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Implicitly Weighted Methods in Robust Image Analysis

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    0379860 - ÚI 2013 RIV US eng J - Journal Article
    Kalina, Jan
    Implicitly Weighted Methods in Robust Image Analysis.
    Journal of Mathematical Imaging and Vision. Roč. 44, č. 3 (2012), s. 449-462. ISSN 0924-9907. E-ISSN 1573-7683
    R&D Projects: GA MŠk(CZ) 1M06014
    Institutional research plan: CEZ:AV0Z10300504
    Keywords : robustness * high breakdown point * outlier detection * robust correlation analysis * template matching * face recognition
    Subject RIV: BB - Applied Statistics, Operational Research
    Impact factor: 1.767, year: 2012

    This paper is devoted to highly robust statistical methods with applications to image analysis. The methods of the paper exploit the idea of implicit weighting, which is inspired by the highly robust least weighted squares regression estimator. We use a correlation coefficient based on implicit weighting of individual pixels as a highly robust similarity measure between two images. The reweighted least weighted squares estimator is considered as an alternative regression estimator with a clear interpretation. We apply implicit weighting to dimension reduction by means of robust principal component analysis. Highly robust methods are exploited in tasks of face localization and face detection in a database of 2D images. In this context we investigate a method for outlier detection and a filter for image denoising based on implicit weighting.
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