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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Dongdong Cao Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Classification of multisource remote sensing images has been studied for decades, and many methods have been proposed. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising neural network method, its good performance not only depends on the classifier itself, but also has relation to the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multisource remote sensing image based on residual error in this paper. In particular, a feature-selection scheme approach is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feedforward neural network is investigated. The results of experiments carried out on a multisource data set confirm the validity of the proposed approach

关键词： Remote sensing Neural networks Remote monitoring Earth Feedforward neural networks Computer errors Classification algorithms Bayesian methods Robustness image processing

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Classification Fusion in Wireless Sensor Networks

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自动化学报 2006年第6期32卷 947-955页

作者： LIU Chun-Ting HUO Hong FANG Tao LI De-Ren SHEN Xiao Institute of Image Processing ＆ Pattern Recognition Shanghai Jiaotong University Shanghai 200240 State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079

In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.

关键词： Wireless sensor networks classification fusion wavelet decomposition weighted k-nearest-neighbor Dempster-Shafer theory

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A DSP/FPGA -based parallel architecture for real-time image processing

A DSP/FPGA -based parallel architecture for real-time image ...

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6th World Congress on Intelligent Control and Automation, WCICA 2006

作者： Luxin, Yan Tianxu, Zhang Sheng, Zhong Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)1424403324

DSP/FPGA-based parallel architecture oriented to real-time image processing applications is presented. The architecture is structured with high performance DSPs interconnected by FPGA. Within FPGA a FIFO interconnection network and the specific data communication protocol are implemented, which interconnect 3 DSPs (TMS320C6414) effectively. The measured performances in the prototype with the proposed parallel architecture, including inter-DSP data communication performance and system computing capacity, show high data transfer bandwidth (up to 400 Mbytes/s) with low latency as well as high image processing performance, which achieve a good balance for parallel image processing. © 2006 IEEE.

关键词： Digital signal processing

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Using Linear Intersection for Node Location Computation in Wireless Sensor Networks

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自动化学报 2006年第6期32卷 907-914页

作者： SHI Qin-Qin HUO Hong FANG Tao LI De-Ren Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan Hubei 430072

Knowing the locations of nodes in wireless sensor networks (WSN) is essential for many applications. Nodes in a WSN can have multiple capabilities and exploiting one or more of the capabilities can help to solve the localization problem. In this paper, we assume that each node in a WSN has the capability of distance measurement and present a location computation technique called linear intersection for node localization. We also propose an applied localization model using linear intersection and do some concerned experiments to estimate the location computation algorithm.

关键词： wirelell sensor network localization linear intersection error

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PROBABILISTIC SIMILARITY MEASURES ANALYSIS FOR REMOTE SENSING image RETRIEVAL

PROBABILISTIC SIMILARITY MEASURES ANALYSIS FOR REMOTE SENSIN...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： PING GUO QIAN BAO QIAN YIN Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

In image database retrieval there are many classical similarity measures that can be used to find the target image, these measures are mostly belong to geometry model from the point of view of the data model, while little attention has been devoted to the studies on methods based on probability density distribution. In this paper we experimental investigate some probabilistic similarity measures, present two methods for design of the similarity function of two mixture Gaussian distributions, on the basis of the nearest neighbor rule and K nearest neighbor rule respectively. An experimental study was conducted to examine and evaluate the measures for application to image databases, and the experiment results show that the methods based on K nearest neighbor rule achieve better performance.

