Simon Reich

Group(s): Computer Vision
Email:
sreich@gwdg.de
Phone: +49 551/ 39 10764
Room: E.01.103

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    Year
    Title
    Journal / Proceedings / Book
    Reich, S. and Abramov, A. and Papon, J. and Wörgötter, F. and Dellen, B. (2013).
    A Novel Real-time Edge-Preserving Smoothing Filter. International Conference on Computer Vision Theory and Applications, 5 - 14.
    BibTeX:
    @inproceedings{reichabramovpapon2013,
      author = {Reich, S. and Abramov, A. and Papon, J. and Wörgötter, F. and Dellen, B.},
      title = {A Novel Real-time Edge-Preserving Smoothing Filter},
      pages = {5 - 14},
      booktitle = {International Conference on Computer Vision Theory and Applications},
      year = {2013},
      location = {Barcelona (Spain)},
      month = {February 21-24},
      url = http://www.visapp.visigrapp.org/Abstracts/2013/VISAPP_2013_Abstracts.htm},
      abstract = The segmentation of textured and noisy areas in images is a very challenging task due to the large variety of objects and materials in natural environments, which cannot be solved by a single similarity measure. In this paper, we address this problem by proposing a novel edge-preserving texture filter, which smudges the color values inside uniformly textured areas, thus making the processed image more workable for color-based image segmentation. Due to the highly parallel structure of the method, the implementation on a GPU runs in real-time, allowing us to process standard images within tens of milliseconds. By preprocessing images with this novel filter before applying a recent real-time color-based image segmentation method, we obtain significant improvements in performance for images from the Berkeley dataset, outperforming an alternative version using a standard bilateral filter for preprocessing. We further show that our combined approach leads to better segmentations in terms of a standard performance measure than graph-based and mean-shift segmentation for the Berkeley image dataset.}}
    		
    Abstract: The segmentation of textured and noisy areas in images is a very challenging task due to the large variety of objects and materials in natural environments, which cannot be solved by a single similarity measure. In this paper, we address this problem by proposing a novel edge-preserving texture filter, which smudges the color values inside uniformly textured areas, thus making the processed image more workable for color-based image segmentation. Due to the highly parallel structure of the method, the implementation on a GPU runs in real-time, allowing us to process standard images within tens of milliseconds. By preprocessing images with this novel filter before applying a recent real-time color-based image segmentation method, we obtain significant improvements in performance for images from the Berkeley dataset, outperforming an alternative version using a standard bilateral filter for preprocessing. We further show that our combined approach leads to better segmentations in terms of a standard performance measure than graph-based and mean-shift segmentation for the Berkeley image dataset.
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