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D-MASC: A Novel Search Strategy for Detecting Regions of Interest in Linear Parameter Space

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Similarity Search and Applications (SISAP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11223))

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Abstract

The parameter space transform has been utilized over decades in context of edge detection in the computer vision domain. However the usage of the parameter space transform in context of clustering is a more recent application with the purpose of detecting (hyper)linear correlated clusters. The runtime for detecting edges or hyperlinear correlations can be very high. The contribution of our work is to provide a novel search strategy in order to accelerate the detection of regions of interest in parameter space serving as a foundation for faster detection of edges and linear correlated clusters.

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Author information

Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Munich, Germany

    Daniyal Kazempour, Kevin Bein, Peer Kröger & Thomas Seidl

Authors
  1. Daniyal Kazempour
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  2. Kevin Bein
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  3. Peer Kröger
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  4. Thomas Seidl
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Corresponding author

Correspondence to Daniyal Kazempour .

Editor information

Editors and Affiliations

  1. University of Geneva, Carouge, Switzerland

    Stéphane Marchand-Maillet

  2. Arizona State University, Tempe, AZ, USA

    Yasin N. Silva

  3. Center for Scientific Research and Higher Education, Ensenada, Mexico

    Edgar Chávez

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Kazempour, D., Bein, K., Kröger, P., Seidl, T. (2018). D-MASC: A Novel Search Strategy for Detecting Regions of Interest in Linear Parameter Space. In: Marchand-Maillet, S., Silva, Y., Chávez, E. (eds) Similarity Search and Applications. SISAP 2018. Lecture Notes in Computer Science(), vol 11223. Springer, Cham. https://doi.org/10.1007/978-3-030-02224-2_13

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  • DOI: https://doi.org/10.1007/978-3-030-02224-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02223-5

  • Online ISBN: 978-3-030-02224-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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