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Correspondence consensus of two sets of correspondences through optimisation functions

  • Identification data

    Identifier:  imarina:5128993
    Authors:  Francisco Moreno-Garcia, Carlos; Serratosa, Francesc
    Abstract:
    We present a consensus method which, given the two correspondences between sets of elements generated by separate entities, enounces a final correspondence consensus considering the existence of outliers. Our method is based on an optimisation technique that minimises the cost of the correspondence while forcing (to the most) to be the mean correspondence of the two original correspondences. The method decides the mapping of the elements that the original correspondences disagree and returns the same element mapping when both correspondences agree. We first show the validity of the method through an experiment in ideal conditions based on palmprint identification, and subsequently present two practical experiments based on image retrieval.
  • Others:

    Link to the original source: https://link.springer.com/article/10.1007/s10044-015-0486-y
    APA: Francisco Moreno-Garcia, Carlos; Serratosa, Francesc (2017). Correspondence consensus of two sets of correspondences through optimisation functions. Pattern Analysis And Applications, 20(1), 201-213. DOI: 10.1007/s10044-015-0486-y
    Paper original source: Pattern Analysis And Applications. 20 (1): 201-213
    Article's DOI: 10.1007/s10044-015-0486-y
    Journal publication year: 2017
    Entity: Universitat Rovira i Virgili
    Paper version: info:eu-repo/semantics/acceptedVersion
    Record's date: 2025-02-08
    URV's Author/s: MORENO GARCIA, CARLOS FRANCISCO / Serratosa Casanelles, Francesc d'Assís
    Department: Enginyeria Informàtica i Matemàtiques
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Publication Type: Journal Publications
    Author, as appears in the article.: Francisco Moreno-Garcia, Carlos; Serratosa, Francesc
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Thematic Areas: Medicina i, Matemática / probabilidade e estatística, Interdisciplinar, Engenharias iv, Computer vision and pattern recognition, Computer science, artificial intelligence, Ciências biológicas i, Ciência da computação, Artificial intelligence, Administração pública e de empresas, ciências contábeis e turismo
    Author's mail: francesc.serratosa@urv.cat
  • Keywords:

    Munkres algorithm
    Graph edit distance
    Bipartite graph matching
    Artificial Intelligence
    Computer Science
    Computer Vision and Pattern Recognition
    Medicina i
    Matemática / probabilidade e estatística
    Interdisciplinar
    Engenharias iv
    Ciências biológicas i
    Ciência da computação
    Administração pública e de empresas
    ciências contábeis e turismo
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