Combining neural networks and clustering techniques for object recognition in indoor video sequences
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/86156
Tipus de documentReport de recerca
Data publicació2006-06
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
Abstract
This 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.
CitacióSerratosa, F., Amézquita, N., Alquézar, R. "Combining neural networks and clustering techniques for object recognition in indoor video sequences". 2006.
Forma partLSI-06-30-R
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
T06-2.pdf | 275,4Kb | Visualitza/Obre |