A multiscale approach to contour detection by texture suppression - art. no. 60640D

Giuseppe Papari*, Patrizio Campisi, Nicolai Petkov, Alessandro Neri

*Bijbehorende auteur voor dit werk

    OnderzoeksoutputAcademicpeer review

    Samenvatting

    In this paper we propose a multiscale biologically motivated technique for contour detection by texture suppression. Standard edge detectors react to all the local luminance changes, irrespective whether they are due to the contours of the objects represented in the scene, rather than to natural texture like grass, foliage, water, etc. Moreover, edges due to texture are often stronger than edges due to true contours. This implies that further processing is needed to discriminate true contours from texture edges. In this contribution we exploit the fact that, in a multiresolution analysis, at coarser scales, only the edges due to object contours are present while texture edges disappear. This is used in combination with surround inhibition, a biologically motivated technique for texture suppression, in order to build a contour detector which is insensitive to texture. The experimental results show that our approach is also robust to additive noise.

    Originele taal-2English
    TitelImage Processing: Algorithms and Systems, Neural Networks, and Machine Learning
    RedacteurenER Dougherty, JT Astola, KO Egiazarian, NM Nasrabadi, SA Rizvi
    Plaats van productieBELLINGHAM
    UitgeverijSPIE - INT SOC OPTICAL ENGINEERING
    Pagina'sD640-D640
    Aantal pagina's12
    ISBN van geprinte versie0-8194-6104-0
    StatusPublished - 2006
    EvenementConference on Image Processing - Algorithms and Systems, Neural Networks, and Machine Learning - , Canada
    Duur: 16-jan.-200618-jan.-2006

    Publicatie series

    NaamPROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE)
    UitgeverijSPIE-INT SOC OPTICAL ENGINEERING
    Volume6064
    ISSN van geprinte versie0277-786X

    Other

    OtherConference on Image Processing - Algorithms and Systems, Neural Networks, and Machine Learning
    Land/RegioCanada
    Periode16/01/200618/01/2006

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