R and nter for Modern Information Technology Application in Enterprise

Suzhou, China

R and nter for Modern Information Technology Application in Enterprise

Suzhou, China
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Gu L.,Randnter for Modern Information Technology Application in Enterprise | Gu L.,Nanjing University of Posts and Telecommunications
2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 | Year: 2012

The initialization of one clustering method based on one-class support vector machines often employs random samples. This way can lead to the unstable clustering results. In this paper, the k-harmonic means clustering takes the place of this random initialization. To investigate the effectiveness of the novel proposed approach, several experiments are done on one artificial dataset and two real datasets. Experimental results show that our presented method can not only obtain the stable clustering accuracies, but aloes improve the clustering performance significantly compared to other different initialization, such as random initialization and k-means initialization. © 2012 IEEE.


Gu L.,Randnter for Modern Information Technology Application in Enterprise | Gu L.,Nanjing University of Posts and Telecommunications
2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 | Year: 2012

Semi-supervised clustering takes advantage of a small amount of labeled data to bring a great benefit to the clustering of unlabeled data. Based on a locality sensitive k-means clustering method, this paper presents two novel semi-supervised clustering algorithms inspired by the semi-supervised variants of the k-means clustering by seeding. To investigate the effectiveness of our approaches, experiments are done on one artificial dataset and three real datasets. Experimental results show that two proposed methods can improve the clustering performance significantly compared to other unsupervised and semi-supervised clustering algorithms. © 2012 IEEE.


Gu L.,R and nter for Modern Information Technology Application in Enterprise | Gu L.,Nanjing University of Posts and Telecommunications
Proceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012 | Year: 2012

In this paper, a nearest neighbor rule is applied to the clustering method based on one-class support vector machines. Although the traditional clustering method inspired the k-means clustering employs the kernel-based one-class support vector machines in improving the clustering performance, it forms the coarse decision boundaries. So this paper uses a nearest neighbor rule to establishing the better decision boundaries. Experimental results show that the novel clustering algorithm can increase the clustering accuracies according to a nearest neighbor rule. © 2012 IEEE.


Cui Z.,R and nter for Modern Information Technology Application in Enterprise | Cui Z.,Soochow University of China | Zhang G.,Soochow University of China
Journal of Software | Year: 2010

Medical image classification as an important research topic both in image processing and biomedical engineering. The ridgelet transform has good directional selective ability to locally and sparsely in representing the image compared with the traditional wavelet transform. This paper proposes a novel classification model for medical image, which is using ridgelet transform and dynamic fuzzy theory. Firstly, the image was decomposed by digital ridgelet transform to obtain the approximation coefficients and detailed coefficients in different sub-bands with directional parameters. Then the dynamic fuzzy theory was applied to construct a membership function to calculate coefficients from each sub-bands respectively, and a weight of sub-bands degree was adjust by precision requirement. At last similarity degrees are calculated by coefficients degree and weight. Medical images were classified by the result sort order of the degrees effectively. © 2010 ACADEMY PUBLISHER.


Chen K.,R and nter for Modern Information Technology Application in Enterprise | Chen K.,Suzhou Vocational University | Wu J.,R and nter for Modern Information Technology Application in Enterprise | Wu J.,Suzhou Vocational University
Journal of the Optical Society of America A: Optics and Image Science, and Vision | Year: 2014

Circle detection is an important issue that has not been perfectly solved in automated image analysis to date. It is traditionally carried out via pixel-based 3D voting algorithms, involving tremendous computation and requiring huge storage space with questionable accuracy. In this report, a novel edge-section-based 1D voting algorithm is developed in circle detection to improve the detection rate and precision. Based on experiments with simulated image data and a ground-tested standard dataset, the novel scheme significantly outperformed all previous state-of-the-art schemes in detection rate and precision, and was comparable to the state of the art in processing speed. © 2014 Optical Society of America.


Zhang G.,Soochow University of China | Cui Z.,Soochow University of China | Cui Z.,R and nter for Modern Information Technology Application in Enterprise
Key Engineering Materials | Year: 2011

Graph cuts as an increasingly important tool for solving a number of energy minimization problems in computer vision and other fields, meanwhile beamlet transform as time-frequency and multiresolution analysis tool is often used in the domain of image processing, especially for image fusion. By analyzing the characters of DSA medical image, this paper proposes a novel DSA image fusion method which is combining beamlet transform and graph cuts theory. Firstly, the image was decomposed by beamlet transform to obtain the different subbands coefficients. Then an energy function based on graph cuts theory was constructed to adjust the weight of these coefficients to obtain an optimum fusion object. At last, an inverse of the beamlet transform reconstruct a synthesized DSA image which could contain more integrated accurate detail information of blood vessels. By contrast, the efficiency of our method is better than other traditional fusion methods.


