Patents by Inventor Toufiq Parag

Toufiq Parag has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 8548231
    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.
    Type: Grant
    Filed: March 16, 2010
    Date of Patent: October 1, 2013
    Assignee: Siemens Corporation
    Inventors: Vinay Damodar Shet, Maneesh Kumar Singh, Claus Bahlmann, Visvanathan Ramesh, Stephen P. Masticola, Jan Neumann, Toufiq Parag, Michael A. Gall, Roberto Antonio Suarez
  • Patent number: 7840061
    Abstract: A method adapts a boosted classifier to new samples. A boosted classifier is trained using initial samples. The boosted classifier is a combination of weak classifiers. Each weak classifier of the boosted classifier is updated adaptively by adding contributions of new samples and deleting contributions old samples.
    Type: Grant
    Filed: February 28, 2007
    Date of Patent: November 23, 2010
    Assignee: Mitsubishi Electric Research Laboratories, Inc.
    Inventors: Fatih M. Porikli, Toufiq Parag
  • Publication number: 20100278420
    Abstract: First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grmmars, 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.
    Type: Application
    Filed: March 16, 2010
    Publication date: November 4, 2010
    Applicant: Siemens Corporation
    Inventors: Vinay Damodar Shet, Maneesh Kumar Singh, Claus Bahlmann, Visvanathan Ramesh, Stephen P. Masticola, Jan Neumann, Toufiq Parag, Michael A. Gall, Roberto Antonio Suarez
  • Publication number: 20080205750
    Abstract: A method adapts a boosted classifier to new samples. A boosted classifier is trained using initial samples. The boosted classifier is a combination of weak classifiers. Each weak classifier of the boosted classifier is updated adaptively by adding contributions of new samples and deleting contributions old samples.
    Type: Application
    Filed: February 28, 2007
    Publication date: August 28, 2008
    Inventors: Fatih M. Porikli, Toufiq Parag