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Institution

Hong Kong Baptist University

EducationHong Kong, China
About: Hong Kong Baptist University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & China. The organization has 7811 authors who have published 18919 publications receiving 555274 citations. The organization is also known as: Hong Kong Baptist College & HKBU.


Papers
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Journal ArticleDOI
TL;DR: A novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and a new image signature based on the RH model for supervised texture classification are presented.
Abstract: In this correspondence, we propose a novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and present a new image signature based on the RH model for supervised texture classification. Our RH makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients, and the concatenation of the RH model parameters for all wavelet subbands forms the so-called RH signature. To justify the usefulness of the RH signature, we discuss and investigate some of its statistical properties. These properties would clarify the sufficiency of the signature to characterize the wavelet subband information. In addition, we shall also present an efficient RH signature extraction algorithm based on the coefficient-counting technique, which helps to speed up the overall classification system performance. We apply the RH signature to texture classification using the well-known databases. Experimental results show that our proposed RH signature in conjunction with the use of symmetrized Kullback-Leibler divergence gives a satisfactory classification performance compared with the current state-of-the-art methods.

135 citations

Journal ArticleDOI
TL;DR: Using a strategy of semi-continuous cultures coupled with feeding at a low concentration, molasses without pretreatment has the same effect as pretreated one on supporting the algal cell growth, lipid and astaxanthin production of Chlorella zofingiensis.

135 citations

Journal ArticleDOI
TL;DR: This article reviews the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis, and summarizes the models and algorithms for low-Rank matrix recovery and illustrates their advantages and limitations with numerical experiments.
Abstract: Low-rank modeling generally refers to a class of methods that solves problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing, and bioinformatics. Recently, much progress has been made in theories, algorithms, and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attention to this topic. In this article, we review the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis. We first give an overview of the concept of low-rank modeling and the challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this article with some discussions.

135 citations

Journal ArticleDOI
TL;DR: In this paper, chemical analyses showed that the most abundant BDE congeners found were BDE-209 in both workplace dust and home dust, followed by BDE 99 and BDE 47.

135 citations


Authors

Showing all 7946 results

NameH-indexPapersCitations
Weihong Tan14089267151
Bin Liu138218187085
Jun Lu135152699767
John P. Giesy114116262790
Qiang Yang112111771540
Ming Hung Wong10371039738
Wei Wang95354459660
Jianhua Zhang9241528085
Xiaojun Wu91108831687
Guibin Jiang8885034633
Shu Tao8763927304
Paul K.S. Lam8748525614
Cheng-Yong Su8758132322
Hai-Long Jiang8619830946
Baowen Li8347723080
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202346
2022246
20211,655
20201,479
20191,244
20181,093