From the Publisher:
PCNN's represent a new advance in imaging technology, allowing images to be refined to levels well beyond that of the original. This volume provides an introduction to the topic by reviewing the theoretical foundations as well as a number of image processing applications, including segmentation, edge extraction, texture extraction, object identification, object isolation, motion processing, noise suppression, and image fusion. This is the first book to cover PCNN technology, an area which will have many applications in medical, military and industrial imaging.

Image processing using pulse-coupled neural networks

Using self-organising maps in the detection and recognition of road signs

Automatic road sign recognition systems are aimed at detection and recognition of one or more road signs from real-world color images. In this research, road signs are detected and extracted from real world scenes on the basis of their color and shape features. A dynamic region growing technique is adopted to enhance color segmentation results obtained in the HSV color space. The technique is based on a dynamic threshold that reduces the effect of hue instability in real scenes due to external brightness variation. Classification is then performed on extracted candidate regions using multilayer perceptron neural networks. The obtained results show good detection and recognition rates of the entire system with real outdoor scenes, using several light conditions. Finally, the implementation of the neural layer on the Georgia Institute of Technology SIMD Pixel Processor is outlined.

A neural network based automatic road signs recognizer

This paper presents a real-time system to detect speed limit signs and remind drivers about the allowable speed limit in a specific road. The developed system consists of two main tasks, namely detection and recognition. In our work, speed limit sign is detected and extracted from real world scenes on the basis of their color and shape features. The detection task is based on a combination of color segmentation and shape detection techniques. It significantly speeds up the shape detection process by calculating the cross-correlation in frequency domain. Next, classification is then performed on extracted candidate region using multi-layer perceptron neural networks. Experiment results proved the feasibility of this system.

A Speed limit Sign Recognition System Using Artificial Neural Network

Increasing safety and reducing road accidents, thereby saving lives, are one of the great interests in the context of advanced driver assistance systems. Apparently, among the complex and challenging tasks of future intelligent vehicles is road lanes detection and road signs recognition. In this paper, a multitask driver assistance system has been proposed. First, the system provides the driver with real-time information from lanes markers and road signs, which consist of the most important and challenging tasks. Secondly, it generates an acoustic warning to the driver in advance of any danger. This warning then allows the driver to take appropriate corrective actions in order to mitigate or completely avoid the event. The proposed system was tested on real road scene captured from moving vehicle. From the experimental results, the system has demonstrated a robust performance for detecting the road lanes and signs under different conditions.

Robust road lanes and traffic signs recognition for driver assistance system

A Connectionist Traffic Sign Recognition System for Onboard Driver Information

In this paper, we deal with the development of a support system for vehicle handling based on a traffic sign recognition system. This pattern recognition system consists of three modules dedicated to detection, recognition, and knowledge-based control. Neural networks are used as adaptive classifiers. The stage of development and the applicability of such a system are discussed.

S. Schreiber

Papers

A Connectionist Traffic Sign Recognition System for Onboard Driver Information

A connectionist traffic sign recognition system for onboard driver information