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IEEE International Conference on Cognitive Informatics

作者： Liu, Yongguo Hao, Jianrui Peng, Jun School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China College of Electronic Information Engineering Chongqing University of Science and Technology Chongqing 400050 China

ISBN: (纸本)9781424480401

In this paper, a novel method for predicting RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS) is proposed. In RNATS, two search models, intensification search and diversification search, are designed to exploit the local regions around the current solution and explore the unvisited space, respectively. Simulation experiments are conducted for six RNA sequences to show that the proposed method is feasible and effective. © 2010 IEEE.

关键词： Tabu search

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Teaching Reform and Innovation of the Course - Digital Image processing Experiments

Teaching Reform and Innovation of the Course - Digital Image...

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The 5th International Conference on Computer Science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE'10)

作者： Haifeng Zhao Jin Tang Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230039P. R. China

Digital image processing is an interdisciplinary course, which needs students have a strong background in mathematics. To help them change from passive learning to active learning, teaching reform and innovation of the course "Digital Image processing Experiments" is discussed in this paper. The combined platform of experiments includes TI DSP experimental box and three different kinds of programming languages. The six experimental projects cover the basic theories of digital image processing. The result can offer a significant reference for the teaching innovations in the other related specialties.

关键词： digital image processing:experimental teaching matlab:DSP

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Block wavelet transforms of image reconstruction using compressed sampling

Block wavelet transforms of image reconstruction using compr...

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Congress on Image and signal processing, CISP

作者： Liang Zhang Xian-Liang Wu Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China Department of Physics and Electronic Engineering Hefei Teachers College Hefei China

ISBN: (数字)9781424465163

ISBN: (纸本)9781424465132

This paper describes the compressed sampling using the block wavelet transform which has more flexibility in reconstruction the images. Compressed sampling is considered for signals and images that are sparse in a wavelet basis. We propose a process of the image by sampling the data far below Nyquist rate in terms of compressed sampling, which shows that image data can be reconstructed from an extremely small set of measurements than what is generally considered necessary. A novel wavelet based interframe compression scheme has been developed and put into practice. It is based on a unique block wavelet transform that we have developed. BWT based interframe compression is very efficient in both compression and speed performance. The compression performance of our image codec hold a slightly lower PSNR value produces a more visually pleasing result. This implementation also preserves the scalability of the wavelet embedded coding technique.

关键词： Wavelet transforms Image coding Image reconstruction Complexity theory Filter bank Image resolution

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A Novel Random Sampling Method for Radar Image Compression

A Novel Random Sampling Method for Radar Image Compression

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International Conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Liang Zhang Ming-Sheng Chen Lei Xu Xian-Liang Wu Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China Department of Physics and Electronic Engineering Hefei Teachers College Hefei China

In this paper, we propose a process of the radar image data by Random sampling. The sampling rate is far lower than Nyquist-Shannon sampling theorem, which shows that image data can be reconstructed from an extremely small set of measurements than what is generally considered necessary. Compressive Sampling is considered for signals and images that are sparse in a wavelet basis. A low complexity compression method for high resolution image based on block partition in wavelet region is proposed. The image is partitioned into blocks in wavelet domain and then compressed separately with CS. The bit rates for each block is allocated according to the texture complexity of the block. The proposed method eliminates the "block effect" caused by traditional block partition in pixel domain, and solves the problem caused by traditional block partition that the areas with simple texture are good in reconstruction quality while those with complicated texture are too poor to be used because of the uneven distribution of texture complexity. The experimental results show that the compression performance of the proposed method is quite similar to the results obtained by global compression. This compression method is particularly suitable for the high resolution remote sensing image which sparse in a wavelet domain.

关键词： Image coding Wavelet transforms Image reconstruction Radar imaging Filter bank

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An automatic image annotation method based on the mutual K-nearest neighbor graph

An automatic image annotation method based on the mutual K-n...

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International Conference on Natural Computation (ICNC)

作者： Yu Tang Guo Bin Luo Department of Computer Science and Technology Hefei University of Technology Hefei China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

In order to improve the accuracy of the image annotation, an automatic image annotation method based on mutual K-nearest neighbor graph (MKNN) is proposed. The proposed algorithm describes the relationship between low-level features, annotation words and image by a mutual K-nearest neighbor graph. Semantic information is extracted by exploiting the mutual relationship of two nodes in the mutual K-nearest neighbor graph. Inverse document frequency (IDF) is introduced to adjust the weights of edges between the image node and its annotation word's node, which overcomes the deviation caused by high-frequency words. Experimental results in Corel image dataset show that the proposed algorithm improves effectively the image annotation performance..

