Patents by Inventor Xiao Bian

Xiao Bian 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: 9870519
    Abstract: A system, method and computer program product for hierarchical sparse dictionary learning (“HiSDL”) to construct a learned dictionary regularized by an a priori over-complete dictionary, includes providing at least one a priori over-complete dictionary for regularization, performing sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary, using a processor, updating the sparse coded dictionary with regularization using at least one auxiliary variable to provide a learned dictionary, determining whether the learned dictionary converges to an input data set, and outputting the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set. The system and method includes, when the learned dictionary lacks convergence, repeating the steps of performing sparse coding, updating the sparse coded dictionary, and determining whether the learned dictionary converges to the input data set.
    Type: Grant
    Filed: July 8, 2015
    Date of Patent: January 16, 2018
    Assignee: NEC Corporation
    Inventors: Xia Ning, Guofei Jiang, Xiao Bian
  • Patent number: 9785919
    Abstract: Systems and methods for automatically identifying and classifying distress of an aircraft component are provided. In one embodiment, a method includes accessing one or more digital images captured of the aircraft component and providing the one or more digital images as an input to a multi-layer network image classification model. The method further includes generating a classification output for the one or more images from the multi-layer network image classification model and automatically classifying the distress of the aircraft component based at least in part on the classification output.
    Type: Grant
    Filed: December 10, 2015
    Date of Patent: October 10, 2017
    Assignee: General Electric Company
    Inventors: David Scott Diwinsky, Ser Nam Lim, Xiao Bian
  • Publication number: 20170169400
    Abstract: Systems and methods for automatically identifying and classifying distress of an aircraft component are provided. In one embodiment, a method includes accessing one or more digital images captured of the aircraft component and providing the one or more digital images as an input to a multi-layer network image classification model. The method further includes generating a classification output for the one or more images from the multi-layer network image classification model and automatically classifying the distress of the aircraft component based at least in part on the classification output.
    Type: Application
    Filed: December 10, 2015
    Publication date: June 15, 2017
    Inventors: David Scott Diwinsky, Ser Nam Lim, Xiao Bian
  • Patent number: 9418318
    Abstract: A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L?1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L?1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.
    Type: Grant
    Filed: August 26, 2014
    Date of Patent: August 16, 2016
    Assignees: Siemens Aktiengesellschaft, North Carolina State University
    Inventors: Mariappan S. Nadar, Xiao Bian, Qiu Wang, Hasan Ertan Cetingul, Hamid Krim, Lucas Plaetevoet
  • Publication number: 20160012334
    Abstract: A system, method and computer program product for hierarchical sparse dictionary learning (“HiSDL”) to construct a learned dictionary regularized by an a priori over-complete dictionary, includes providing at least one a priori over-complete dictionary for regularization, performing sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary, using a processor, updating the sparse coded dictionary with regularization using at least one auxiliary variable to provide a learned dictionary, determining whether the learned dictionary converges to an input data set, and outputting the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set. The system and method includes, when the learned dictionary lacks convergence, repeating the steps of performing sparse coding, updating the sparse coded dictionary, and determining whether the learned dictionary converges to the input data set.
    Type: Application
    Filed: July 8, 2015
    Publication date: January 14, 2016
    Inventors: Xia Ning, Guofei Jiang, Xiao Bian
  • Publication number: 20150063687
    Abstract: A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L?1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L?1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.
    Type: Application
    Filed: August 26, 2014
    Publication date: March 5, 2015
    Inventors: Mariappan S. Nadar, Xiao Bian, Qiu Wang, Hasan Ertan Cetingul, Hamid Krim, Lucas Plaetevoet