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dc.creatorGnjatović, Milan
dc.creatorMaček, Nemanja
dc.creatorSaračević, Muzafer
dc.creatorAdamović, Saša
dc.creatorJoksimović, Dušan
dc.creatorKarabašević, Darjan
dc.date.accessioned2023-12-05T13:59:10Z
dc.date.available2023-12-05T13:59:10Z
dc.date.issued2023
dc.identifier.issn2075-1680
dc.identifier.urihttp://jakov.kpu.edu.rs/handle/123456789/1514
dc.description.abstractThis paper introduces a heuristic for multiple sequence alignment aimed at improving real-time object recognition in short video streams with uncertainties. It builds upon the idea of the progressive alignment but is cognitively economical to the extent that the underlying edit dis- tance approach is adapted to account for human working memory limitations. Thus, the proposed heuristic procedure has a reduced computational complexity compared to optimal multiple sequence alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation conducted in real-life settings demonstrated a significant improvement in number recognition accu- racy in short video streams under uncertainties caused by noise and incompleteness. The second line of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine learning approaches, which are typically not tailored to the task of object sequence recognition from a limited number of frames of incomplete data recorded in a dynamic scene situationsr
dc.language.isoensr
dc.publisherBasel, Switzerland : MDPIsr
dc.rightsrestrictedAccesssr
dc.sourceAxiomssr
dc.subjectmultiple sequence alignmentsr
dc.subjectobject recognitionsr
dc.subjectuncertainty in vision tasksr
dc.subjectcognitive economysr
dc.titleCognitively Economical Heuristic for Multiple Sequence Alignment under Uncertaintiessr
dc.typearticlesr
dc.rights.licenseARRsr
dc.citation.volume12
dc.citation.issue1
dc.citation.spage3
dc.identifier.doi10.3390/axioms12010003
dc.type.versionpublishedVersionsr


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