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Michael C GallGardenville, PA

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Doylestown, PA

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Michael Gall Photo 50

Prevention Educator At Safe Connections

Position:
Prevention Educator at Safe Connections
Location:
Saint Louis, Missouri
Industry:
Civic & Social Organization
Work:
Safe Connections since Jul 2012
Prevention Educator Meds & Food for Kids Aug 2010 - Jan 2012
Communications Specialist Washington University in St. Louis - St. Louis Aug 2011 - Dec 2011
Center for Mental Health Services Research RA Other Places Publishing Dec 2009 - Aug 2010
Contributing Writer Peace Corps - Federated States of Micronesia Aug 2007 - Nov 2009
ESL Instructor / Community Organizer Mercy Franciscan at St. John - Cincinnati Area Aug 2008 - May 2009
Practicum Student
Education:
Washington University in St. Louis 2010 - 2011
Master of Social Work (MSW) Xavier University 2003 - 2007
Bachelor, Social Work
Michael Gall Photo 51

Michael Gall

Location:
United States
Michael Gall Photo 52

Michael Gall

Location:
United States
Michael Gall Photo 53

Michael Gall

Location:
United States

Publications & IP owners

Us Patents

Predicate Logic Based Image Grammars For Complex Visual Pattern Recognition

US Patent:
8548231, Oct 1, 2013
Filed:
Mar 16, 2010
Appl. No.:
12/724954
Inventors:
Vinay Damodar Shet - Princeton NJ, US
Maneesh Kumar Singh - Lawrenceville NJ, US
Claus Bahlmann - Princeton NJ, US
Visvanathan Ramesh - Plainsboro NJ, US
Stephen P. Masticola - Kingston NJ, US
Jan Neumann - Arlington VA, US
Toufiq Parag - Piscataway NJ, US
Michael A. Gall - Belle Mead NJ, US
Roberto Antonio Suarez - Jackson NJ, US
Assignee:
Siemens Corporation - Iselin NJ
International Classification:
G06K 9/62
G06F 17/00
US Classification:
382156, 706 56
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.

Possible Relatives

Dean J Gall  Dana E Gall  Aaron D Gall  Denise R Gall  Darren J Gall  Donna L Gall  Edwin L Gall  Michael A Last  Michael D Stanaland  Michael Stanberry 

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