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检索条件"机构=The Key Laboratory of Image Information Processing and Intelligent Control"

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image Compression with Downsampling and Overlapped Transform at Low Bit Rates

Image Compression with Downsampling and Overlapped Transform...

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International Conference on image processing

作者： Jiaji Wu Yan Xing Guangming Shi Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University

ISBN: (纸本)9781424456536;9781424456543

This paper proposes an image coding method based on adaptive downsampling which not only uses the pixel redundancy but also considers visual redundancy. At the encoder side, codec adaptively chooses some smooth regions of the original image to downsample, and then overlapped transform with selectivity, block DCT and adaptive-shape DCT (SA-DCT) are used against the image after being downsampled. For the incomplete transformed image, OB-SPECK is adopted to code. At the decoder side, in order to reduce the computational complexity, we use the simple cubic interpolation which not only is very suitable to the downsampled regions but also enhances greatly the real time of this coding system. Experimental results show the proposed method outperforms JPEG2000, SPECK, SPIHT, and LT+SPECK at low bit rates.

关键词： Adaptive downsampling Interpolation image compression Visual redundancy Bit rate image compression Data compression downsampling Interpolation image coding method original image Transform

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Texture image segmentation using Brushlet-domain hidden Markov models

Texture image segmentation using Brushlet-domain hidden Mark...

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Liu, Fang Yang, Kai Hao, Hongxia Hou, Biao Zhong, Hua School of Computer Science and Technology Xidian University Xi'an Shanxi 710071 China Intelligent Perception and Image Understanding Key Lab. of Ministry of Education of China Xi'an Shanxi 710071 China Intelligent Information Processing Laboratory Xidian University Xi'an Shanxi 710071 China

ISBN: (纸本)9780819478092

By researching the Brushlet domain coefficients of texture images, we found that the distribution of the magnitudes of Brushlet domain coefficients roughly meet rayleigh distribution. And there are correlations between Brushlet coefficients in adjacent scales. Therefore, Rayleigh Mixture Model (RMM) is used to characterize the statistics of the magnitudes of Brushlet coefficients. To capture the inter-scale persistence of Brushlet coefficients, a "four to four" models with markov property is adopted in this paper. On the basis, by combining with the multi-scale Bayesian segmentation method, we propose a multiscale Bayesian texture segmentation algorithm that is based on a Brushlet domain hidden Markov tree (BruHMT) model. The experiment results indicate that our method is feasible and effective. Especially for coarse texture, our method is superior than texture segmentation method using Wavelet domain hidden Markov tree (WHMT) model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Hidden Markov models

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High-Precision Discrete Convolution Algorithm Based on Moments

High-Precision Discrete Convolution Algorithm Based on Momen...

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WRI International Conference on Communications and Mobile Computing, CMC

作者： Jianguo Liu Zhenbing Liu Guoyou Wang Key Laboratory of Education Ministry for Image Processing and Intelligent Control State Key Laboratory of Multispectral Information Processing Technologies Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

Convolution is an important and useful operation in the fields of digital signal processing. In this paper, based on the previous work of performing discrete Fourier transform (DFT) via linear sums of discrete moments, we have made development to eliminate multiplications in discrete Fourier transforms by performing appropriate bit operations and shift operations in binary system, which can be implemented by integer additions of fixed points; then using the convolution theorem with the DFT, we compute the convolution with two DFTs, a point-by-point product, and an inverse DFT. Our algorithm involves fewer multiplications and can deal with arbitrary length signals.

关键词： Convolution Signal processing algorithms Discrete Fourier transforms Very large scale integration Algorithm design and analysis Laboratories Educational technology Digital signal processing Mobile communication Mobile computing

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High-Precision and Arbitrary-length Fixed-Point Fast Hartley Transform without Multiplications

High-Precision and Arbitrary-length Fixed-Point Fast Hartley...

