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匹配条件： “Jiliu Zhou” ，找到相关结果约42625条。
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Fractional Directional Differentiation and Its Application for Multiscale Texture Enhancement
Chaobang Gao,Jiliu Zhou,Weihua Zhang
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/325785

Abstract: This paper derives the directional derivative expression of Taylor formula for two-variable function from Taylor formula of one-variable function. Further, it proposes a new concept, fractional directional differentiation (FDD), and corresponding theories. To achieve the numerical calculation, the paper deduces power series expression of FDD. Moreover, the paper discusses the construction of FDD mask in the four quadrants, respectively, for digital image. The differential coefficients of every direction are not the same along the eight directions in the four quadrants, which is the biggest difference by contrast to general fractional differentiation and can reflect different fractional change rates along different directions, and this benefits to enlarge the differences among the image textures. Experiments show that, for texture-rich digital images, the capability of nonlinearly enhancing comprehensive texture details by FDD is better than those by the general fractional differentiation and Butterworth filter. By quantity analysis, it shows that state-of-the-art effect of texture enhancement is obtained by FDD.

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A Novel Image Denoising Algorithm Based on Riemann-Liouville Definition
Jinrong HU,Yifei Pu,Jiliu Zhou
Journal of Computers , 2011, DOI: 10.4304/jcp.6.7.1332-1338

Abstract: In this paper, a novel image denoising algorithm named fractional integral image denoising algorithm (FIIDA) is proposed, which based on fractional calculus Riemann-Liouville definition. The structures of n*n fractional integral masks of this algorithm on the directions of 135 degrees, 90 degrees, 45 degrees, 0 degrees, 180 degrees, 315 degrees, 270 degrees and 225 degrees are constructed and discussed. The denoising performance of FIIDA is measured using experiments according to subjective and objective standards of visual perception and PSNR values. The simulation results show that the FIIDA’s performance is prior to the Gaussian smoothing filter, especially when the noise standard deviation is less than 30.

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Improved DCT-Based Nonlocal Means Filter for MR Images Denoising
Jinrong Hu,Yifei Pu,Xi Wu,Yi Zhang,Jiliu Zhou
Computational and Mathematical Methods in Medicine , 2012, DOI: 10.1155/2012/232685

Abstract: The nonlocal means (NLM) filter has been proven to be an efficient feature-preserved denoising method and can be applied to remove noise in the magnetic resonance (MR) images. To suppress noise more efficiently, we present a novel NLM filter based on the discrete cosine transform (DCT). Instead of computing similarity weights using the gray level information directly, the proposed method calculates similarity weights in the DCT subspace of neighborhood. Due to promising characteristics of DCT, such as low data correlation and high energy compaction, the proposed filter is naturally endowed with more accurate estimation of weights thus enhances denoising effectively. The performance of the proposed filter is evaluated qualitatively and quantitatively together with two other NLM filters, namely, the original NLM filter and the unbiased NLM (UNLM) filter. Experimental results demonstrate that the proposed filter achieves better denoising performance in MRI compared to the others.

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Application to Three-Dimensional Canonical Correlation Analysis for Feature Fusion in Image Recognition
Xiaogang Gong,Jiliu Zhou,Huilin Wu,Gang Lei
Journal of Computers , 2011, DOI: 10.4304/jcp.6.11.2427-2433

Abstract: This paper presents a three-dimensional canonical correlation analysis (TCCA) method, and applies it to feature fusion for image recognition. It is an extension of traditional canonical correlation analysis (CCA) and two-dimensional canonical correlation analysis (2DCCA). Considering two views of a three-dimensional data, the TCCA can directly find the relations between them without reshaping the data into matrices or vectors, We stress that TCCA dramatically reduce the computational complexity, compared to the CCA and 2DCCA. To evaluate the algorithm, we are using Gabor wavelet to generate the three-dimensional data, and fusing them at the feature level by TCCA. Some experiments on ORL database and JAFEE database and compared with other methods, the results show that the TCCA not only the computing complexity is lower, the recognition performance is better but also suitable for data fusion.

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Nonlocal Mean Image Denoising Using Anisotropic Structure Tensor
Xi Wu,Mingyuan Xie,Wei Wu,Jiliu Zhou
Advances in Optical Technologies , 2013, DOI: 10.1155/2013/794728

Abstract:

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Nonlocal Mean Image Denoising Using Anisotropic Structure Tensor
Xi Wu,Mingyuan Xie,Wei Wu,Jiliu Zhou
Advances in Optical Technologies , 2013, DOI: 10.1155/2013/794728

