a Relaxation Scheme to Improve Terrain Elevation Data. (1998)

Crombie,Michael A., Shine,James A.

Elevation matrices derived from correlation of digital stereo images often contain errors resulting from a breakdown of the correlation process. One such matrix was corrected using a relaxation...