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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Hsian-Min Chen Shih-Yu Chen Jyh Wen Chai Clayton Chi-Chang Chen Chao-Cheng Wu Yen-Chieh Ouyang Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University 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 County Baltimore MD USA

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions.

关键词： Classification algorithms Training Image segmentation Brain Skull Software algorithms Support vector machines

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Volume-based Magnetic Resonance Brain Image Classification

Volume-based Magnetic Resonance Brain Image Classification

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yaw-Jiunn Chiou Jyh-Wen Chai Clayton Chi-Chang Chen Shih-Yu Chen Hsian-Min Chen Yen-Chieh Ouyang Wu-Chung Su Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University 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

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices.

关键词： Training Support vector machine classification Image classification Vectors Volume measurement Magnetic resonance

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Surveillance of pedestrian bridge traffic using neural networks

Surveillance of pedestrian bridge traffic using neural netwo...

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Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009

作者： Watkins, Steve E. Stanley, R. Joe Gopal, Anand Moss, Randy H. Department of Electrical and Computer Engineering Applied Optics Laboratory Missouri University of Science and Technology Rolla MO 65409 United States Department of Electrical and Computer Engineering Signal Processing Laboratory Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9780819475527

A computer-vision monitoring system is demonstrated that automatically detects the presence and location of people. The approach investigated the potential for real-time, automated surveillance and tracking in a realistic environment. Economy was obtained by the use of gray-scale, fixed perspective images and efficiency was obtained by the use of selected object features and a neural-network-processing algorithm. The system was applied to pedestrian traffic on an outdoor bridge and consequently had to handle complex images. Image sequences of single and multiple people were used with differences in clothing, position, lighting, season, etc. A two-stage algorithm was implemented in which (1) new objects were identified in a highly variable scene and (2) the objects were classified with a back-propagation neural network. The image processing techniques included segmentation and filtering and the neural network used fourteen object features as inputs. The implementation had excellent people-discrimination accuracy despite the noise in the images and had low computational complexity with respect to alternative techniques. © 2009 SPIE.

关键词： Neural networks

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A robust face detection method using intrinsic images and an energy minimization technique under the near-IR light spectrum

A robust face detection method using intrinsic images and an...

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2009 International Conference on Image processing, computer Vision, and Pattern Recognition, IPCV 2009

作者： Lahlou, Mouncef Adjouadi, Malek Barreto, Armando Center for Advanced Technology and Education Department of Electrical and Computer Engineering Florida International University Miami FL United States Digital Signal Processing Laboratory Department of Electrical and Computer Engineering Florida International University Miami FL United States

ISBN: (纸本)9781601321190

Detecting faces accurately under natural lighting conditions remains a challenging task for advancing the performance of face recognition systems. Most face detection algorithms are associated with visible spectrum imagery which compromises their efficiency when image acquisition is subject to variable illumination. In this study, we propose a simple yet powerful solution that uses multiple cues to detect and locate faces accurately in near infrared (NIR) imaging systems. Specific objectives include (I) extract optimally the illumination and reflectance components of the original image using an intrinsic images decomposition technique, (2) modify the segmentation process by using an energy minimization procedure in an adaptive fashion, and (3) propose an enhanced face detection process on the basis of pupil localization. The evaluation of our face detector on image sequences of 45 different subjects from the CBSR NIR face dataset demonstrated a highly competitive accuracy with less than 5% error rate.

关键词： Face recognition

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Applying fuzzy DLs in the extraction of image semantics

Applying fuzzy DLs in the extraction of image semantics

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作者： Dasiopoulou, Stamatia Kompatsiaris, Ioannis Strintzis, Michael G. Centre for Research and Technology Hellas Informatics and Telematics Institute Thessaloniki Greece Information Processing Laboratory Electrical and Computer Engineering Department Aristotle University of Thessaloniki Greece

ISBN: (纸本)3642105610

Statistical learning approaches, bounded mainly to knowledge related to perceptual manifestations of semantics, fall short to adequately utilise the meaning and logical connotations pertaining to the extracted image semantics. Instigated by the Semantic Web, ontologies have appealed to a significant share of synergistic approaches towards the combined use of statistical learning and explicit semantics. While the relevant literature tends to disregard the uncertainty involved, and treats the extracted image descriptions as coherent, two valued propositions, this paper explores reasoning under uncertainty towards a more accurate and pragmatic handling of the underlying semantics. Using fuzzy DLs, the proposed reasoning framework captures the vagueness of the extracted image descriptions and accomplishes their semantic interpretation, while resolving inconsistencies rising from contradictory descriptions. To evaluate the proposed reasoning framework, an experimental implementation using the fuzzyDL Description Logic reasoner has been carried out. Experiments in the domain of outdoor images illustrate the added value, while outlining challenges to be further addressed. © 2009 Springer-Verlag.

