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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yaw-Jiunn Chiou Hsiang-Min Chen Jyh Wen Chai Clayton Chi-Chang Chen Yen-Chieh Ouyang Wu-Chung Su Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University and Hospital Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and technology Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image.

关键词： Brain image analysis Magnetic analysis Magnetic resonance Independent component analysis Magnetic resonance imaging Support vector machines signal analysis image resolution signal resolution

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Building hardware-based low-cost experimental DSP learning modules

Building hardware-based low-cost experimental DSP learning m...

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2008 ASEE Annual Conference and Exposition

作者： Uluagac, A. Williams, Douglas Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Department of Undergraduate Affairs Center for Signal and Image Processing Arbutus Center for Distributed Engineering Education

Students often face difficulties in grasping and understanding fundamental digital signal processing (DSP) concepts in the classroom environment. Most DSP courses do not have labs associated with them, and the ones that do typically depend on the usage of text-based programming languages (e.g., MATLAB) that do not allow real-time external hardware interaction. Although the availability of external DSP platforms (e.g. National Instruments Speedy-33, Texas Instruments DSP Student Kits, etc.) for educational purposes has increased because of recent advances in embedded processors and sensor technology, they are rarely used in early DSP courses because of the programming sophistication needed for real-time processing. In this paper we discuss the development of low-cost experimental DSP learning modules for classes that do not usually have a lab component. These modules use National Instruments' Lab VIEW for their programming and development platform with the Speedy-33 DSP board and LEGO Mindstorms NXT Brick as the hardware platforms. Many of the modules can also be run completely on the host computer's sound card. Modules have been developed for examining different aspects of topics such as sampling, aliasing, and filtering, while working with data that has been captured and processed in real-time. Students are able to interact with the hardware and data through GUIs, thus obviating the need to first develop real-time programming skills. This paper will describe these modules and how they are designed to be used both in lectures and as part of homework assignments. © American Society for engineering Education, 2008.

关键词： Digital signal processing

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A program for distributed laboratories in the ECE CURRICULUM

A program for distributed laboratories in the ECE CURRICULUM

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2008 ASEE Annual Conference and Exposition

作者： Ferri, Bonnie Auerbach, Jill Jackson, Joel Michaels, Jennifer Williams, Douglas Georgia Tech. Graduate Affairs in ECE Georgia Tech. School of Electrical and Computer Engineering Georgia Institute of Technology Center for Signal and Image Processing School of ECE Georgia Tech. Georgia Institute of Technology Faculty of the School of Electrical and Computer Engineering Georgia Tech.

This paper describes a project that seeks to improve undergraduate learning by developing a cohesive program where experiments are introduced into a wide selection of ECE courses that currently do not have labs. Most of the experiments are low cost and portable, which facilitates a decentralized laboratory environment where students perform the experiments at their homes or in the classroom rather than in dedicated laboratories. We will assess our work through the involvement of twelve faculty members teaching ten courses, some of which are offered in distance learning settings. © American Society for engineering Education, 2008.

关键词： Laboratories

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Distributed initialization of sensor networks with communication and computation trees

Distributed initialization of sensor networks with communica...

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

作者： Milind Borkar James H. McClellan Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

When compared to the tracking problem in which prior knowledge is available, generating the initial distribution for the state vector of a phenomenon of interest, with no prior knowledge of the desired state, is a challenging problem. In this paper, the authors develop a fully distributed initialization algorithm that fuses data in heterogeneous sensor networks using communication trees. Monte Carlo methods are used to fuse the collected data and to represent the desired state vector distribution. The presented algorithm utilizes an importance function that is additive in the local node posterior distributions, providing a robust alternative to belief propagation methods in which particles are generated according to the product of local node posteriors.

关键词： computer networks Distributed computing State estimation Sensor fusion Belief propagation Fuses image sensors signal processing image processing Knowledge engineering

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A Reusable Distributed Arithmetic Architecture for FIR Filtering

A Reusable Distributed Arithmetic Architecture for FIR Filte...

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51st Midwest Symposium on Circuits and Systems (MWSCAS 2008), vol.1

作者： Haw-Jing Lo Heejong Yoo David V. Anderson Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

This paper presents a new hardware efficient distributed arithmetic (DA) architecture for high order (> 1024) digital filters. The new architecture is termed reusable distributed arithmetic (RDA). The proposed architecture has a linear dependence of memory size on filter length versus the exponential dependence found in lookup table (LUT)-based designs by removing the LUT and generating the required combinations online. In addition, the proposed RDA architecture reuses the computation blocks much like the way multipliers are reused in multiplier-based architectures to reduce hardware complexity. The proposed RDA design is compared against a multiplier-based (MM) design to illustrate the area dependency of both designs on filter length. FPGA synthesis results confirm that the RDA design is capable of much higher order filters (2048 tap) than the MM design (512 tap) while at the same time having similar equivalent gate counts and throughput.

