원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.6 No.2
2013.04
pp.165-174
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Texture contains high and low frequency information which could be hierarchically extracted by scattering the texture along multiple paths, with a cascade of wavelet modulus operators implemented in a deep convolutional network, which builds a scattering energy distribution network. Therefore, the scattering transform is used, in this paper, to get texture energy features. Besides, the classification of scattering energy feature matrix at all levels is done by using the Ostu global threshold processing method. Experimental results indicate that high accuracy can be achieved for both texture segmentation and license plate location with the proposed methods.
목차
Abstract
1. Introduction
2. Wavelet Scattering Convolution Network
2.1. Wavelet Modulus
2.2. Scattering Operator
2.3. Scattering Convolution Network
3. Texture Segmentation Based on Wavelet Scattering ConvolutionNetwork
4. Experimental Results and Discussion
4.1. Artificial Texture Segmentation
4.2. License Plate Location
5. Conclusion
Acknowledgements
References
1. Introduction
2. Wavelet Scattering Convolution Network
2.1. Wavelet Modulus
2.2. Scattering Operator
2.3. Scattering Convolution Network
3. Texture Segmentation Based on Wavelet Scattering ConvolutionNetwork
4. Experimental Results and Discussion
4.1. Artificial Texture Segmentation
4.2. License Plate Location
5. Conclusion
Acknowledgements
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보