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International Conference on image analysis and Processing

作者： Jari Hannuksela Pekka Sangi Janne Heikkila Xu Liu David Doermann Machine Vision Group Infortech Oulu Department of Electrical and Information Engineering University of Oulu Finland Language and Media Processing Laboratory Institute for Advanced Computer Studies University of Maryland College Park USA

This paper presents a novel user interaction concept for document image scanning with mobile phones. A high resolution mosaic image is constructed in two main stages. Firstly, online camera motion estimation is applied to the phone to assist the user to capture small image patches of the document page. Automatic image stitching process with the help of estimated device motion is carried out to reconstruct the full view of the document. Experiments on document images captured and processed with mosaicing software clearly show the feasibility of the approach.

关键词： Mobile handsets Motion estimation Cameras image resolution Mice Optical character recognition software Solid modeling Motion measurement image motion analysis Machine vision

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NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL image SEGMENTATION

NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAG...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhoundi-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

ISBN: (纸本)9781424406715;1424406714

We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.

关键词： Biomedical imaging image segmentation Shape Couplings Brain Magnetic resonance Magnetic resonance imaging Principal component analysis Probability density function Iterative algorithms

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A Probabilistic Framework for Content-Based Diagnosis of Retinal Disease

A Probabilistic Framework for Content-Based Diagnosis of Ret...

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

作者： Kenneth W. Tobin Mohamed Abdelrahman Edward Chaum V. Priya Govindasamy Thomas P. Karnowski Image Science and Machine Vision Group at the Oak Ridge National Laboratory Oak Ridge Tennessee USA Electrical and Computer Engineering Dept. Tennessee Tech University Cookeville Tennessee USA Retinal Diseases at the University of Tennessee Health Science Center Hamilton Eye Institute Memphis TN USA

Diabetic retinopathy is the leading cause of blindness in the working age population in the industrialized world. computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is leading to the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states.

关键词： Retina image retrieval Degenerative diseases Laboratories image analysis Pathology Content based retrieval Cameras Blindness Machine vision

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LANDING AN UNMANNED AIR VEHICLE: vision BASED MOTION ESTIMATION AND NONLINEAR CONTROL

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Asian Journal of Control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Designing an embedded video processing camera using a 16-bit microprocessor for a surveillance system

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Journal of VLSI Signal Processing Systems for Signal, image, and Video Technology 2006年第1期42卷 57-68页

作者： Sato, Koichi Evans, Brian L. Aggarwal, J.K. Department of Electrical and Computer Engineering Computer and Vision Research Center University of Texas at Austin Austin TX 78712 United States Department of Electrical and Computer Engineering Embedded Signal Processing Laboratory University of Texas at Austin Austin TX 78712 United States Department of Electrical and Computer Engineering University of Texas Austin Design and Implementation of Signal Processing Systems Technical Committee IEEE Signal Processing Society IEEE Department of Electrical and Computer Engineering Computer and Vision Research Center IEEE Computer Society

This paper describes the design and implementation of a hybrid intelligent surveillance system that consists of an embedded system and a personal computer (PC)-based system. The embedded system performs some of the image processing tasks and sends the processed data to the PC. The PC tracks persons and recognizes two-person interactions by using a grayscale side view image sequence captured by a stationary camera. Based on our previous research, we explored the optimum division of tasks between the embedded system and the PC, simulated the embedded system using dataflow models in Ptolemy, and prototyped the embedded system in real-time hardware and software using a 16-bit CISC microprocessor. This embedded system processes one 320 × 240 frame in 89 ms, which yields one-third of the rate of 30 Hz video system. In addition, the real-time embedded system prototype uses 5.7 K bytes of program memory, 854 K bytes of internal data memory and 2 M bytes external DRAM. © Springer Science + Business Media, Inc. 2006.