关键词： Finite impulse response filter Field programmable gate arrays Hardware Logic gates Digital filters Transistors computer architecture

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Blindly Separating Mixtures of Multiple Layers with Spatial Shifts

Blindly Separating Mixtures of Multiple Layers with Spatial ...

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26th IEEE Conference on computer Vision and Pattern Recognition (CVPR 2008), vol.1

作者： Kun Gai Zhenwei Shi Changshui Zhang Visiting Researcher School of Electrical Electronic and Computer Engineering Tsinghua University Beijing China Image Processing Center School of Astronautics Beijing Aeronautics and Astronautics University Beijing China

We address the problem of blindly separating mixtures of multiple layer images with unknown spatial shifts and mixing coefficients. Our proposed method can handle the over-determined, determined and under-determined cases where mixtures are more than, as many as and fewer than layers, respectively. The method is fast in over-determined and determined cases, with the same complexity as the fast Fourier transform (FFT), and can separate more layers from fewer mixtures in the under-determined case. It consists of two main steps. First, a novel sparse blind separation algorithm is applied, to estimate the spatial shifts, the mixing coefficients and the edge image of each layer. Second, all layers are reconstructed, by large scale linear programming in the under-determined case, or by least-squares solutions in other cases. The effectiveness of this technology is shown in the experiments on two simulated mixtures of four layers with spatial shifts, real mixture photos containing transparency and reflections, and real mixture images in a dissolve from a video.

关键词： Windows Layout Laboratories Space technology Fast Fourier transforms image reconstruction Linear programming Optical reflection Intelligent systems Information science

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Iris Feature extraction Discrete cosine transforms image recognition Databases Training Humans

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Constrained optimization of nonparametric entropy-based segmentation of brain structures

Constrained optimization of nonparametric entropy-based segm...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

关键词： Constraint optimization Brain Iterative algorithms image segmentation Shape Couplings Magnetic resonance Magnetic resonance imaging Principal component analysis Buildings

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Towards an inclusive computational model of visual cortex

Towards an inclusive computational model of visual cortex

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： M. Gheiratmand H. Soltanian-Zadeh H. Khaloozadeh Electrical Engineering Department K.N. Toosi University of Technology Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA School of Cognitive Sciences Institute for Studies Theoretical Physics and Mathematics (IPM) Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

Understanding the primatespsila visual system has been one of the challenging problems of different groups of scientists for years. Though many studies, from physiology and neuroscience to computer vision, are done on different aspects of visual processing in the cortex, a comprehensive computational model of visual cortex is still missing. We have implemented a computational model of object recognition in ventral visual pathway in our previous work. This hierarchical model covers visual areas V1/V2, V4/PIT, and AIT sending inputs to the Prefrontal Cortex (PFC) for categorization. To extend our model, in this work, we have added a simple model of motion detection in neurons of areas V1 and MT of the dorsal stream to our previous model. This has enabled the model to perform another principal function of the visual cortex, i.e., motion perception.

关键词： Brain modeling Computational modeling Neurons Biological system modeling Object recognition Motion analysis Solid modeling Visual system computer vision Motion detection

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Face verification algorithm based on combination of modular 2DPCA and CSLDA

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2008年第6期45卷 1029-1035页

作者： Yuan, Ning Wu, Xiaojun Wang, Shitong Yang, Jingyu Kittler, Josef School of Information Engineering Jiangnan University Wuxi 214122 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing 210094 China Center for Vision Speech and Signal Processing Department of Electrical Engineering University of Surrey Surrey GU2 7XH United Kingdom

An improved face verification algorithm is proposed based on the combination of modular 2DPCA and CSLDA in this paper. Feature extraction of client specific linear discriminant analysis (CSLDA) transforms an image matrix to a vector which causes great dimensionality and computational complexity. Furthermore, the local feature is not considered in CSLDA. Then the new method is studied to avoid the deficiency. The initial features are extracted with the original images which are divided into modular sub-images. The 2DPCA is performed to get the low dimensional features which can be computed conveniently. The local features are extracted efficiently using the proposed new method. Then CSLDA is utilized on the new pattern which is obtained through the modular 2DPCA to extract the final features. Compared with PCA, the discriminant information obtained from the between-class scatter matrix and within-class scatter matrix are included using CSLDA. Moreover, client specific subspace could describe the diversity of the different face better and has more robust discriminant information than the traditional LDA. The experimental results obtained on the facial database XM2VTS using the Lausanne protocol and the facial database ORL using the user-defined protocol show that the verification performance of the new method is superior to that of the primary method CSLDA.

关键词： Feature extraction

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