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Parametric improvement of lateral interaction in accumulative computation in motion-based segmentation

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dc.contributor.author Fernández Caballero, Antonio es_ES
dc.contributor.author López Bonal, María Teresa es_ES
dc.contributor.author Martínez Cantos, Javier es_ES
dc.contributor.author Carmona Suárez, Enrique es_ES
dc.date.accessioned 2012-03-20T07:10:51Z
dc.date.available 2012-03-20T07:10:51Z
dc.date.issued 2008 es_ES
dc.identifier.citation Neurocomputing, 2008, 71(4-6): 776-786 es_ES
dc.identifier.issn 0957-4174 es_ES
dc.identifier.uri http://hdl.handle.net/10578/2092
dc.description.abstract Segmentation of moving objects is an essential component of any vision system. However, its accomplishment is hard due to some challenges such as the occlusion treatment or the detection of objects with deformable appearance. In this paper an artificial neuronal network approach for moving object segmentation, called lateral interaction in accumulative computation (LIAC), which uses accumulative computation and recurrent lateral interaction is revisited. Although the results reported for this approach so far may be considered relevant, the problems faced each time (environment, objects of interest, etc.) make that the system outcome varies. Hence, our aim is to improve segmentation provided by LIAC in a double sense: by removing the detected objects not matching some size or compactness constraints, and by learning suitable parameters that improve the segmentation behavior through a genetic algorithm. es_ES
dc.format text/plain en_US
dc.language.iso es en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Ingenierías es_ES
dc.title Parametric improvement of lateral interaction in accumulative computation in motion-based segmentation es_ES
dc.type info:eu-repo/semantics/article en_US


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