The Experts below are selected from a list of 19761 Experts worldwide ranked by ideXlab platform

Michael Beetz - One of the best experts on this subject based on the ideXlab platform.

  • The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-Adapting Separation Criteria
    International Journal of Computer Vision, 2004
    Co-Authors: Robert Hanek, Michael Beetz
    Abstract:

    The task of fitting Parametric Curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, object tracking, and 3-D reconstruction. In this article, we propose the Contracting Curve Density ( CCD ) algorithm as a solution to the Curve-fitting problem. The CCD algorithm extends the state-of-the-art in two important ways. First, it applies a novel likelihood function for the assessment of a fit between the Curve model and the image data. This likelihood function can cope with highly inhomogeneous image regions, because it is formulated in terms of local image statistics . The local image statistics are learned on the fly from the vicinity of the expected Curve. They provide therefore locally adapted criteria for separating the adjacent image regions. These local criteria replace often used predefined fixed criteria that rely on homogeneous image regions or specific edge properties. The second contribution is the use of blurred Curve models as efficient means for iteratively optimizing the posterior density over possible model parameters. These blurred Curve models enable the algorithm to trade-off two conflicting objectives, namely heaving a large area of convergence and achieving high accuracy. We apply the CCD algorithm to several challenging image segmentation and 3-D pose estimation problems. Our experiments with RGB images show that the CCD algorithm achieves a high level of robustness and sub-pixel accuracy even in the presence of severe texture, shading, clutter, partial occlusion, and strong changes of illumination.

Damjan Strnad - One of the best experts on this subject based on the ideXlab platform.

  • SURFMOD: teaching tool for Parametric Curve and surface methods in CAGD based on comparison and analysis
    IEEE Transactions on Education, 2006
    Co-Authors: Nikola Guid, Simon Kolmanič, Damjan Strnad
    Abstract:

    Parametric Curves and surfaces are topics usually included in different disciplines, such as computer graphics, geometric modeling, computer-aided design (CAD), computer-aided geometric design (CAGD), etc. These rapidly emerging and extensive fields require students to understand a wide range of methods. In this context, SURFMOD, a teaching tool for Parametric Curves and surfaces, was constructed. SURFMOD enables interactive investigation, different quality analyses, and simultaneous studies of various methods using the same data, while, at the same time, adjusting the control parameters

Robert Hanek - One of the best experts on this subject based on the ideXlab platform.

  • The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-Adapting Separation Criteria
    International Journal of Computer Vision, 2004
    Co-Authors: Robert Hanek, Michael Beetz
    Abstract:

    The task of fitting Parametric Curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, object tracking, and 3-D reconstruction. In this article, we propose the Contracting Curve Density ( CCD ) algorithm as a solution to the Curve-fitting problem. The CCD algorithm extends the state-of-the-art in two important ways. First, it applies a novel likelihood function for the assessment of a fit between the Curve model and the image data. This likelihood function can cope with highly inhomogeneous image regions, because it is formulated in terms of local image statistics . The local image statistics are learned on the fly from the vicinity of the expected Curve. They provide therefore locally adapted criteria for separating the adjacent image regions. These local criteria replace often used predefined fixed criteria that rely on homogeneous image regions or specific edge properties. The second contribution is the use of blurred Curve models as efficient means for iteratively optimizing the posterior density over possible model parameters. These blurred Curve models enable the algorithm to trade-off two conflicting objectives, namely heaving a large area of convergence and achieving high accuracy. We apply the CCD algorithm to several challenging image segmentation and 3-D pose estimation problems. Our experiments with RGB images show that the CCD algorithm achieves a high level of robustness and sub-pixel accuracy even in the presence of severe texture, shading, clutter, partial occlusion, and strong changes of illumination.

  • fitting Parametric Curve models to images using local self adapting separation criteria
    2004
    Co-Authors: Robert Hanek
    Abstract:

    The task of fitting Parametric Curve models to boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, 3-D reconstruction, and object tracking. In this thesis, we propose the Contracting Curve Density (CCD) algorithm and the CCD tracker as solutions to this problem. The CCD algorithm solves the Curve-fitting problem for a single image whereas the CCD tracker solves it for a sequence of images. The CCD algorithm extends the state-of-the-art in two important ways. First, it applies a novel likelihood function for the assessment of a fit between the Curve model and the image data. This likelihood function can cope with highly inhomogeneous image regions because it is formulated in terms of local image statistics that are learned on the fly from the vicinity of the expected Curve. Second, the CCD algorithm employs blurred Curve models as efficient means for iteratively optimizing the posterior density over possible model parameters. Blurred Curve models enable the algorithm to trade-off two conflicting objectives, namely a large area of convergence and a high accuracy. The CCD tracker is a fast variant of the CCD algorithm. It achieves a low runtime, even for high-resolution images, by focusing on a small set of carefully selected pixels. In each iteration step, the tracker takes only such pixels into account that are likely to further reduce the uncertainty of the Curve. Moreover, the CCD tracker exploits statistical dependencies between successive images, which also improves its robustness. We show how this can be achieved without substantially increasing the runtime. In extensive experimental investigations, we demonstrate that the CCD approach outperforms other state-of-the-art methods in terms of accuracy, robustness, and runtime. The CCD algorithm and the CCD tracker achieve sub-pixel accuracy and robustness even in the presence of strong texture, shading, clutter, partial occlusion, poor contrast, and substantial changes of illumination. We present results for different Curve-fitting problems such as image segmentation, 3-D pose estimation, and object tracking.

Nikola Guid - One of the best experts on this subject based on the ideXlab platform.

  • SURFMOD: teaching tool for Parametric Curve and surface methods in CAGD based on comparison and analysis
    IEEE Transactions on Education, 2006
    Co-Authors: Nikola Guid, Simon Kolmanič, Damjan Strnad
    Abstract:

    Parametric Curves and surfaces are topics usually included in different disciplines, such as computer graphics, geometric modeling, computer-aided design (CAD), computer-aided geometric design (CAGD), etc. These rapidly emerging and extensive fields require students to understand a wide range of methods. In this context, SURFMOD, a teaching tool for Parametric Curves and surfaces, was constructed. SURFMOD enables interactive investigation, different quality analyses, and simultaneous studies of various methods using the same data, while, at the same time, adjusting the control parameters

Luiz Velho - One of the best experts on this subject based on the ideXlab platform.