Toward adaptive robust state estimation based on MCC by using the generalized Gaussian density as kernel functions

2015 ◽  
Vol 71 ◽  
pp. 297-304 ◽  
Author(s):  
Yanbo Chen ◽  
Feng Liu ◽  
Shengwei Mei ◽  
Jin Ma
Keyword(s):  
State Estimation ◽  
Kernel Functions ◽  
Gaussian Density ◽  
Generalized Gaussian Density

Bearings fault detection using wavelet transform and generalized Gaussian density modeling

Measurement ◽  
2020 ◽  
Vol 155 ◽  
pp. 107557 ◽  
Author(s):  
Xinmin Tao ◽  
Chao Ren ◽  
Yongkang Wu ◽  
Qing Li ◽  
Wenjie Guo ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Gaussian Density ◽  
Generalized Gaussian Density ◽  
Density Modeling

HANDWRITING-BASED PERSONAL IDENTIFICATION

2006 ◽  
Vol 20 (02) ◽  
pp. 209-225 ◽  
Author(s):  
ZHENYU HE ◽  
XINGE YOU ◽  
YUAN YAN TANG ◽  
BIN FANG ◽  
JIANWEI DU
Keyword(s):  
Good Method ◽  
Research Topic ◽  
Personal Identification ◽  
Identification Accuracy ◽  
Writer Identification ◽  
Challenging Problem ◽  
Elapsed Time ◽  
Gaussian Density ◽  
Generalized Gaussian Density ◽  
Active Research

Handwriting-based personal identification, which is also called handwriting-based writer identification, is an active research topic in pattern recognition. Despite continuous effort, offline handwriting-based writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting image. As a result, plenty of dynamic writing information, which is very valuable for writer identification, is unavailable for offline writer identification. In this paper, we present a novel wavelet-based Generalized Gaussian Density (GGD) method for offline writer identification. Compared with the 2-D Gabor model, which is currently widely acknowledged as a good method for offline handwriting identification, GGD method not only achieves a better identification accuracy but also greatly reduces the elapsed time on calculation in our experiments.

Parametric generalized Gaussian density estimation

1989 ◽  
Vol 86 (4) ◽  
pp. 1404-1415 ◽  
Author(s):  
Mahesh K. Varanasi ◽  
Behnaam Aazhang
Keyword(s):  
Density Estimation ◽  
Gaussian Density ◽  
Generalized Gaussian Density

scholarly journals The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guocheng Yang ◽  
Meiling Li ◽  
Leiting Chen ◽  
Jie Yu
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Low Frequency ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Medical Image Fusion ◽  
Gaussian Density ◽  
Generalized Gaussian Density ◽  
Fusion Scheme ◽  
Jensen Shannon Divergence

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.

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