关键词： Data description

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A theoretical model for spontaneous seizure generation based on Markov chain process

A theoretical model for spontaneous seizure generation based...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Farzaneh Shayegh Saeed Sadri Rasoul AmirFattahi Digital signal Processing Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Here a theoretical model for seizure generation is presented. This is done according to a simple physiological model of epileptic activities in which seizure occurrence is mainly attributed to imbalance of excitatory/inhibitory ratio. Although factors that play a role in ictogenesis are not exactly known, this process can be assumed as a stochastic process. Indeed, we claim that in neural synapses both excitatory and inhibitory gains change randomly. We propose that their values theoretically change as a tri-variant 2 nd -order Markov chain process. States of Markov chain must be selected such that any sudden change is avoided. Important result of this modeling is that epileptic behavior of a cortical area may appear in various manners not in a unique one. Thus different observations in seizure onset all are validated and it is proved that they do not contradict each other.

关键词： Brain modeling Epilepsy Electroencephalography Neurons Signal generators Stochastic processes Neural engineering Digital signal processing Electronic mail Synchronous generators

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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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Comparison between noise reduction algorithms for speech signals using auditory masking effects

Comparison between noise reduction algorithms for speech sig...

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IEEE GCC Conference & Exhibition

作者： Mehdi Yektaeian Rassoul Amirfattahi Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Auditory masking is aggressively exploited by algorithms used for the lossy compression of audio signals. In compression of audio signals, the intent is to hide the noise introduced by the coding below the masking threshold, thus making the noise inaudible. This will render the coding process transparent, enabling better compression without audible degradation of the signal. In this article, we show that using masking properties of the hearing system allows for improved noise reduction. A novel method for noise reduction in speech signals is proposed. This method is shown to outperform non-auditory based methods, and compares well with other perceptually motivated noise reduction methods. It is found that the proposed method, Soulodre's PNRF combined with the ITU's PEAQ auditory model, have more musical noise but less signal distortion than the method proposed by Tsoukalas, which obtain marginally better results in informal testing results.

关键词： Signal to noise ratio Noise reduction Speech Speech enhancement Masking threshold

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Noise reduction algorithm for robust speech recognition using MLP neural network

Noise reduction algorithm for robust speech recognition usin...

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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA

作者： Masoumeh P. Ghaemmaghami Farbod Razzazi Hossein Sameti Saeed Dabbaghchian Bagher BabaAli Department of Electrical Engineering Science & Research Branch Azad University Tehran Iran Speech Processing Laboratory Department of Computer Engineering Sharif University of Technology Iran Department of Computer Engineering Speech Processing Lab Tehran Iran

We propose an efficient and effective nonlinear feature domain noise suppression algorithm, motivated by the minimum mean square error (MMSE) optimization criterion. Multi layer perceptron (MLP) neural network in the log spectral domain minimizes the difference between noisy and clean speech. By using this method as a pre-processing stage of a speech recognition system, the recognition rate in noisy environments is improved. We can extend the application of the system to different environments with different noises without re-training it. We need only to train the preprocessing stage with a small portion of noisy data which is created by artificially adding different types of noises from the NOISEX-92 database to the TIMIT speech database. Experimental results show that the proposed method can achieve significant improvement of recognition rates.

关键词： Noise reduction Noise robustness Speech recognition Neural networks Working environment noise Speech enhancement Feature extraction Application software Speech processing Databases

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Signal processing for neural spike trains

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Computational intelligence and neuroscience 2010年第1期2010卷 698751页

作者： Theodore W Berger Zhe Sage Chen Andrzej Cichocki Karim G Oweiss Rodrigo Quian Quiroga Nitish V Thakor Department of Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA 90089 USA Neuroscience Statistics Research Laboratory Massachusetts Institute of Technology Cambridge MA 02139 USA Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Wako-shi Saitama 351-0198 Japan Department of Electrical and Computer Engineering Michigan State University MI 48824 USA Department of Engineering University of Leicester Leicester LE1 7RH UK School of Medicine Johns Hopkins University Baltimore MD 21205 USA

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