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Congress on Evolutionary Computation

作者： C.K. Oh G.J. Barlow US Naval Research Laboratory Washington D.C. DC USA Center for Robotics and Intelligent Machines Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Autonomous navigation controllers were developed for fixed wing unmanned aerial vehicle (UAV) applications using multiobjective genetic programming (GP). We designed four fitness functions derived from flight simulations and used multiobjective GP to evolve controllers able to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle closely around the emitter. Controllers were evolved for three different kinds of radars: stationary, continuously emitting radars, stationary, intermittently emitting radars, and mobile, continuously emitting radars. We selected realistic flight parameters and sensor inputs to aid in the transference of evolved controllers to physical UAVs.

关键词： Unmanned aerial vehicles Genetic programming Erbium Robot kinematics Robot control Automatic control Radar intelligent robots Mobile robots Navigation

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MRSI brain tumor characterization using wavelet and wavelet packets feature spaces and artificial neural networks

MRSI brain tumor characterization using wavelet and wavelet ...

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

作者： A. Yazdan-Shahmorad H. Soltanian-Zadeh R.A. Zoroofi Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique for assessing biochemical fingerprint of tissue composition. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of MRSI. There are several technical reasons that make the brain easier than other organs to be examined with MRSI. This work presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types (malignant glioma, astrocytoma, and oligodendroglioma). After extracting features from MRSI data using wavelet and wavelet packets, we use artificial neural networks to determine the abnormal spectra and the type of abnormality. We evaluated the proposed methods using clinical and simulated MRSI data and biopsy results. The MRSI analysis results were correct 97% of the time when classifying the spectra of the clinical MRSI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determining tumor types using the clinical and simulated MRSI data, respectively.

关键词： Wavelet packets Neoplasms Artificial neural networks Biopsy Magnetic resonance Spectroscopy Magnetic resonance imaging Fingerprint recognition Surgery Cancer

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Multiresolution automatic segmentation of T1-weighted brain MR images

Multiresolution automatic segmentation of T1-weighted brain ...

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IEEE International Symposium on Biomedical Imaging

作者： M. Zeydabadi R.A. Zoroofi H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.

关键词： Image resolution Image segmentation Biomedical imaging Multiresolution analysis Image analysis Magnetic analysis Algorithm design and analysis Brain modeling Magnetic liquids Magnetic resonance

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Using a non-prior training active feature model

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3333卷 69-78页

作者： Kim, Sangjin Kang, Jinyoung Shin, Jeongho Lee, Seongwon Paik, Joonki Kang, Sangkyu Abidi, Besma Abidi, Mongi Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University 221 Huksuk-Dong Tongjak-Ku Seoul 156-756 Korea Republic of Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Tennessee Knoxville TN 27996-2100 United States

This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time. © Springer-Verlag 2004.

关键词： Optical flows

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Activation detection in multi-subject studies of fMRI using GLRT

Activation detection in multi-subject studies of fMRI using ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： S.M. Shams G.A. Hossein-Zadeh H. Soltanian-Zadeh Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics University of Tehran School of Cognitive Sciences Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Institute for Studies in Theoretical Physics and Mathematics and Medical Image Analysis Laboratory Henry Ford Health System University of Tehran School of Cognitive Sciences Iran

A new method based on generalized likelihood ratio test (GLRT) for activation detection in multi-subject studies of functional MRI (fMRI) is proposed. In this method, we test the correlation between the fMRI time series of different subjects and the bases of a signal subspace which increases the flexibility of method in detecting different shapes of hemodynamic response. The proposed multivariate method can be applied to group studies where the conventional cross-correlation method cannot be used due to its univariate property. This method is applied to both experimental and simulated fMRI data and the results are compared to those of general linear model (GLM). We show that the proposed method detects more significant activated regions in analyzing experimental data and more true voxels in simulated data

关键词： Testing Hemodynamics Brain modeling Process control intelligent control Physics computing Mathematics Time series analysis Magnetic resonance imaging Data analysis

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OPTIMIZING THE SUBDIVISION OF ELEMENTS IN DISCONTINUITY MESHING FOR HIERARCHICAL RADIOSITY

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International Journal on Artificial Intelligence Tools 2003年第4期12卷 395-411页

作者： IOANNIS A. PANTAZOPOULOS SPYROS G. TZAFESTAS Intelligent Robotics and Automation Laboratory Signals Control and Robotics Division Electrical and Computer Engineering Department National Technical University of Athens Zographou Athens Greece

This paper presents a new topological structure for use in the context of hierarchical radiosity combined with discontinuity meshing. This is most useful for a new strategy adopted for subdividing the elements of a scene consisting of convex polygons. The subdivision is done in a local optimization manner keeping the aspect ratios of produced polygons low. The generated meshes give high visual accuracy.

