A robust method for road sign detection and recognition
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Handwritten character recognition using a MLP
Knowledge-based intelligent techniques in character recognition
Real-Time Image Processing on a Focal Plane SIMD Array
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
Road Signs Recognition Using a Dynamic Pixel Aggregation Technique in the HSV Color Space
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Efficient FPGA implementation of a knowledge-based automatic speech classifier
ICESS'05 Proceedings of the Second international conference on Embedded Software and Systems
Automatic recognition of road sign passo-carrabile
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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An Automatic Road Sign Recognition System {A(RS)2} is aimed at detection and recognition of one or more road signs from realworld color images. The authors have proposed an A(RS)2 able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology SIMD Pixel Processor. Experimental trials supporting the feasibility of real-time processing on this platform are also reported.