This paper presents a supervised classification method of sonar image, which takes advantages of both multi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet d...This paper presents a supervised classification method of sonar image, which takes advantages of both multi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is obtained. In the part of classifier construction, the Learning Vector Quantization (LVQ) network is adopted as a classifier. Experiments of sonar image classification were carried out with satisfactory results, which verify the effectiveness of this method.展开更多
Phase Doppler anemometry(PDA) is very sensitive to the shape of testing particles, which is based on sphericity assumption and Mie’s theory. In practice, there exists effectiveness of non sphericity and the response ...Phase Doppler anemometry(PDA) is very sensitive to the shape of testing particles, which is based on sphericity assumption and Mie’s theory. In practice, there exists effectiveness of non sphericity and the response of PDA system deviates from the theoretical prediction. In this paper, the statistic characteristics of PDA signal are analyzed and a method of identifying and quantifying irregular particles is proposed. It is concluded that phase difference of PDA signal for irregular particles is an unbiased estimation for spherical particles.展开更多
文摘This paper presents a supervised classification method of sonar image, which takes advantages of both multi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is obtained. In the part of classifier construction, the Learning Vector Quantization (LVQ) network is adopted as a classifier. Experiments of sonar image classification were carried out with satisfactory results, which verify the effectiveness of this method.
文摘Phase Doppler anemometry(PDA) is very sensitive to the shape of testing particles, which is based on sphericity assumption and Mie’s theory. In practice, there exists effectiveness of non sphericity and the response of PDA system deviates from the theoretical prediction. In this paper, the statistic characteristics of PDA signal are analyzed and a method of identifying and quantifying irregular particles is proposed. It is concluded that phase difference of PDA signal for irregular particles is an unbiased estimation for spherical particles.
