Earticle

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

키워드

저자

• Huajuan Wu [ College of Computer Science and Engineering, Northwest Normal University, College of Computer Science and Engineering, Changshu Institute of Technology ]
• Mingjun Li [ School of Computer and Information Engineering, Harbin University of Commerce ]
• Mingxin Zhang [ College of Computer Science and Engineering, Northwest Normal University, College of Computer Science and Engineering, Changshu Institute of Technology ]
• Jinlong Zheng [ College of Computer Science and Engineering, Changshu Institute of Technology ]
• Jian Shen [ College of Computer Science and Engineering, Changshu Institute of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605