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Yibin, China

Zhong F.,Southwest Jiaotong University | Zhong F.,Yibin University | Zhang J.,Southwest Jiaotong University
IEEE Transactions on Image Processing | Year: 2013

Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method. © 1992-2012 IEEE.

Zhong F.,Southwest Jiaotong University | Zhong F.,Yibin University | Zhang J.,Southwest Jiaotong University
Neurocomputing | Year: 2013

This paper presents a novel approach based on enhanced local directional patterns (ELDP) to face recognition, which adopts local edge gradient information to represent face images. Specially, each pixel of every facial image sub-block gains eight edge response values by convolving the local 3×3 neighborhood with eight Kirsch masks, respectively. ELDP just utilizes the directions of the most prominent edge response value and the second most prominent one. Then, these two directions are encoded into a double-digit octal number to produce the ELDP codes. The ELDP dominant patterns (ELDPd) are generated by statistical analysis according to the occurrence rates of the ELDP codes in a mass of facial images. Finally, the face descriptor is represented by using the global concatenated histogram based on ELDP or ELDPd extracted from the face image which is divided into several sub-regions. The performances of several single face descriptors not integrated schemes are evaluated in face recognition under different challenges via several experiments. The experimental results demonstrate that the proposed method is more robust to non-monotonic illumination changes and slight noise without any filter. © 2013 Elsevier B.V.

Qin F.,Yibin University
Research Journal of Applied Sciences, Engineering and Technology | Year: 2013

In order to improve the quality of restored image, a blind image restoration algorithm is proposed, in which both the Signal-to-Noise Ratio (SNR) and the Gaussian Point Spread Function (PSF) of the degraded image are estimated. Firstly, the SNR of the degraded image is estimated through local deviation method. Secondly, the PSF of the degraded image is estimated through error-parameter method. Thirdly, Utilizing the estimated SNR and PSF, high resolution image is restored through Wiener filtering restoration algorithm. Experimental results show that the quality and peak signal-to-noise of the restored image are better around the real value and justify the fact that the SNR an-d PSF estimation plays great important part in blind image restoration. © Maxwell Scientific Organization, 2013.

Qin F.,Yibin University
2012 5th International Congress on Image and Signal Processing, CISP 2012 | Year: 2012

In order to improve the quality of the defocus blurred image, the defocus point spread function (PSF) of the imaging system needs to be estimated. A blind image restoration algorithm was proposed, in which the defocus PSF of the blurred image was estimated through error-parameter estimation method. Firstly, the error-parameter curve was generated through Wiener filtering algorithm. Then, by analyzing the error-parameter curve, the defocus radius of the blurred image was estimated. Finally, utilizing the estimated PSF, image restoration was performed through Wiener filtering algorithm. Experimental results showed that the defocus PSF was estimated with high accuracy, and justified the fact that the defocus PSF estimation plays a great important part in blind image restoration. © 2012 IEEE.

Qin X.,Hangzhou Normal University | Chang S.-S.,Yibin University | Cho Y.J.,Gyeongsang National University
Nonlinear Analysis: Real World Applications | Year: 2010

In this paper, we consider an iterative method for finding a common element of the set of a generalized equilibrium problem, of the set of solutions to a system of variational inequalities and of the set of fixed points of a strict pseudo-contraction. Strong convergence theorems are established in the framework of Hilbert spaces. The results presented in this paper improve and extend the corresponding results announced by many others. © 2009 Elsevier Ltd. All rights reserved.

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