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Blurring Mean-Shift with a Restricted Data-Set Modification for Applications in Image Processing

  • Eduard Sojka
  • Jan Gaura
  • Štepán Šrubař
  • Tomáš Fabián
  • Michal Krumnikl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6455)

Abstract

A new mean-shift technique, blurring mean-shift with a restricted dataset modification, is presented. It is mainly intended for applications in image processing since, in this case, the coordinates of the points entering into the mean-shift procedure may be obviously split into two parts that are treated in different ways: The spatial part (geometrical position in image) and the range part (colour/brightness). The basic principle is similar as in the blurring mean-shift algorithm. In contrast to it, the changes of the dataset are restricted only to the range values (colour/brightness); the spatial parts do not change. The points that are processed during computation may be viewed as points of a certain image that evolves during the iterations. We show that the process converges. As a result, an image is obtained with the areas of constant colour/brightness, which can be exploited for image filtering and segmentation. The geodesic as well as Euclidean distance can be used. The results of testing are presented showing that the algorithm is useful.

Keywords

Input Image Geodesic Distance Input Point Left Image Spatial Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Eduard Sojka
    • 1
  • Jan Gaura
    • 1
  • Štepán Šrubař
    • 1
  • Tomáš Fabián
    • 1
  • Michal Krumnikl
    • 1
  1. 1.Faculty of Electrical Engineering and InformaticsVŠB - Technical University of OstravaOstrava-PorubaCzech Republic

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