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Proceedings Paper

Door detection in images based on learning by components
Author(s): Grazia Cicirelli; Tiziana D'Orazio; Nicola Ancona
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Paper Abstract

In this paper we present a vision-based technique for detecting targets of the environment which has to be reached by an autonomous mobile robot during its navigational task. The targets the robot has to reach are the doors of our office building. Color and shape information are used as identifying features for detecting principal components of the door. In fact in images the door can appear of different dimensions depending on the attitude of the robot with respect to the door, therefore detection of the door is performed by detecting its most significant components in the image. Positive and negative examples, in form of image patterns, are manually selected from real images for training two neural classifiers in order to recognize the single components. Each classifier has been realized by a feed-forward neural network with one hidden layer and sigmoid activation function. Moreover for selecting negative examples, relevant for the problem at hand, a bootstrap technique has been used during the training process. Finally the detecting system has been applied to several test real images for evaluating its performance.

Paper Details

Date Published: 5 October 2001
PDF: 6 pages
Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); doi: 10.1117/12.444196
Show Author Affiliations
Grazia Cicirelli, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Tiziana D'Orazio, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Nicola Ancona, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)

Published in SPIE Proceedings Vol. 4572:
Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall, Editor(s)

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