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    Item type:Publication, Access status: without files ,
    Representing directions for hough transforms
    (Springer, 2007)
    Wenzel, Fabian
    ;
    Grigat, Rolf-Rainer
    Many algorithms in computer vision operate with directions, i. e. with representations of 3D-points by ignoring their distance to the origin. Even though minimal parametrizations of directions may contain singularities, they can enhance convergence in optimization algorithms and are required e. g. for accumulator spaces in Hough transforms. There are numerous possibilities for parameterizing directions. However, many do not account for numerical stability when dealing with noisy data. This paper gives an overview of different parametrizations and shows their sensitivity with respect to noise. In addition to standard approaches in the field of computer vision, representations originating from the field of cartography are introduced. Experiments demonstrate their superior performance in computer vision applications in the presence of noise as they are suitable for Gaussian filtering.
    Publicationtype: Conference Paper
      33
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    Item type:Publication, Access status: with files ,
    Strategies for fast true motion block matching
    (SciTePress, 2006)
    Heijden, Hendrik van der
    ;
    Wenzel, Fabian
    ;
    Grigat, Rolf-Rainer
    Block matching is a widely used method for fast motion estimation. Although using a very simple motion model, which does not fit most real world video material, many motion compensating video compression algorithms use block matching because of its speed. Applications based on true motion vector estimates often use an optical flow algorithm because of their higher need for accuracy at the expense of increased computing time. This paper presents a modified block matching algorithm suitable for true motion applications. A modified full search will be used on a cost function consisting of SAD and a vector field smoothing term. Several strategies as search center prediction, spiral search, early search termination and multilevel successive elimination are implemented to keep the computational demand low. This way, high-quality estimates can be computed in real-time.
    Publicationtype: Conference Paper
      80  112
  • Some of the metrics are blocked by your 
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    Item type:Publication, Access status: with files ,
    Representing directions for hough transforms
    (SciTePress, 2006)
    Wenzel, Fabian
    ;
    Grigat, Rolf-Rainer
    Many algorithms in computer vision operate with directions, i. e. with representations of 3D-points by ignoring their distance to the origin. Even though minimal parametrizations of directions may contain singularities, they can enhance convergence in optimization algorithms and are required e. g. for accumulator spaces in Hough transforms. There are numerous possibilities for parameterizing directions. However, many do not account for numerical stability when dealing with noisy data. This paper gives an overview of different parametrizations and shows their sensitivity with respect to noise. In addition to standard approaches in the field of computer vision, representations originating from the field of cartography are introduced. Experiments demonstrate their superior performance in computer vision applications in the presence of noise as they are suitable for Gaussian filtering.
    Publicationtype: Conference Paper
      74  346