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Gaussian-weighted Jensen–Shannon divergence as a robust fitness function for multi-model fitting

Zhou, K.

Machine vision and applications. VOL 24; NUMBER 6, ; 2013, 1107-1119 -- Springer Science + Business Media Part 6; (pages 1107-1119) -- 2013

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  • Title:
    Gaussian-weighted Jensen–Shannon divergence as a robust fitness function for multi-model fitting
  • Author: Zhou, K.
  • Found In: Machine vision and applications. VOL 24; NUMBER 6, ; 2013, 1107-1119
  • Journal Title: Machine vision and applications.
  • Subjects: Electrical and Electronic Engineering; Mechanical Engineering; Civil Engineering; LCC: TA1632; Dewey: 006.42
  • Publication Details: Springer Science + Business Media
  • Language: English
  • Abstract: Model fitting is a fundamental component in computer vision for salient data selection, feature extraction and data parameterization. Conventional approaches such as the RANSAC family show limitations when dealing with data containing multiple models, high percentage of outliers or sample selection bias, commonly encountered in computer vision applications. In this paper, we present a novel model evaluation function based on Gaussian-weighted Jensen–Shannon divergence, and integrate into a particle swarm optimization (PSO) framework using ring topology. We avoid two problems from which most regression algorithms suffer, namely the requirements to specify inlier noise scale and the number of models. The novel evaluation method is generic and does not require any estimation of inlier noise. The continuous and meta-heuristic exploration facilitates estimation of each individual model while delivering the number of models automatically. Tests on datasets comprised of inlier noise and a large percentage of outliers (more than 90 % of the data) demonstrate that the proposed framework can efficiently estimate multiple models without prior information. Superior performance in terms of processing time and robustness to inlier noise is also demonstrated with respect to state of the art methods.
  • Identifier: Journal ISSN: 0932-8092
  • Publication Date: 2013
  • Physical Description: Electronic
  • Shelfmark(s): 5326.570000
  • UIN: ETOCRN335620329

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