关键词： Probabilistic similarity measures class separability measures mixture Gaussian distribution K nearest neighbor rule image histogram

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A NEW STRATEGY TO IMPROVE image FUSION EFFECT

A NEW STRATEGY TO IMPROVE IMAGE FUSION EFFECT

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： JIAN-WEI LIU QIAN YIN PING GUO Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

The purpose of image fusion is to combine information from several different source images to one image, which becomes reliable and much easier to be comprehended by people. Based on analyzing the relations of average and standard deviation of the two or more source images, a new strategy to improve image fusion effect and a new evaluation measure named RAS (the ratio between average and standard deviation) are proposed in this paper. We apply wavelet transform to decompose an image into low-frequency sub-image and high-frequency sub-images and apply different fusion rules respectively to low-frequency sub-image and high-frequency sub-images. According to subjective evaluation and objective criteria, such as entropy, root mean square error (RMSE), peak-to-peak signal-to-noise ratio (PSNR),RAS, the proposed strategy is very effective and universal to some extent for fusing a class of images whose average and standard deviation are approximately equal respectively through extensive experiments.

关键词： image fusion Wavelet transform Multi-Resolution (MR) RAS

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ECG Signal Maxima Detection Using Wavelet Transform

ECG Signal Maxima Detection Using Wavelet Transform

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： S. ktata k. Ouni N. Ellouze Signal Image and Pattern Recognition Laboratory National Engineering School of Tunis Tunis Tunisia

This paper deals with the application of a novel approach for ECG signal characteristic points detection by wavelet transform. The aim of this work is to detect automatically the R peaks, the T and P wave maxima, separately. After having represented the ECG equivalent in time frequency domain, we detect the complex QRS maximum and the T wave using the truncation of these waves by rectangular window. The influence of scale levels in our proposed algorithm is tested for a large amount of ECG signals. Results are given for signals extracted from the MIT/BIH database, confirming the robustness of our approach

关键词： Electrocardiography Signal detection Wavelet transforms Frequency Databases Signal analysis Continuous wavelet transforms Robustness Wavelet analysis Signal resolution

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Feature Selection Based on Adaptive Fuzzy Membership Functions

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自动化学报 2006年第4期32卷 496-503页

作者： XIE Yan-Tao SANG Nong ZHANG Tian-Xu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some ***,their rule number will grow exponentially as the data dimension *** the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for *** overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural ***,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

关键词： Membership function feature selection architecture pruning artificial neural networks

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Pitch and Energy Contribution in Emotion and Speaking styles recognition Enhancement

Pitch and Energy Contribution in Emotion and Speaking styles...

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Computational Engineering in Systems Applications, CESA, IMACS Multiconference on FLAGGED

作者： M. kammoun N. Ellouze Signal Image and Pattern Recognition Laboratory National School of Engineers of Tunis Tunis Tunisia

This paper highlights the influence of some prosodic features in enhancing the recognition accuracies of emotions and speaking styles in speech. In this work, we seek to recognize 10 emotions and speaking styles based on real speech. After having extracted a large amount of cues, we use the hidden Markov models classifier. Results are given on text-independent emotion recognition using SUSAS database. The aim of this work is to test the influence of energy and pitch in emotion recognition. Throughout this study, MFCC, log energy and pitch frequency, are used as the base features. The obtained recognition accuracy for 10 different emotions and speaking styles exceeds 85% reaching 89.35% for the slow style using the best combination of spectral and prosodic features

关键词： Emotion recognition Hidden Markov models Stress Speech analysis Spatial databases Speech processing pattern recognition Speech recognition Laboratories Speech enhancement

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JOINT BLURRED image RESTORATION WITH PARTIALLY KNOWN INFORMATION

JOINT BLURRED IMAGE RESTORATION WITH PARTIALLY KNOWN INFORMA...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： QING WU XING-CE WANG PING GUO Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

A new restoration method for joint blurred images with partially known information is proposed in this paper. The joint blur here is assumed to be motion blurs and defocus blur mixed together. Under the condition of two blur effects are supposed to be independent linear shift-invariant processes and motion blur parameter can be obtained with known information, a reduced update Kalman filter (RUKF) is used for degraded image restoration and the best defocus point spread function (PSF) parameter is determined based on the maximum entropy principle (MEP). Experimental results with real images show that the proposed approach works well.

关键词： PSF estimation Joint blurred image Reduced update Kalman filter Maximum entropy principle

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