关键词： Image edge detection Semantics Training Nearest neighbor searches Computational modeling Vocabulary Visualization

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Image denoising based on Non-Local means and multi-scale dyadic wavelet transform

Image denoising based on Non-Local means and multi-scale dya...

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International Conference on Computer Science and Information Technology (CSIT)

作者： Gang Yu Yong Yin Hongjun Wang Zhi Liu Dengwang Li School of Information science and engineering Shandong University Lin'an China Key Laboratory of Intelligent Computing & Information Processing Ministry of Education Xiangtan China

A variety of wavelet transform methods have been introduced to remove noise from images. However, many of these algorithms remove the fine details and smooth the structures of the image when removing noise. The wavelet coefficient magnitude sum (WCMS) algorithm can preserve edges, but it is at the expense of removing noise. The Non-Local means algorithm can removing noise effective. But it tend to cause distortion ( eg white). Meanwhile, when the noise is large, the method is not so effective. In this paper, we propose an efficient denoising algorithm. we denoised the image with non-local means algorithm in the spatial domain and WCMS algorithm in wavelet domain, weighted, combined them and got the image that we want. The experiment shows that our algorithm can improve PSNR form 0.6 dB to 1.0 dB and the image boundary is more clearly.

关键词： Discrete wavelet transforms Image edge detection PSNR Histograms Noise reduction

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CBCT image denoising based on multi-scale wavelet transform

CBCT image denoising based on multi-scale wavelet transform

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Yong Yin Gang Yu Hongjun Wang Zhi Liu Dengwang Li School of Information science and engineering Shandong University Jinan China Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan China

Cone-Beam Computed Tomography (CBCT) has always been in the forefront of medical image processing. The denoising as a image pre-processing, has a great affected on the image analysis and recognition. In this paper, a new algorithm for image denoising was proposed. By thresholding the interscale wavelet coefficient magnitude sum(WCMS) within a cone of influence (COI), the wavelet coefficients are classified into 2 categories: irregular coefficients, and edge-related and regular coefficients. They are processed by different ways. Meanwhile according to the projection image sequences characteristics in CBCT system, an effective noise variance estimated methods was proposed. The experiment shows that our algorithm can improve PSNR form 1.3dB to 2.6dB, and the image border is more clearly.

关键词： Noise Wavelet coefficients Image reconstruction Classification algorithms Noise reduction Image edge detection Image denoising

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On Dynamic Weighting of Data in Clustering with K-Alpha Means

On Dynamic Weighting of Data in Clustering with K-Alpha Mean...

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International Conference on Pattern Recognition

作者： Si-Bao Chen Hai-Xian Wang Bin Luo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Child Development and Learning Science of Ministry of Education Research Center of Learning Science South-East University Nanjing Jiangsu China

ISBN: (纸本)9781424475421

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances.

关键词： Clustering algorithms Heuristic algorithms Indexes Partitioning algorithms signal processing algorithms Euclidean distance Iris

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International Conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Zhujin Zhang Shuo Wang Xingyi Zhang Zheng Zhang School of Computer and Information Science University of South Australia Adelaide SA Australia Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

Randić et al. proposed a significant graphical representation for DNA sequences, which is very compact and avoids loss of information. In this paper, we build a fast algorithm for this graphical representation with time complexity O(n 2 ), and find another important advantage in the representation: no degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the representation. The approach adopts four elements of covariance matrix as a descriptor, and is illustrated on the first exon of beta-globin genes from 11 different species.

关键词： Rabbits Educational institutions Bovine Humans Sun DNA Mice

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Study of high-resolution range profile for monostatic MIMO radar based on SIAR

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2010年第4期32卷 734-739+763页

作者： Zhao, Guang-Hui Chen, Bai-Xiao Gao, Zhao-Zhao Intelligent Perception and Image Understanding Key Lab of Ministry of Education of China Xidian Univ. Xi'an 710071 China National Key Lab for Radar Signal Processing Xidian Univ. Xi'an 710071 China National Information Control Laboratory Chengdu 610036 China

Conventional pulse compression use a periodical echo of single receive antenna, which is modulated by a certain carrier-frequency, in other words, single spectrum is exploited. But for MIMO radar, as the multi-carrier-frequency signals are transmitted simultaneously, if the spectrum of the target echo after channel separation can be combined to form the whole band spectrum echo, the corresponding range resolution can improve several times as compared with the conventional method, and it will be more convenient for follow-up detection and tracking. Considering the difference between the frequency modulation band and the interval between the adjacent frequencies, the spectrum joint after channel separation will be overlapped or spaced. The methods of spectrum moving of each echo and the spectrum extrapolation with Root-MUSIC algorithm are proposed, by which high-resolution range profile of the target is obtained. Simulation results verify the validity of these methods.

关键词： Multiple signal classification

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