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Object Detection in Multi-channel and Multi-scale Images Based on the Structural Tensor

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Computer Analysis of Images and Patterns (CAIP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3691))

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Abstract

The paper presents theory and practical aspects of the detectors of characteristic objects in multi-channel images. It is based on the scale-space version of the structural tensor, adapted to operate on multi-channel signals. The method allows for object detection in N(2D signal space with additional respect to the scale-space. Responses of the structural tensor are composed in a linear weighted sum that allows for better signal discrimination. In such a unified tensor framework different feature detectors were defined for detection of lines, corners, lines in the Hough space, structural places, etc. Although the presented method was developed for road sing recognition is can be also used for detection of other regular shapes. The sought objects are defined by a syntactical description of building line segments and their connection type. The paper presents also experimental results and implementation details.

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Author information

Authors and Affiliations

  1. AGH – University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

Authors
  1. Bogusław Cyganek
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Editor information

Editors and Affiliations

  1. INRIA-Rocquencourt, Domaine de Voluceau, BP105, 78153, Le Chesnay, France

    André Gagalowicz

  2. Ghent University, 9000, Gent, Belgium

    Wilfried Philips

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© 2005 Springer-Verlag Berlin Heidelberg

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Cyganek, B. (2005). Object Detection in Multi-channel and Multi-scale Images Based on the Structural Tensor. In: Gagalowicz, A., Philips, W. (eds) Computer Analysis of Images and Patterns. CAIP 2005. Lecture Notes in Computer Science, vol 3691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556121_70

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  • DOI: https://doi.org/10.1007/11556121_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28969-2

  • Online ISBN: 978-3-540-32011-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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