关键词： Hierarchical radiosity discontinuity meshing mesh pre(post)-processing multigriding aspect-ratio

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Textual message read by a mobile robot

Textual message read by a mobile robot

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IEEE International Workshop on intelligent Robots and systems (IROS)

作者： D. Letourneau F. Michaud J.-M. Valin C. Proulx Department of Electrical Engineering and Computer Engineering Universite de Sherbrooke LABORIUS - Research Laboratory on Mobile Robotics and Intelligent Systems Sherbrooke Quebec CANADA

Giving the ability to read characters and symbols is highly desirable for increased autonomy of mobile robots operating in the real world. The idea is fairly simple: give a robot the ability to acquire an image of a message to read, extract the symbols and recognize them. Image character recognition research has been going on for decades now, with good results. But compared to conventional character recognition systems, the challenge with a mobile robot is to find a textual message to capture in the world and to get a good view of the message, knowing that the viewpoint of the robot depends on its position in relation to the message, which cannot be pre-specified. In this paper we present our approach making it possible for an autonomous mobile robot to read messages. We outline the constraints under which the approach works, and present results obtained using a Pioneer 2 robot equipped with a Pentium 233 MHz and a pan-tilt-zoom camera.

关键词： Mobile robots Robot vision systems Cameras intelligent robots Character recognition Image recognition Navigation Laboratories Mobile computing intelligent systems

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Implementation of a neural network based system for estimating the strength of a board using mixed signals

Implementation of a neural network based system for estimati...

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IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： H. Saboksayr A. Saravi P.D. Lawrence F. Lam IEEE Control System Society Vancouver Canada Robotics and Control Laboratoryratory Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Robotics and Control Laboratory Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Department of Wood Science University of British Columbia Vancouver Canada

An intelligent lumber grading system was developed to provide a new way for estimating the strength of a board by posing the estimation problem as an empirical learning problem. This system processed the X-ray image, extracted geometric features (of 1000 boards that eventually underwent destructive strength testing), and predicted the strength of the lumber by using a neural network. The X-ray image was passed through a threshold filter to separate the knots based on the fact that a denser knot produces a local maximum (as a rounded protrusion in an otherwise almost flat density surface) of the X-ray image. Each knot was modeled by a three-dimensional-cone with seven parameters. Information on all the detected knots such as volume, and knot-area-ratio were fed to a processor to generate 16 geometrical features (such as; the average of knot area ratio, and the number of knots detected in each board), which characterize each board. Then by using back-propagation as the training method, cross correlation as the measure of accuracy, and actual strength of a thousand boards as the empirical data set, a neural network was trained to estimate the strength of each board. The learning system consisted of three layers, with 1, 5, 16 neurons in output, hidden and input layer respectively. Ten-fold cross validation was used to produce an unbiased accuracy of the estimation problem. The learning and testing sets comprised of 900 and 100 boards respectively. By repeating the learning and testing for ten times and averaging the results, a coefficient of determination of 0.4059 was reached in this study for using X-ray images alone. The same methodology was applied to MOE (modulus of elasticity) and a coefficient of determination of 0.56 was reached. The results were improved by fusing the X-ray image and MOE using a learning system consisting of three layers, with 1, 5, 40 neurons in output, hidden and input layer respectively. Ten-Fold cross validation resulted in a coefficient o

关键词： Neural networks X-ray imaging Feature extraction Learning systems Neurons intelligent systems system testing Filters Character generation Elasticity

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Automatic face region tracking for highly accurate face recognition in unconstrained environments

Automatic face region tracking for highly accurate face reco...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Y.-O. Kim J. Paik Jingu Heo A. Koschan B. Abidi M. Abidi Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Image Processing Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Tennessee Knoxville USA

We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

关键词： Face recognition Surveillance Real time systems Humans Cameras intelligent systems Security Image quality Layout Low pass filters

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NeuroFAST: High accuracy neuro-fuzzy modeling

NeuroFAST: High accuracy neuro-fuzzy modeling

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IEEE International Conference on Artificial Intelligence systems, ICAIS 2002

作者： Tzafestas, S.G. Zikidis, K.C. Intelligent Robotics and Automation Laboratory Electrical and Computer Engineering Department National Technical University of Athens Zographou Athens15773 Greece

ISBN: (纸本)0769517331

Most fuzzy modeling algorithms rely either on simplistic (grid type) or off-line (trial-and-error type) structure identification methods. The proposed neurofuzzy modeling architecture, NeuroFAST, is an on-line, structure and parameter learning algorithm, featuring high function approximation accuracy. It is based on the first order Takagi-Sugeno-Kang (TSK) model (functional reasoning), where the consequence part of each fuzzy rule is a linear equation of the input variables. Fuzzy rules are allocated as learning evolves by a modified Fuzzy ART (Adaptive Resonance Theory) mechanism, assisted by fuzzy rule splitting and adding procedures (structure learning). The well known δ-rule continuously tunes learning weights on both premise and consequence parts (parameter identification). Tested on the Box-Jenkins gas furnace process modeling and the Mackey-Glass chaotic time series prediction, NeuroFAST yields very good results in terms of approximation accuracy, outperforming all known approaches. © 2002 IEEE.

关键词： Fuzzy rules

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