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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Liang Tao Hon Keung Kwan MOE Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei Anhui China Department of Electrical and Computer Engineering University of Windsor Windsor ONT Canada

The traditional DCT-based real-valued discrete Gabor transform (RGDT) was limited to the critical sampling case. The biorthogonality relationship between the analysis window and the synthesis window for the transform has not been unveiled. To overcome those drawbacks, this paper proposes a novel DCT-based real-valued discrete Gabor transform, which can be applied under both the critical sampling condition and the over-sampling condition. And the biorthogonality relationship between the analysis window and the synthesis window for the transform is also proved in this paper. Because it only involves real operations and can utilize fast DCT and DDCT algorithms for fast computation, it facilitates computation and implementation by hardware and/or software compared to the traditional complex-valued discrete Gabor transform.

关键词： Discrete transforms Sampling methods Hardware Discrete cosine transforms Software algorithms Biomedical signal processing Signal sampling Signal synthesis Signal processing algorithms parallel algorithms

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Expression-Compensated 3D Face Recognition with Geodesically Aligned Bilinear Models

Expression-Compensated 3D Face Recognition with Geodesically...

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Iordanis Mpiperis Sotiris Malassiotis Michael G. Strintzis Informatics and Telematics Institute Centre for Research and Technology Hellas Thessaloniki Greece Information Processing Laboratory Electrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki Greece

In this paper, we present a technique for addressing 3-dimensional face recognition in presence of facial expressions using a bilinear model. The bilinear model allows decoupling the impact of identity and expression on face appearance and encoding their contribution in separate control parameters. This is achieved by first representing faces as parametric surface models described by a fixed length parameter vector. A generic face model is fitted to each face based on a novel technique that relies on geodesic distances to find implicitly corresponding facial landmarks between the model and the face in hand. Model parameters are then used for bilinear decomposition. The experimental results on the publicly available BU-3DFE face database demonstrate the effectiveness of our technique.

关键词： Face recognition Deformable models Face detection Databases Iterative closest point algorithm Surface fitting Encoding Wavelet coefficients Nose Skin

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Side information generation with constrained relaxation for distributed multi-view video coding

Side information generation with constrained relaxation for ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hui Lv Hongkai Xiong Yongsheng Zhang Zhihai He Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering University of Missouri Columbia MO USA

Apart from the existing temporal and inter-view interpolation technique in distributed multi-view video coding (DMVC), this paper is dedicated to not only investigating multiple side information implication at the decoder, but also preserving the constrained relaxation with high-level features matching. We present a novel feature-based Wyner-Ziv coding framework (FWZC) for DMVC, which devotes scale-invariant features extraction and matching to generate inter-view correlated side information without knowing the camera parameters and has a more significant improvement rate-distortion performance. The scale-invariant local features are identified as the most resistant to image deformations and affine distortion between different views of an object or scene. The plausible filling-in from a priori distinctive feature constraints between neighboring views could make a promising compensation to inter-view side information generation for joint multi-view decoding. The experimental results show high precision for objects with high motion and quite significant improvement in the rate-distortion performance.

关键词： Video coding Decoding Cameras Interpolation Layout Rate-distortion Laboratories Feature extraction Helium Fusion power generation

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A study on the effect of camera motion on human visual attention

A study on the effect of camera motion on human visual atten...

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IEEE International Conference on Image processing

作者： Golnaz Abdollahian Zygmunt Pizlo Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Psychological Sciences Purdue University West Lafayette IN USA

The aim of this paper is to examine the effect of camera motion in user generated video with respect to human visual attention. Having a more accurate human attention model is particularly useful in applications such as video summarization where the identification of visual importance is crucial to the quality of the results. Most models proposed thus far, have not considered camera motion as an independent factor in identifying visual saliency. In this study eye movement was recorded while subjects watched videos with different types of camera motion. The distribution of the fixation points indicated high correlation between the visual saliency and the type and direction of camera movement. The statistical tests confirmed that the results are statistically significant.