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WRI International Conference on Communications and Mobile Computing, CMC

作者： Zhenbing Liu Jianguo Liu Guoyou Wang Key Laboratory of Education Ministry for Image Processing and Intelligent Control State Key Laboratory of Multispectral Information Processing Technologies Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

Discrete Hartley transform (DHT) is an important tool in digital signal processing. In this paper, based on our previous work of performing DHT via linear sums of discrete moments, we have made development to eliminate multiplications in discrete Hartley transforms by performing appropriate bit operations and shift in binary system, which can be implemented by integer additions of fixed points. An efficient and regular systolic array is designed to implement it, and the complexity analysis is also given. Different to other fast Hartley transforms, our algorithm can deal with arbitrary length signals and get high precision. The approach is also applicable to multi-dimensional DHT and DHT inverses.

关键词： Discrete transforms Signal processing algorithms Digital signal processing Systolic arrays Laboratories Educational technology Very large scale integration Hardware Mobile communication Mobile computing

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Fuzzy PID control of a wearable rehabilitation robotic hand driven by pneumatic muscles

Fuzzy PID control of a wearable rehabilitation robotic hand ...

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International Symposium on Micromechatronics and Human Science (MHS)

作者： Jun Wu Jian Huang Yongji Wang Kexin Xing Qi Xu Key Laboratory of Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan China

Intensive task-oriented repetitive physical therapies need be provided by individualized interaction between the patients and the rehabilitation specialists to improve hand motor performance for those survived from stroke and traumatic brain injury. The goal of this research is to develop a novel wearable device for robotic assisted hand repetitive therapy. We designed a pneumatic muscle (PM) driven therapeutic device that is wearable and provides assistive forces required for grasping and finger extension. The robot has two distinct degrees of freedom at the thumb and all other fingers. The embedded sensors can feedback position and force information for robot control and quantitative evaluation of task performance. It is potential of providing supplemental at-home therapy in addition to in the clinic treatment. To realize the trajectory tracking control, a fuzzy PID controller is designed for the proposed device. The experimental results show angle tracking control of the robotic hand using the fuzzy PID controller has better performance than that using a conventional PID controller.

关键词： Fuzzy control Three-term control Rehabilitation robotics Muscles Medical treatment Robot sensing systems Fingers Force feedback Robot control Brain injuries

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Hybrid multiobjective estimation of distribution algorithm by local linear embedding and an immune inspired algorithm

Hybrid multiobjective estimation of distribution algorithm b...

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Congress on Evolutionary Computation

作者： Dongdong Yang Licheng Jiao Maoguo Gong Hongxiao Feng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A novel hybrid multiobjective estimation of distribution algorithm is proposed in this study. It combines an estimation of distribution algorithm based on local linear embedding and an immune inspired algorithm. Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise continuous (m-1)-dimensional manifold, where m is the number of objectives. By this regularity, a local linear embedding based manifold algorithm is introduced to build the distribution model of promising solutions. Besides, for enhancing local search ability of the EDA, an immune inspired sparse individual clone algorithm (SICA) is introduced and combined with the EDA. The novel hybrid multiobjective algorithm, named HMEDA, is proposed accordingly. Compared with three other state-of-the-art multiobjective algorithms, this hybrid algorithm achieves comparable results in terms of convergence and diversity. Besides, the tradeoff proportions of EDA to SICA in HMEDA are studied. Finally, the scalability to the number of decision variables of HMEDA is investigated too.

关键词： Electronic design automation and methodology Machine learning algorithms Probability distribution Evolutionary computation Cloning Hybrid power systems Genetic mutations Genetic algorithms Sampling methods Principal component analysis

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3D medical image compression based on multiplierless low-complexity RKLT and shape-adaptive wavelet transform

3D medical image compression based on multiplierless low-com...

