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Edge Detection in Hyperspectral Imaging: Multivariate Statistical Approaches

  • Sergey Verzakov
  • Pavel Paclík
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)

Abstract

Edge detection is well developed area of image analysis. Many various kinds of techniques were designed for one-channel images. Also, a considerable attention was paid to edge detection in color, multispectral, and hyperspectral images. However, there are still many open issues in edge detection in multichannel images. For example, even the definition of multichannel edge is rather empirical and is not well established. In this paper statistical pattern recognition methodology is used to approach the problem of edge detection by considering image pixels as points in a multidimensional feature space. Appropriate multivariate techniques are used to retrieve information which can be useful for edge detection. The proposed approaches were tested on the real-world data.

Keywords

Probability Density Function Edge Detection Hyperspectral Image Joint Probability Density Function Multivariate Statistical Approach 
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 2006

Authors and Affiliations

  • Sergey Verzakov
    • 1
  • Pavel Paclík
    • 1
  • Robert P. W. Duin
    • 1
  1. 1.Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands

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