关键词： Cameras Humans Layout Testing Video sequences Image processing Laboratories Psychology Information analysis Shape

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User-guided interictal spike detection

User-guided interictal spike detection

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

作者： Mahmoud El-Gohary James McNames Siegward Elsas Biomedical Signal Processing Laboratory Portland State University Portland OR USA Biomedical Signal Processing Laboratory Department of Electrical and Computer Engineering Portland State University Portland OR USA OHSU Augmentative Communication Clinic Oregon Health and Sciences University Portland OR USA

In the diagnosis and treatment of epilepsy, long-term monitoring may be required to document and study interictal activities such as interictal spikes. However, visual inspection of the EEG done by an expert is too time consuming and researchers normally resort to automatic detection methods. We describe a new EEG user-guided interictal spike detection algorithm that only requires the user to annotate a few spikes. We use the annotations to build a template that captures the relevant features of spikes, and then use Mean Squared Error (MSE) test to detect all of the other spikes in the recording. The detected events are rank ordered so that the user can easily identify the true spikes and their time of occurrence. The true spikes are then annotated to the EEG signals and reported to the EEG expert for further evaluation. This design provides a compromise between the enormous time commitments necessary to annotate recordings by hand and the inability of fully-automatic spike detection algorithms to account for the variability between subjects. Because spike morphology and spatial distribution change considerably when patients go through cycles of wake and sleep in long-term monitoring, this detection algorithm uses multichannel multiple templates to detect more than one type of event. The algorithm is able to achieve an average sensitivity of 96% and an average of 4.8 false detections/ hour.

关键词： Electroencephalography Brain modeling Detectors Feature extraction Signal processing algorithms Epilepsy Morphology

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A kidney segmentation approach from DCE-MRI using level sets

A kidney segmentation approach from DCE-MRI using level sets

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： H. Abdelmunim Aly A. Farag W. Miller Mohamed AboelGhar Department of Computer Science Computational Biomedicine Laboratory University of Houston Houston TX USA Computer Vision and Image Processing LabDepartment of Electrical and Computer Engineering University of Louisville Louisville KY USA Medical School Mansoura University Mansoura Egypt

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. Automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (DCEMRI), is of great importance. Kidney segmentation is the first step for such classification. The image intensity inside the kidney is used as an indication of failure/success. Differentiating between different cases cases is implemented by comparing subsequential kidney scans signals. So, this process is mainly dependent on segmentation. This paper introduces a new shape-based segmentation approach based on level sets. Training shapes are collected from different real data sets to represent the shape variations. Signed distance functions are used to represent these shapes. The methodology incorporates image and shape prior information in a variational framework. The shape registration is considered the backbone of the approach where more general transformations can be used to handle the process. We introduce a novel shape dissimilarity measure that enables the use of different (inhomogeneous) scales. The approach gives successful results compared with other techniques restricted to transformations with homogeneous scales. Results for segmenting kidney images will be illustrated and compared with other approaches to show the efficiency of the proposed technique.

关键词： Level set Image segmentation computer vision Magnetic resonance imaging Shape measurement Biopsy Biomedical computing computer science Image processing Biomedical imaging

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Numerical simulation of two-dimensional photonic crystal with FDTD method

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Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology 2008年第3期30卷 5-7页

作者： Zhou, Jian Jiang, Hong-Zhou Zhang, Lin Wang, Lin Zeng, Chun-Nian Chen, Ji State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan 430070 China School of Information Engineering Wuhan University of Technology Wuhan 430070 China School of Automotive Engineering Wuhan University of Technology Wuhan 430070 China Department of Electrical and Computer Engineering University of Houston Houston United States

Based on the theory of photonic crystal and its characteristic, which was known as photonic band gap (PBG), the finite-difference time-domain (FDTD) method, including absorbing boundary condition and periodic boundary condition, was used to simulate transmission characteristic of two-dimensional photonic crystal, which has periodic Al2O3 cylinder array structure. Incident wave was presumed as s-polarized wave. In two incident angles which were 0° and 30°, electric field, phase and transmissivity in the range of 1-30 μm were calculated. Numerical simulation results showed that PBG of photonic crystal existed in the range 12-18 μm (infrared waveband), and it may be used to develop infrared wave-guide.

