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检索条件"机构=Computer Science 11—Embedded Software Laboratory"

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Memetic Ant Colony Optimization for Band Selection of Hyperspectral Imagery Classification

Memetic Ant Colony Optimization for Band Selection of Hypers...

引用

Chinese Conference on Pattern Recognition (CCPR)

作者： Zexuan Zhu Zhen Ji Sen Jia Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper proposes a novel memetic ant colony optimization (MACO) algorithm for band selection on hyperspectral imagery classification. The method incorporates filter method based local search and ant colony optimization (ACO) based global search to take advantage of both. Particularly, the local search fine-tunes the paths explored by the ants by adding the relevant bands and eliminating irrelevant/redundant ones. A comparison study to the filters methods (including Gain Ratio, ReliefF, AP based method, and FCBF) and the counterpart wrapper ACO feature selection on four hyperspectral imagery datasets demonstrates that MACO is capable of attaining competitive or better classification accuracy with fewer selected bands. The empirical results suggest that MACO is effective and efficient in identifying relevant bands while eliminating irrelevant/redundant ones.

关键词： Hyperspectral imaging Accuracy Memetics Ant colony optimization Yttrium Radio frequency Correlation

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Hand-Based Biometrics Fusing Palmprint and Finger-Knuckle-Print

Hand-Based Biometrics Fusing Palmprint and Finger-Knuckle-Pr...

引用

International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB)

作者： Linlin Shen Li Bai Zhen Ji Shenzhen Key Laboratory of Embedded System Design School of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Nottingham Nottingham UK

Although biometrics technology has progressed substantially, its performance is still to be improved for real applications. This paper aims to improve the accuracy of personal identification, when only single sample is registered as template, by integrating multiple hand-based biometrics, i.e. palmprint and finger-knuckle-print. To make fusion much easier, the same feature, so called fusion code, and decision level fusion strategy are used. Experimental results show that much better performance than single modal biometrics has been achieved.

关键词： Biometrics Feature extraction Databases Accuracy Fingers Face Pixel

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Feature Selection Technique for Hyperspectral Imagery Classification with Noise Reduction Preprocessing

Feature Selection Technique for Hyperspectral Imagery Classi...

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Chinese Conference on Pattern Recognition (CCPR)

作者： Sen Jia Zhen Ji Zexuan Zhu Yuntao Qian Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science University of Zhejiang Hangzhou China

The rich information available in hyperspectral imagery has posed significant opportunities for material classification and identification. The main problem encountered with the classification process is the high dimensionality of hyperspectral data and the low-sized training dataset. Hence, dimensionality reduction is often adopted to avoid the "curse of dimensionality" phenomenon. However, noise generated by various sources (primarily the sensor and the atmosphere) inevitably decrease the precision of the classifier. In this paper, two wavelet-based methods, wavelet shrinkage and discrete wavelet transform, are applied to preprocess the hyperspectral imagery in sequence, denoising the spatial images and spectral signatures, respectively. After that, affinity propagation, which is a recently proposed feature selection approach, is used to choose representative features from the noise-reduced data. Experimental results demonstrate that the features acquired by the new scheme make the classification results more accurate than those without noise reduction preprocessing.

关键词： Hyperspectral imaging Discrete wavelet transforms Noise reduction Noise Accuracy

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Hierarchical alternating least squares algorithm with Sparsity Constraint for hyperspectral unmixing

Hierarchical alternating least squares algorithm with Sparsi...

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IEEE International Conference on Image Processing

作者： Sen Jia Yuntao Qian Jiming Li Yan Li Zhong Ming Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science University of Zhejiang Hangzhou China

In this paper, we not only extend the temporal hierarchical alternating least squares (HALS) to spatial domain, but also incorporate two necessary characteristics of material abundances, full additivity and sparsity, to unmix hyperspectral data. The new algorithm is abbreviated as HALSSC (HALS with Sparsity Constraint). Different from the other endmember extraction approaches, the proposed algorithm does not need the existence assumption of pure pixel of each endmember in the scene. Experimental results on highly mixed synthetic data and real hyperspectral data from Washington DC mall confirm the accuracy of the developed algorithm.

关键词： Hyperspectral imaging Pixel Signal processing algorithms Data mining Reflectivity Materials

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Feature extraction and selection hybrid algorithm for hyperspectral imagery classification

Feature extraction and selection hybrid algorithm for hypers...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sen Jia Yuntao Qian Jiming Li Weixiang Liu Zhen Ji Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science University of Zhejiang Hangzhou China

Due to the enormous amounts of data contained in hyperspectral imagery, the main challenge for hyperspectral image classification is to improve the accuracy with less computation complexity. Hence, dimensionality reduction (DR) is often adopted, which includes two different kinds of methods, feature extraction and feature selection. In this paper, discrete wavelet transform (DWT) and affinity propagation (AP), which belong to feature extraction and feature selection respectively, are combined together to accomplish the DR task. Firstly, DWT-based features are extracted from the original hyperspectral data; secondly, AP is applied to select representative features from the obtained ones. Experimental results demonstrate that, compared with some other DR methods which only make use of feature extraction or feature selection, the features acquired by the hybrid technique make the classification results more accurate.

关键词： Feature extraction Hyperspectral imaging Discrete wavelet transforms Accuracy Approximation methods Noise

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Secure Mobile Services by Face and Speech Based Personal Authentication

Secure Mobile Services by Face and Speech Based Personal Aut...

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Linlin Shen Songhao Zheng Wei Li Nengheng Zheng Shenzhen Key Laboratory of Embedded System Design College of Computer Science & Software Engineerin College of Information & Engineering Shenzhen University Shenzhen China

With the popularity of mobile devices, people are now able to access mobile services like website surfing, e-banking, e-shopping and voice-mail etc. anywhere anytime. Most of the services require secure biometric based personal authentication. This paper aims to improve the accuracy of personal authentication in mobile environment by integrating multiple modal biometrics, i.e. face and speech. A specially designed Mobile Biometry (MOBIO) database was used to test the performances. While facial biometrics achieved an average EER of 27.3％, speech biometrics achieved an average EER of 33.4％. The multiple modals biometric system reduces the average error rate to 21.0％, which clearly proves the usefulness of fusing face and speech information.

关键词： Face recognition speech recognition fusion

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A Gabor wavelet based palmprint recognition algorithm and its implementation on TI TMS320VC5509A platform

A Gabor wavelet based palmprint recognition algorithm and it...

引用

Chinese Conference on Pattern Recognition

作者： Zeng, Qiming Ji, Zhen You, Junmin Li, Wei College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Shenzhen Key Laboratory of Embedded System Design Shenzhen 518060 China

ISBN: (纸本)9781424472109

A Gabor wavelet based palmprint recognition algorithm is introduced in this paper. With high recognition rate and low computational complexity, the algorithm is suitable for portable platform. TI TMS320VC5509A is a high performance digital signal processor (DSP) provided by Texas Instruments Corporation. By using the 5509A chip as the core processor, working with AVR microcontroller, CPLD device and CMOS image sensor, the hardware platform is constructed. By porting the recognition algorithm to the hardware platform, a portable palmprint recognition system is proposed in this paper. Experimental results on PolyU palmprint database and actual captured palmprint image database have demonstrated that, the implemented recognition system can indicate palmprints effectively with little computation time and chip resources required. ©2010 IEEE.

关键词： Palmprint recognition

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Coding Gabor Features for Multi-Modal Biometrics

Coding Gabor Features for Multi-Modal Biometrics

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Chinese Conference on Pattern Recognition (CCPR)

作者： Linlin Shen Zhen Ji Yuwen Li Li Bai Shenzhen Key Laboratory of Embedded System Design School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Information Technology Shenzhen China School of Computer Science University of Nottingham UK

Although biometrics technology has progressed substantially, its performance is still to be improved for real applications. This paper aims to improve the accuracy of personal identification, when only single sample is registered as template, by integrating multiple hand-based biometrics. To make fusion much easier, the same feature, so called fusion code, and decision level fusion strategy are used. Two fusion cases, face & palmprint and FKP & palmprint were taken as examples to verify the effectiveness. Experimental results show that much better performance than single modal biometrics has been achieved.

关键词： Face Biometrics Feature extraction Databases Testing Biomedical imaging Accuracy

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L1/2 Sparsity Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing

L1/2 Sparsity Constrained Nonnegative Matrix Factorization f...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Yuntao Qian Sen Jia Jun Zhou Antonio Robles-Kelly College of Computer Science University of Zhejiang Hangzhou China Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China Canberra Research Laboratory NICTA Canberra ACT Australia College of Engineering and Computer Science Australian National University Canberra ACT Australia

Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint, sparsity has been modeled making use of L 1 or L 2 regularizers. However, the full additivity constraint of material abundances is often overlooked, hence, limiting the practical efficacy of these methods. In this paper, we extend the NMF algorithm by incorporating the L 1/2 sparsity constraint. The L 1/2 -NMF provides more sparse and accurate results than the other regularizers by considering the end-member additivity constraint explicitly in the optimisation process. Experiments on the synthetic and real hyperspectral data validate the proposed algorithm.

关键词： Hyperspectral imaging Pixel Signal to noise ratio Equations Cost function Materials

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Face recognition using gabor wavelet and self-adaptive intelligent single particle optimizer

Face recognition using gabor wavelet and self-adaptive intel...

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Chinese Conference on Pattern Recognition

作者： Zhou, Jiarui Ji, Zhen Huang, Weigan Tian, Tao College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Shenzhen Key Laboratory of Embedded System Design Shenzhen 518060 China

ISBN: (纸本)9781424472109

A Gabor wavelet based face recognition algorithm of PSOGabor is introduced in this paper. By using particular Gabor filters in feature extraction on important region of face image, PSOGabor can attain more representative feature information with less processing time. To overcome the drawback of overly dependence on the input parameters in intelligent single particle optimizer (ISPO), an improved algorithm of self-adaptive ISPO (AdpISPO) is proposed in this paper. With no critical parameter required, AdpISPO can achieve higher performance than original algorithms. Experimental results demonstrated that, by employing AdpISPO in Gabor filters' selection, PSOGabor can attain higher recognition rate with less computational time. ©2010 IEEE.

关键词： Face recognition

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