Abstract: We present a novel nonlocal mean (NLM) algorithm using an anisotropic structure tensor to achieve higher accuracy of imaging denoising and better preservation of fine image details. Instead of using the intensity to identify the pixel, the proposed algorithm uses the structure tensor to characterize the boundary information around the pixel more comprehensively. Meanwhile, similarity of the structure tensor is computed in a Riemannian space for more rigorous comparison, and the similarity weight of the pixel (or patch) is determined by the intensity and structure tensor simultaneously. The proposed algorithm is compared with the original NLM algorithm and a modified NLM algorithm that is based on the principle component analysis. Quantitative and qualitative comparisons of the three NLM algorithms are presented as well. 1. Introduction Image denoising is a key preprocessing step for higher level of processes such as image segmentation and pattern recognition. The most straightforward denoising approach is the direct application of spatial coherence which assumes noisy samples in a local area of a given pixel follow the same distribution of that pixel [1]. Although many efforts have been done dedicatedly to overcome it such as anisotropic filtering [2] and total variation minimization [3], this kind of algorithms comes with a common drawback of image blurring due to smoothing effect in both homogeneous regions and at object boundaries. Besides denoising methods in spatial domain, removing noise in transformation domain is also well developed, such as DCT transform [4] and wavelet [5]. In contrast to spatial coherence based image smoothing, nonlocal means (NLM) denoising algorithms have been recently proposed, which average pixel intensities weighted by the similarity of pixel gray level in a certain neighborhood [6]. This kind of pixel selection scheme makes NLM significantly outperform traditional denoising methods such as anisotropic filtering [2], total variation [3], and bilateral filtering [7], which has enabled it to be used in various applications such as computer vision and statistical nonparametric regression [8, 9]. Extension of the original approach including scale and rotation invariance for the data patches used to define the weights is well studied [10–13]. However, as proposed in local denoising methods before [14], the pixel intensity itself cannot fully characterize the information contained in the image. Besides this, this kind of pointwise mean will cause large flat zones and spurious contours which are called “staircasing” effects. To

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Fractional-order quantum particle swarm optimization
Aamir Muhammad,Jiliu Zhou,Lai Xu,Yi Zhang,Yifei Pu
- , 2019, DOI: 10.1371/journal.pone.0218285

Abstract:

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Evaluation of Beijing Urban Residents’Resource Worrying Consciousness Based on the Logistic Regression Model
Jiliu Sun
International Journal of Business and Management , 2009, DOI: 10.5539/ijbm.v4n10p61

Abstract: According to the data in the survey of the urban residents’ resource conservation index of Beijing in July of 2008, the binary choice model, i.e. the Logistic regression model, is used to compare and study residents (different sexes and different ages)’ worrying degrees about the actuality of resources in China. The result of the quantitative research indicated that Chinese residents had strong resource worrying consciousness, and older residents more worried about the actuality of resources in China, and female worrying degree was higher than male’s.

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Associate Data Mining Method Research Based on Grid System
网格环境下多媒体关联规则数据挖掘方法研究
Yang Mu Zhou Jiliu Hu Yanmei,
羊牧,周激流,胡艳梅
现代图书情报技术 , 2007,

Abstract: This paper analyzes the requirements of data mining and introduces the common forms and existing problems in data mining.In the light of the problems,the authors discusse the associate data mining method of multimedia based on grid system which is the application of Apriori algorithm under the grid system.By analyzing the instance,the method is proved to have not only the accuracy of classics Apriori algorithm but also the characteristics of grid parallel excavation.Therefore,it can improve the data mining speed greatly and enhance the operation efficiency.

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Lack of correlation between obesity and gastroesophageal reflux disease (GERD) in a pediatric cohort [PDF]
Simon S. Rabinowitz, Sridhar Goli, Jiliu Xu, Xuchen Zhang, Anthony Nicastri, Virginia Anderson, Dimitre G. Stefanov, Steven M. Schwarz
Open Journal of Pediatrics (OJPed) , 2013, DOI: 10.4236/ojped.2013.34057

Abstract:

Objectives: Previous reports correlating obesity with gastroesophageal reflux disease (GERD) in children have yielded conflicting results. This study examined whether increasing BMI (body mass index) correlated with increasing grades of endoscopic and/or histopathologic GERD in a cohort of inner city children. Designs and Methods: 340 consecutive children (1 - 18 years) were classified as obese (BMI > 95%) for age and gender. Both endoscopic findings and esophageal histopathology were characterized by graded adult GERD scoring systems. Normal and abnormal grades were then stratified based on BMI. Results: In the study cohort (mean age 10.5 ± 4.7 yrs), 29% were obese (BMI > 95%) and 24% of the total cohort demonstrated endoscopic features of GERD. With increasing severity of endoscopic GERD (from normal to advanced), the percentage of obese patients at each grade did not increase (Grade 0 = 30%, Grade 1 = 26%, Grades 2 - 4 = 30%). Histopathologic findings consistent with GERD were noted in 21% of the total population studied. Again, the percentage of obese children in each diagnostic category (Grades 0 - 3) did not increase with increasing severity of inflammation. Conclusions: In this cohort of inner city children, obesity was not associated with an increased prevalence of GERD as defined by adult scoring systems.

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