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Báo cáo hóa học: " Fast Pattern Detection Using Normalized Neural Networks and Cross-Correlation in the Frequency Domain"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Fast Pattern Detection Using Normalized Neural Networks and Cross-Correlation in the Frequency Domain | EURASIP Journal on Applied Signal Processing 2005 13 2054-2060 2005 Hindawi Publishing Corporation Fast Pattern Detection Using Normalized Neural Networks and Cross-Correlation in the Frequency Domain Hazem M. El-Bakry Multimedia Devices Laboratory University ofAizu Aizu Wakamatsu 965-8580 Japan Email d8071106@u-aiza.ac.jp Qiangfu Zhao Multimedia Devices Laboratory University ofAizu Aizu Wakamatsu 965-8580 Japan Email qf-zhao@u-aizu.ac.jp Received 12 January 2004 Revised 20 December 2004 Neural networks have shown good results for detection of a certain pattern in a given image. In our previous work a fast algorithm for object face detection was presented. Such algorithm was designed based on cross-correlation in the frequency domain between the input image and the weights of neural networks. Our previous work also solved the problem of local subimage normalization in the frequency domain. In this paper the effect of image normalization on the speedup ratio of pattern detection is presented. Simulation results show that local subimage normalization through weight normalization is faster than subimage normalization in the spatial domain. Moreover the overall speedup ratio of the detection process is increased as the normalization of weights is done offline. Keywords and phrases fast pattern detection neural networks cross-correlation image normalization. 1. INTRODUCTION Pattern detection is a fundamental step before pattern recognition. Its reliability and performance have a major influence in a whole pattern recognition system. Nowadays neural networks have shown very good results for detecting a certain pattern in a given image 1 2 3 . But the problem with neural networks is that the computational complexity is very high because the networks have to process many small local windows in the images 4 5 . Some authors tried to speed up the detection process of neural networks 6 7 8 . They proposed a multilayer perceptron MLP algorithm for fast object face detection. .