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IEEE International Conference on image processing

作者： Lei Wang Jiaji Wu Licheng Jiao Guangming Shi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A multiplierless low complexity reversible integer Karhunen-Loe¿ve transform (Low-RKLT) is proposed based on matrix factorization. Conventional methods based on KLT suffer from high computational complexity and unability of applying in lossless medical image compression. To solve the two problems, multiplierless Low-RKLT is investigated using multi-lifting in this paper. Combined with ROI coding method, we have proposed a progressive lossy-to-lossless ROI compression method for three dimensional (3D) medical images with high performance. In our proposed method Low-RKLT is used for the inter-frame decorrelation after SA-DWT in the spatial domain. Simulation results show that, the proposed method performs much better in both lossless and lossy compression than 3D-DWT-based method.

关键词： Biomedical imaging image coding Wavelet transforms Medical diagnostic imaging Karhunen-Loeve transforms Decorrelation Computational intelligence Computational complexity Wavelet analysis Wavelet domain

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Design RF diplexer by directional immune clonal selection algorithm

Design RF diplexer by directional immune clonal selection al...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiuyi Wu Licheng Jiao Yangyang Li Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Based on the clonal selection theory of the immune system, a novel optimal algorithm, called a directional immune clonal selection algorithm (DICSA), is proposed to design RF diplexer. In DICSA, antibody is proliferated and divided into a set of subpopulation groups. In the antibody's updating, the clonal mutation is applied to avoid premature convergences. The clonal selection realizes the information communication between the subpopulation groups so as to improve the search efficiency. DICSA is applied to a practical problem, designing RF diplexer. The results show that DICSA has stable performance during the optimization process with a satisfactory result.

关键词： Algorithm design and analysis Radio frequency Filters Laboratories information processing Design optimization Circuits Artificial intelligence Artificial immune systems Least squares methods

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Medical image segmentation based on immune clonal optimization

Medical image segmentation based on immune clonal optimizati...

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IEEE International Conference on intelligent Computing and intelligent Systems (ICIS)

作者： Wenping Ma Licheng Jiao Ronghua Shang Fujia Zhao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781424447374;9781424447541

Based on the clonal selection theory of artificial immune system, a novel optimal entropy threshold medical image segmentation method is proposed, in which, the affinity function is the optimal entropy threshold, and the medical image segmentation is considered as a optimization problem, clonal operator effectively enlarges searching range, supplies the diversity of solutions and can find the optimal threshold. This paper applies new algorithm to the challenging application: gray matter/white matter segmentation in MRI images, the algorithm is depicted in detail and the convergence is proven, the performance and computational complexity of the algorithm are described by quantitative analysis. Experiment results demonstrate the potential of the algorithm for medical image segmentation.

关键词： Biomedical imaging image segmentation Immune system Entropy Artificial immune systems Optimization methods Magnetic resonance imaging Computational complexity image analysis Performance analysis

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Feature Extraction Combining PCA and Immune Clonal Selection for Hyperspectral Remote Sensing image Classification

Feature Extraction Combining PCA and Immune Clonal Selection...

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International Conference on Artificial Intelligence and Computational Intelligence (AICI)

作者： Xiangrong Zhang Runxin Li Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A new feature extraction method based on immune clonal selection (ICSA) and PCA is proposed for classification of hyperspectral remote sensing image. As the hyperspectral remote sensing image is acquired in very narrow spectral channels, the resulting high-dimensional feature sets may contain redundant information. Therefore, feature extraction is necessary to classify a data with large dimension such as the hyperspectral remote sensing image. PCA is popularly used for feature extraction. However, in traditional PCA, selecting the larger eigenvectors as principal components implies information loss. There is no systematic way to determine which principal components (PCs) should be used. A new feature-extraction model to select the optimal principal components using ICSA is developed. The data acquired by the NASA airborne AVIRIS instrument over the Kennedy Space Center, Florida is used for evaluation. Experimental results show that our method can get better results.

关键词： Feature extraction Diversity reception Principal component analysis Hyperspectral sensors Hyperspectral imaging Remote sensing image classification Personal communication networks NASA Instruments

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