Show simple item record

dc.contributor.authorSerratosa, Francesc
dc.contributor.authorAmézquita Gómez, Nicolás
dc.contributor.authorAlquézar Mancho, René
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.identifier.citationSerratosa, F., Amézquita, N., Alquézar, R. "Combining neural networks and clustering techniques for object recognition in indoor video sequences". 2006.
dc.description.abstractThis paper presents the results obtained in a real experiment for object recognition in a sequence of images captured by a mobile robot in an indoor environment. Objects are simply represented as an unstructured set of spots (image regions) for each frame, which are obtained from the result of an image segmentation algorithm applied on the whole sequence. In a previous work, neural networks were used to classify the spots independently as belonging to one of the objects of interest or the background from different spot features (color, size and invariant moments). In this work, clustering techniques are applied afterwards taking into account both the neural net outputs (class probabilities) and geometrical data (spot mass centers). In this way, context information is exploited to improve the classification performance. The experimental results of this combined approach are quite promising and better than the ones obtained using only the neural nets.
dc.format.extent8 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.otherClass probabilities
dc.titleCombining neural networks and clustering techniques for object recognition in indoor video sequences
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
local.citation.authorSerratosa, F.; Amézquita, N.; Alquézar, R.

Files in this item


This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder