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IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Chirayuth Sreecholpech Somying Thainimit Kasetsart Signal & Image Processing Laboratory Department of Electrical Engineering Kasetsart University Bangkok Thailand

This paper proposes a method to segment iris area from the closed-up eye image. The method is a model-based method. It approximates pupil boundary and iris boundary using two circles, and approximates eyelids using two parabolas. The proposed method utilizes intensity gradient with local refinement to detect pupil boundary. A new concept of using signal to noise ratio (SNR), a ratio of mean and standard deviation of the edge image as a feature to detect outer iris boundary is introduced. Eyelids and eyelashes are located using dark line detector. The detected outer iris boundary and eyelids are modeled using a weighted integral approach. From our experiments, the SNR of an edge image is a robust feature. The proposed iris segmentation method can be applied on different iris databases without parameter tunings. The proposed method is validated using two databases: 250 images from CASIA-IRISV3-Interval database and 250 images from KSIP_DB01R database. Performance of our proposed method is evaluated by comparing the obtained segmented iris with its ground truth image. The ground truths of these 500 images are generated manually. The proposed system yields 92.44% correct segmentation rate (CSR) for CASIA-IRISV3-Interval database and 89.21% for KSIP_DB01R database. The average error rate (AER) of false accept rate (FAR) and false reject rate (FRR) is 5.44% for CASIA-IRISV3 and 8.16% for KSIP_DB01R. Additionally, results of our proposed segmentation method are compared to the results of the open source iris segmentation method. Our proposed system yields half the FRR error of the open source method.

关键词： Robustness Iris image segmentation image databases Spatial databases Eyelids image edge detection signal to noise ratio Computer vision Eyelashes

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An empirical mode decomposition and composite kernel approach to increase hyperspectral image classification accuracy

An empirical mode decomposition and composite kernel approac...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Begüm Demir Sarp Ertürk Electronics and Telecommunication Engineering Department Kocaeli University Laboratory of Image and Signal Processing Kocaeli Turkey

This paper proposes to increase the classification accuracy of hyperspectral images based on Empirical Mode Decomposition (EMD) algorithm and composite kernels. EMD is a signal decomposition algorithm and decomposes signals into several Intrinsic Mode Functions (IMFs) and a final residue. In this paper, two-dimensional EMD is initially applied to each hyperspectral image band separately and IMFs of hyperspectral image bands are obtained. Composite kernels are used to combine the information contained in the first IMFs and second IMFs of all bands and kernel based Support Vector Machine (SVM) is used for classification. Experimental results confirm the usefulness of the proposed approach compared to direct SVM approach.

关键词： Kernel Hyperspectral imaging image classification Hyperspectral sensors Support vector machines Support vector machine classification signal processing algorithms Matrix decomposition Personal communication networks Laboratories

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Wavelet shrinkage denoising of intrinsic mode functions of hyperspectral image bands for classification with high accuracy

Wavelet shrinkage denoising of intrinsic mode functions of h...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Begüm Demir Sarp Ertürk M. Kemal Güllü Electronics and Telecommunication Engineering Department Kocaeli University Laboratory of Image and Signal Processing Kocaeli Turkey

This paper proposes Empirical Mode Decomposition (EMD) followed by wavelet shrinkage denoising in hyperspectral image classification to improve classification accuracy. EMD decomposes signals into several Intrinsic Mode Functions (IMFs) and a final residue. In this paper, firstly, EMD is applied to each hyperspectral image band separately to obtain the IMFs of all image bands. Then, the first IMF of each band is applied to wavelet shrinkage denoising, as this IMF includes all local high spatial frequency components. The sums of lower order IMFs are then used to reconstruct hyperspectral image bands that are used as new features for classification. Support Vector Machine (SVM) based classification is used as classification approach in this paper. Experimental results show the effectiveness of the proposed approach.

关键词： Noise reduction Hyperspectral imaging Support vector machines Support vector machine classification Frequency image classification Kernel image reconstruction Wavelet transforms Neural networks

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Spectral derivative feature coding for hyperspectral signature analysis

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Pattern Recognition 2009年第3期42卷 395-408页

作者： Chang, Chein-I Chakravarty, Sumit Chen, Hsian-Min Ouyang, Yen-Chieh Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Environmental Restoration and Disaster Reduction Research Center National Chung Hsing University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Department of Radiology China Medical University Hospital Taichung Taiwan

This paper presents a new approach to hyperspectral signature analysis, called spectral derivative feature coding (SDFC). It is derived from texture features used in texture classification to dictate gradient changes among adjacent bands in characterizing spectral variations so as to improve better spectral discrimination and classification. In order to evaluate its performance, two known binary coding methods, spectral analysis manager (SPAM) and spectral feature-based binary coding (SFBC) are used to conduct comparative analysis. Experimental results demonstrate that the proposed SDFC performs more effectively in capturing spectral characteristics than do SPAM and SFBC. © 2008 Elsevier Ltd. All rights reserved.

关键词： Spectrum analysis

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Application of independent component analysis for activation detection in functional magnetic resonance imaging (FMRI) data

Application of independent component analysis for activation...

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IEEE/SP Workshop on Statistical signal processing (SSP)

作者： Mahsa Akhbari Emad Fatemizadeh Biomedical Signal and Image Processing Laboratory (BiSIPL) Department of Electrical Engineering Sharif university of Technology Iran

In this extended summary, our aim is analyzing functional magnetic resonance imaging (fMRI) data by independent component analysis (ICA) in order to find regions of brain which were activated by neural activity in human brain. We employ the minimum description length (MDL) criterion to reduce the dimension of the data and estimate the number of components, which makes ICA work more efficiently. We also use a simple oscillating index method to select automatically the components of interest. MDL and oscillating index criteria have not already been used in applying ICA for analyzing fMRI data. In order to investigate the advantage of using MDL and oscillating index, we perform some experiments for both simulated and experimental fMRI dataset and show the results. In order to justify the performance, receiver operating characteristic (ROC) curve have been drawn.

关键词： Independent component analysis Magnetic resonance imaging Data analysis image analysis signal analysis Humans Blood signal processing image processing Laboratories

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A biologically plausible learning method for neurorobotic systems

A biologically plausible learning method for neurorobotic sy...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Heydar Davoudi Bijan Vosoughi Vahdat Biomedical Signal and Image Processing Laboratory (BiSIPL) Electrical Engineering Department Sharif University of Technology Tehran Iran

This paper introduces an incremental local learning algorithm inspired by learning in neurobiological systems. This algorithm has no training phase and learns the world during operation, in a lifetime manner. It is a semi-supervised algorithm which combines soft competitive learning in input space and linear regression with recursive update in output space. This method is also robust to negative interference and compromises bias-variance dilemma. These qualities make the learning method a good nonlinear function approximator having possible applications in neuro-robotic systems. Some simulations illustrate the effectiveness of the proposed algorithm in function approximation, time-series prediction, and motor control problems.

关键词： Learning systems Function approximation Biological system modeling Linear regression Interference Motor drives Humans Space technology Statistical learning Shape

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Extracting activated regions of fMRI data using unsupervised learning

Extracting activated regions of fMRI data using unsupervised...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heydar Davoudi Ali Taalimi Emad Fatemizadeh Biomedical Signal and Image Processing Laboratory (BiSIPL) Department of Electrical Engineering Sharif University of Technology Tehran Iran

Clustering approaches are going to efficiently define the activated regions of the brain in fMRI studies. However, choosing appropriate clustering algorithms and defining optimal number of clusters are still key problems of these methods. In this paper, we apply an improved version of Growing Neural Gas algorithm, which automatically operates on the optimal number of clusters. The decision criterion for creating new clusters at the heart of this online clustering is taken from MB cluster validity index. Comparison with other so-called clustering methods for fMRI data analysis shows that the proposed algorithm outperforms them in both artificial and real datasets.

关键词： Data mining Unsupervised learning Clustering algorithms Partitioning algorithms Data analysis Heart Clustering methods Feature extraction Quantization Finite impulse response filter

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Real-time processing of simplex growing algorithm

Real-time processing of simplex growing algorithm

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2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

作者： Wu, Chao-Cheng Chang, Chein-I. Ren, Husen Chang, Yang Lang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Center for Space and Remote Sensing Research National Central University Jhong-Li Tao-Yuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781424433957

Simplex growing algorithm (SGA) was recently developed as an alternative to the N-finder algorithm (N-FINDR) which is shown to be a promising endmember extraction technique. This paper further extends the SGA to a real-time processing algorithm, referred to as real-time SGA (RT SGA) that can effectively address four major issues arising in practical implementation for N-FINDR, (1) use of random initial endmembers which causes inconsistent final results, (2) very high computational complexity which results from an exhaustive search for finding all endmembers simultaneously, (3) requirement of dimensionality reduction because of enormous data volumes to be processed and (4) lack of real-time capability. ©2009 IEEE.

关键词： Extraction

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ECG denoising using modulus maxima of wavelet transform

ECG denoising using modulus maxima of wavelet transform

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mohammad Ayat Mohammad B. Shamsollahi Behrooz Mozaffari Shahrzad Kharabian College of Engineering Department of Electrical Engineering American University of Sharjah UAE Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Iran

ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal.

关键词： Electrocardiography Noise reduction Wavelet transforms signal to noise ratio Gaussian noise Biomedical engineering Noise level Distortion Wavelet analysis signal analysis

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Kernel-based Linear Spectral Mixture Analysis for hyperspectral image classification

Kernel-based Linear Spectral Mixture Analysis for hyperspect...

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Workshop on Hyperspectral image and signal processing: Evolution in Remote Sensing, WHISPERS

作者： Keng-Hao Liu Englin Wong Chein-I Chang Remote Sensing Signal and Image Processing Laboratory the Department of Computer Science and Electrical Engineering Department University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.

关键词： image analysis Spectral analysis Hyperspectral imaging image classification Least squares methods Hyperspectral sensors Kernel Subspace constraints Remote sensing image resolution

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