Computer Science ›› 2020, Vol. 47 ›› Issue (12): 197-204.doi: 10.11896/jsjkx.191000054

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Blurred Image Recognition Based on LoG Edge Detection and Enhanced Local Phase Quantization

CHEN Xiao-wen, LIU Guang-shuai, LIU Wang-hua, LI Xu-rui   

  1. School of Mechanical Engineering Southwest Jiaotong University Chengdu 610031,China
  • Received:2019-10-11 Revised:2020-03-10 Published:2020-12-17
  • About author:CHEN Xiao-wen,,born in 1995,post-graduate.His main research interests include machine vision and image iden-tification.
    LIU Guang-shuai,,born in 1978,Ph.D,associate professor.His main research interests include reverse engineering,graph and image processing.
  • Supported by:
    National Natural Science Foundation of China(51275431) and Science and Technology Support Project of Sichuan Province(2015GZ0200).

Abstract: As ablur insensitive texture descriptorLocal phase quantization (LPQ) algorithm describes phase features with blurred invariance in accurately.Besidesit lacks in describing important details of images.In order to solve the issuesan enhanced local phase quantization (ELPQ) combined with Laplace of Gaussian (LoG) edge detection is proposed in this papernamed MrELPQ&MsLoG(Multi-resolution ELPQ and Multi-scale LoG).Firstlythe real and imaginary parts obtained by performing the short-term Fourier transform on the image arepositive and negative quantification and amplitude quantizationcomplementary symbol feature ELPQ_S and amplitude feature ELPQ_M are obtained.Secondlythe edge features in spatial domain are obtained by convolving images with multi-scale Laplace of Gaussian filters.Finallythe symbol feature ELPQ_S and the amplitude feature ELPQ_M in the frequency domain are combined with edge features on the spatial domain.The recognition result is calculated through SVM.On the Brodatz and KTH-TIPS texture data bases with blurred interferencethe ELPQ algorithm has a great improvement over the original LPQ algorithm.Moreoverthe MrELPQ&MsLoG algorithm can further improve the recognition rate of the algorithm.On the ARExtend Yale Band railway fastener data bases with blurred interferencecompared with the current algorithm which has robustness to the blurthe MrELPQ&MsLoG algorithm always maintains a high recognition rate.The experimental results show that the MrELPQ&MsLoG algorithm is robust to blur and has less time for feature extraction.

Key words: Blurred robustness, Edge feature, Frequency domain, Local phase quantization, Spatial domain

CLC Number: 

  • TP391.41
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