Fang L.,R and nter for Modern Information Technology Application in Enterprise | Li H.,Suzhou Vocational University | Liu Z.,R and nter for Modern Information Technology Application in Enterprise | Xian X.,R and nter for Modern Information Technology Application in Enterprise
Advance Journal of Food Science and Technology | Year: 2013

To evaluate the possibility of using visible and near-infrared spectroscopy for the determination of SSC, TSC, TAC and water content in yellow peach, 60 yellow peaches with different maturity were hand-harvested from an orchard in Suzhou city, China and spectralmeasurements were done with an ASD FieldSpec 3 Portable Spectroradiometer (The wavelengths range of 350-2500 nm), on 17 August 2011. In this study, the contents of internal quality with different maturity differ greatly; Total Sugar Content (TSC) is 3.828-26.37%, Total Acid Content (TAC) is 0.383-0.961%, Soluble Solids Content (SSC) is 9.1-12.9° Brix andwater content is 81.211-90.752%. We analyzed the correlation between TSC, TAC, SSC and water content and the two indices of spectral data. These were the reciprocal-logarithm-transformed reflectance (log (1/R)) and the first-order derivative of reciprocal-logarithm-transformed reflectance (dlog (1/R)). The results showed that the spectra of yellow peach had common spectral characteristics and the pattern of the absorption curves was similar to that for other fruits. The first-order derivative of reciprocal-logarithm-transformed reflectance (dlog (1/R)) showed stronger correlation for some wavelengths. These wavelengths with stronger correlation were selected for the sensitive wavelengths and were usedfor the model calibration based on Multiple Linear Regression (MLR). TSC, TAC, SSC and water content of yellow peach were predicted at each sampling point using the multiple linemodels. Overall, although the TAC determination still needs to be improved, the determination of TSC, SSC and water content in yellow peach fruits by ASD near-infrared spectral analysis (350-2500 nm) was still successful (R2>0.61) and the corresponding RMSEs of 2.32,0.44 and 0.85%, respectively, showing that the spectroscopy has the ability to rapidly and non-destructively determine the internal quality of yellow peach. © Maxwell Scientific Organization.


Shang L.,R and nter for Modern Information Technology Application in Enterprise | Shang L.,Suzhou Vocational University | Zhou C.,R and nter for Modern Information Technology Application in Enterprise | Zhou C.,Suzhou Vocational University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

A new face recognition method, realized by the feature fusion technique based on Local Non-negative Sparse Coding (NNSC) and Local Non-negative Matrix Factorization (LNMF) algorithms, is proposed in this paper. NNSC and LNMF are both part-based representations of the multi-dimensional data, used widely and efficiently in image feature extraction and pattern recognition. Here, considered the high recognition rate, the weighting coefficient fusion method between features obtained by algorithms of NNSC and LNMF is discussed in the face recognition task. Using the distance classifier and the Radial Basis Probabilistic Neural Network (RBPNN) classifier, the recognition task is easily implemented on the ORL face database. Moreover, compared with any other algorithm of NNSC and LNMF, experimental results show that the feature fusion method is indeed efficient and applied in the face recognition. © 2010 Springer-Verlag Berlin Heidelberg.


Zhang G.,Soochow University of China | Cui Z.,Soochow University of China | Cui Z.,R and nter for Modern Information Technology Application in Enterprise | Chen J.,Soochow University of China | And 2 more authors.
Journal of Software | Year: 2010

CT image De-noising is an important research topic both in image processing and biomedical engineering. Independent component analysis (ICA) is a statistical technique where the goal is to represent a set of random variables as a linear transformation of statistically independent component variables. The curvelet transform as a multiscale transform has directional parameters occurs at all scales, locations, and orientations. This paper proposes a new model for CT medical image de-noising, which is using independent component analysis and curvelet transform. Firstly, a random matrix was produce to separate the CT image into a separated image for estimate. Then curvelet transform was applied to optimize the coefficients. At last, the coefficients were selected for image reconstruction by inverse of the curvelet transform. By contrast, this approach could remove more noises and reserve more details, and the efficiency of our approach is better than other traditional de-noising approaches. © 2010 Academy Publisher.


Zhang G.,Soochow University of China | Cui Z.,Soochow University of China | Cui Z.,R and nter for Modern Information Technology Application in Enterprise | Zhao P.,Soochow University of China | And 2 more authors.
Journal of Multimedia | Year: 2012

Vehicle video key frame processing as an important part of intelligent transportation systems plays a significant role. Traditional vehicle video key frame extraction often has lots of noises, it can't meet the requirements of the recognition and tracking. In this paper, a novel method which is combined independent component analysis with beamlet transform is proposed. Firstly, a random matrix was produce to separate the key frame into a separated image for estimate. Then beamlet transform was applied to optimize the coefficients. At last, the coefficients were selected for image reconstruction by inverse of the beamlet transform. By contrast, this approach could remove more noises and reserve more details, and the efficiency of our approach is better than other traditional de-noising approaches. © 2012 ACADEMY PUBLISHER.

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