Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017-07-28

AUTHORS

Mohamed Elawady , Olivier Alata , Christophe Ducottet , Cécile Barat , Philippe Colantoni

ABSTRACT

Symmetry is an important composition feature by investigating similar sides inside an image plane. It has a crucial effect to recognize man-made or nature objects within the universe. Recent symmetry detection approaches used a smoothing kernel over different voting maps in the polar coordinate system to detect symmetry peaks, which split the regions of symmetry axis candidates in inefficient way. We propose a reliable voting representation based on weighted linear-directional kernel density estimation, to detect multiple symmetries over challenging real-world and synthetic images. Experimental evaluation on two public datasets demonstrates the superior performance of the proposed algorithm to detect global symmetry axes respect to the major image shapes. More... »

PAGES

344-355

References to SciGraph publications

  • 2006-09. Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2006. Detecting Symmetry and Symmetric Constellations of Features in COMPUTER VISION – ECCV 2006
  • 2016. Reflection Symmetry Detection via Appearance of Structure Descriptor in COMPUTER VISION – ECCV 2016
  • 2011. Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation in IMAGE ANALYSIS
  • 1954-06. An approximation to the density function in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • 2016. Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms in ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS
  • Book

    TITLE

    Computer Analysis of Images and Patterns

    ISBN

    978-3-319-64688-6
    978-3-319-64689-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-64689-3_28

    DOI

    http://dx.doi.org/10.1007/978-3-319-64689-3_28

    DIMENSIONS

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