Patents by Inventor Jochen Maydt

Jochen Maydt 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).

  • Fast method for training and evaluating support vector machines with a large set of linear features
    Patent number: 7174040
    Abstract: A procedure for fast training and evaluation of support vector machines (SVMs) with linear input features of high dimensionality is presented. The linear input features are derived from raw input data by means of a set of m linear functions defined on the k-dimensional raw input data. Training uses a one-time precomputation on the linear transform matrix in order to allow training on an equivalent training set with vector size k instead of m, given a great computational benefit in case of m>>k. A similar precomputation is used during evaluation of SVMs, so that the raw input data vector can be used instead of the derived linear feature vector.
    Type: Grant
    Filed: July 19, 2002
    Date of Patent: February 6, 2007
    Assignee: Intel Corporation
    Inventors: Rainer W. Lienhart, Jochen Maydt
  • Facial classification of static images using support vector machines
    Patent number: 7146050
    Abstract: A procedure for fast training and evaluation image classification systems using support vector machines (SVMs) with linear input features of high dimensionality is presented. The linear input features are derived from raw image data by means of a set of m linear functions defined on the k-dimensional raw input data, and are used for image classification, including facial recognition tasks.
    Type: Grant
    Filed: July 19, 2002
    Date of Patent: December 5, 2006
    Assignee: Intel Corporation
    Inventors: Rainer W. Lienhart, Jochen Maydt
  • Facial classification of static images using support vector machines
    Publication number: 20040013303
    Abstract: A procedure for fast training and evaluation image classification systems using support vector machines (SVMs) with linear input features of high dimensionality is presented. The linear input features are derived from raw image data by means of a set of m linear functions defined on the k-dimensional raw input data, and are used for image classification, including facial recognition tasks.
    Type: Application
    Filed: July 19, 2002
    Publication date: January 22, 2004
    Inventors: Rainer W. Lienhart, Jochen Maydt
  • Fast method for training and evaluating support vector machines with a large set of linear features
    Publication number: 20040015462
    Abstract: A procedure for fast training and evaluation of support vector machines (SVMs) with linear input features of high dimensionality is presented. The linear input features are derived from raw input data by means of a set of m linear functions defined on the k-dimensional raw input data. Training uses a one-time precomputation on the linear transform matrix in order to allow training on an equivalent training set with vector size k instead of m, given a great computational benefit in case of m>>k. A similar pre-computation is used during evaluation of SVMs, so that the raw input data vector can be used instead of the derived linear feature vector.
    Type: Application
    Filed: July 19, 2002
    Publication date: January 22, 2004
    Inventors: Rainer W. Lienhart, Jochen Maydt