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  • Performance comparisons of contour-based corner detectors

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    AwrangjebPUB162.pdf (630.1Kb)
    Author(s)
    Awrangjeb, Mohammad
    Lu, Guojun
    Fraser, Clive S
    Griffith University Author(s)
    Awrangjeb, Mohammad
    Year published
    2012
    Metadata
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    Abstract
    Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues—curve smoothing and curvature estimation, ...
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    Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues—curve smoothing and curvature estimation, which have major impacts on the corner detection performance, are discussed. A number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.
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    Journal Title
    IEEE Transactions on Image Processing
    Volume
    21
    Issue
    9
    DOI
    https://doi.org/10.1109/TIP.2012.2200493
    Copyright Statement
    © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Image Processing
    Computer Vision
    Photogrammetry and Remote Sensing
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Cognitive Sciences
    Publication URI
    http://hdl.handle.net/10072/172508
    Collection
    • Journal articles

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