Self-Organizing Maps
Concurrent Self-Organizing Maps for Pattern Classification
ICCI '02 Proceedings of the 1st IEEE International Conference on Cognitive Informatics
A Neural Approach for Detection of Road Direction in Autonomous Navigation
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Efficient training of artificial neural networks for autonomous navigation
Neural Computation
Color-based road detection in urban traffic scenes
IEEE Transactions on Intelligent Transportation Systems
An improved radial basis function network for visual autonomous road following
IEEE Transactions on Neural Networks
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This paper presents an original approach for visual identification of road direction in autonomous vehicle navigation using a neural network classifier called Concurrent Self-Organizing Maps (CSOM). For comparison, we also evaluate the performances of other two neural classifiers ( Multilayer Perceptron (MLP) and supervised Self-Organizing Map (SOM) ) as well as those of the well-known statistical classifier of Nearest Mean (K-Means). The proposed model has two main processing stages: (a) feature selection, using either a standard edge detection algorithm or the Hough transform; (b) classification, using one of the above mentioned classifiers. The path to be identified has been quantized in three output directions. We present the experimental results obtained by computer simulation, when for training and testing the neural model we used a data set of 210 road images from the CMU VASC Image Database. A real time neural path follower implemented on a mobile robot is also experimented.