Door detection in images based on learning by components

Grazia Cicirelli; Tiziana D'Orazio; Nicola Ancona

doi:10.1117/12.444196

5 October 2001 Door detection in images based on learning by components

Grazia Cicirelli, Tiziana D'Orazio, Nicola Ancona

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444196
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

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.

Citation Download Citation

Grazia Cicirelli, Tiziana D'Orazio, and Nicola Ancona "Door detection in images based on learning by components", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444196

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