关键词： Microprocessor chips

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Characterization of anomaly detection in hyperspectral imagery

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Sensor Review 2006年第2期26卷 137-146页

作者： Chang, Chein-I Hsueh, Mingkai Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD United States

Purpose - The paper aims to characterize anomaly detection in hyperspectral imagery. Design/methodology/approach - This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image-based computer simulations and real image experiments to substantiate the work proposed in this paper. Findings - This paper developed an ACAD and custom-designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are - first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation. Originality/value - This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image-based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

关键词： image processing

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Range image registration with edge detection in spherical coordinates

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International Workshop on Multimedia Content Representation, Classification and Security, MRCS 2006

作者： Sertel, Olcay Ünsalan, Cem Computer Vision Research Laboratory Department of Computer Engineering Yeditepe University Istanbul 34755 Turkey Computer Vision Research Laboratory Department of Electrical and Electronics Engineering Yeditepe University Istanbul 34755 Turkey

ISBN: (纸本)3540393927

In this study, we focus on model reconstruction for 3D objects using range images. We propose a crude range image alignment method to overcome the initial estimation problem of the iterative closest point (ICP) algorithm using edge points of range images. Different from previous edge detection methods, we first obtain a function representation of the range image in spherical coordinates. This representation allows detecting smooth edges on the object surface easily by a zero crossing edge detector. We use ICP on these edges to align patches in a crude manner. Then, we apply ICP to the whole point set and obtain the final alignment. This dual operation is performed extremely fast compared to directly aligning the point sets. We also obtain the edges of the 3D object model while registering it. These edge points may be of use in 3D object recognition and classification. © Springer-Verlag Berlin Heidelberg 2006.

关键词： image reconstruction

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Performance modeling and prediction of face recognition systems

Performance modeling and prediction of face recognition syst...

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2006 IEEE computer Society Conference on computer vision and Pattern Recognition, CVPR 2006

作者： Peng, Wang Qiang, Ji Section of Biomedical Image Analysis Department of Radiology University of Pennsylvania Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute

ISBN: (纸本)0769525970

It is a challenging task to accurately model the performance of a face recognition system, and to predict its individual recognition results under various environments. This paper presents generic methods to model and predict the face recognition performance based on analysis of similarity measurement. We first introduce a concept of "perfect recognition", which only depends on the intrinsic structure of a recognition system. A metric extracted from perfect recognition similarity scores (PRSS) allows modeling the face recognition performance without empirical testing. This paper also presents an EM algorithm to predict the recognition rate of a query set. Furthermore, features are extracted from similarity scores to predict recognition results of individual queries. The presented methods can select algorithm parameters offline, predict recognition performance online, and adjust face alignment online for better recognition. Experimental results show that the performance of recognition systems can be greatly improved using presented methods. © 2006 IEEE.

关键词： Face recognition

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A Progressive Framework for Two-Way Clustering Using Adaptive Subspace Iteration for Functionally Classifying Genes

A Progressive Framework for Two-Way Clustering Using Adaptiv...

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International Joint Conference on Neural Networks (IJCNN)

作者： J.S. Shaik M. Yeasin Computer Vision Pattern and Image Analysis Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

This paper presents an adaptive subspace based two-way clustering of microarray data. To analyze the data at various scales a "Progressive" framework is introduced. The goals are to functionally classify genes and also to find differentially expressed genes in microarray expression profiles. Empirical analysis on Colon Cancer dataset shows that ASI performs favorably in grouping genes with similar functions and finding genes that may have been involved in the formation of colon cancer. It was also observed that the proposed algorithm is robust against ordering of samples and yield results consistent with ground truth information.

关键词： Clustering algorithms Cancer Colon Data analysis Robustness Biological tissues Proteins computer vision image analysis Performance analysis

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A flexible runtime system for image processing in a distributed computational environment for an unmanned aerial vehicle

A flexible runtime system for image processing in a distribu...

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作者： Nordberg, Klas Doherty, Patrick Forssén, Per-Erik Wiklund, Johan Andersson, Per Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden Knowledge Processing Laboratory Department of Computer Science Linköping University Sweden Department of Computer Science Lund University Sweden

A runtime system for implementation of image processing operations is presented. It is designed for working in a flexible and distributed environment related to the software architecture of a newly developed UAV system. The software architecture can be characterized at a coarse scale as a layered system, with a deliberative layer at the top, a reactive layer in the middle, and a processing layer at the bottom. At a finer scale each of the three levels is decomposed into sets of modules which communicate using CORBA, allowing system development and deployment on the UAV to be made in a highly flexible way. image processing takes place in a dedicated module located in the process layer, and is the main focus of the paper. This module has been designed as a runtime system for data flow graphs, allowing various processing operations to be created online and on demand by the higher levels of the system. The runtime system is implemented in Java, which allows development and deployment to be made on a wide range of hardware/software configurations. Optimizations for particular hardware platforms have been made using Java's native interface. © World Scientific Publishing Company.

关键词： image processing

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