关键词： FDTD Periodic structure Photonic band gap Photonic crystal

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EEG and MRI data fusion for early diagnosis of Alzheimer's disease

EEG and MRI data fusion for early diagnosis of Alzheimer's d...

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

作者： Tejash Patel Robi Polikar Christos Davatzikos Christopher M. Clark Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Engineering Department Rowan University Glassboro NJ USA Department of Radiology University of Pennsylvania Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promising to reduce the rate of progression. On the other hand, the efficacy of these new medications critically depends on our ability to diagnose AD at the earliest stage. Currently AD is diagnosed through longitudinal clinical evaluations, which are available only at specialized dementia clinics, hence beyond financial and geographic reach of most patients. Automated diagnosis tools that can be made available to community hospitals would therefore be very beneficial. To that end, we have previously shown that the event related potentials obtained from different scalp locations can be effectively used for early diagnosis of AD using an ensemble of classifiers based decision fusion approach. In this study, we expand our data fusion approach to include MRI based measures of regional brain atrophy. Our initial results indicate that ERPs and MRI carry complementary information, and the combination of these heterogeneous data sources using a decision fusion approach can significantly improve diagnostic accuracy.

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4D wavelet noise suppression of MR diffusion tensor data

4D wavelet noise suppression of MR diffusion tensor data

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hesamoddin Jahanian Azadeh Yazdan-Shahmorad Hamid Soltanian-Zadeh Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Department of Biomedical Engineering University of Michigan Ann Arbor MI USA Controll and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Diffusion tensor imaging (DTI) is known to be promising for providing anatomical information about white-matter fiber bundles that cannot be obtained by other non-invasive in vivo imaging methods. However, its application is limited because of its low signal-to-noise ratio and significant imaging artifacts. To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging 4D wavelet denoising technique is used to improve the signal to noise ratio (SNR) of diffusion tensor images. To evaluate the proposed method, a high SNR data set is built by repeating and averaging the data acquisition several times and is compared to the denoised data. Our results revealed that wavelets would effectively reduce the noise in DTI data with less blurring of tissue types, especially in the white matter. It would suggest that by using the 4D wavelet noise suppression, one could decrease the acquisition time and still have an acceptable SNR.

关键词： Tensile stress Diffusion tensor imaging Signal to noise ratio Noise reduction Magnetic noise Anisotropic magnetoresistance Eigenvalues and eigenfunctions In vivo Magnetic resonance imaging Wavelet analysis

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Fabrication and characterization of microwave immunosensors based on organic semiconductors with nanogold-labeled antibody

Fabrication and characterization of microwave immunosensors ...

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

作者： Feng Li David P. Klemer John K. Kimani Shun Mao Junhong Chen Douglas A. Steeber BARD Laboratory in the Department of Electrical Engineering and Computer Science University of Wisconsin Milwaukee Milwaukee WI USA Plasma Processing and Nanotechnology Group Department of Mechanical Engineering University of Wisconsin Milwaukee Milwaukee WI USA Department of Biological Sciences University of Wisconsin Milwaukee Milwaukee WI USA

Microelectronic biosensors hold great promise for rapid, sensitive and specific in vitro point-of-care immunodiagnostics. In particular, sensors fabricated using organic semiconductors have attractive advantages—such as ease of manufacture and low cost—in the design and implementation of such devices. Furthermore, immobilization of an antibody or protein antigen as a biorecognition element onto an organic semiconducting film allows for direct transduction of biomolecular binding events into an electronic signal which is readily measured and processed. In previous work, we have demonstrated that an antigen can be bound to organic semiconducting films while retaining enzymatic activity after immobilization. The present work considers organic semiconducting films which are spin-cast onto an interdigitated electrode; antibodies labeled with gold-nanoparticles are applied to the organic semiconducting film and serve as a biorecognition element. The sensor geometry includes a high-frequency coplanar waveguide contact metallization to facilitate direct measurement using microwave wafer probes. Equivalent circuit models are derived from microwave measurements over the frequency range 0.3 MHz to 8.5 GHz.

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