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Soft Computing

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01.10.2011 | Focus

An IVFS-based image segmentation methodology for rat gait analysis

verfasst von: Pedro Couto, Aranzazu Jurio, Artur Varejão, Miguel Pagola, Humberto Bustince, Pedro Melo-Pinto

Erschienen in: Soft Computing | Ausgabe 10/2011

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Abstract

In this work, image segmentation is addressed as the starting point within a motion analysis methodology intended for rat biomechanics behavior characterization. First, we propose a general segmentation framework that uses interval valued fuzzy sets (IVFSs) to determine the optimal image threshold value. The amplitude values of the IVFSs are used for representing the unknowledge/ignorance of an expert on determining whether a pixel belongs to the background or to the object of the image. Then, we introduce an extension of this methodology that uses a heuristic-based multi-threshold approach to determine the optimal threshold. Experimental results are presented.

Vorheriger Artikel Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling

Nächster Artikel Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels

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Titel: An IVFS-based image segmentation methodology for rat gait analysis
verfasst von: Pedro Couto
Aranzazu Jurio
Artur Varejão
Miguel Pagola
Humberto Bustince
Pedro Melo-Pinto
Publikationsdatum: 01.10.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 10/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0626-7

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