Download or read book Wavelet Based Bayesian Methods for Image Analysis and Automatic Target Recognition written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work investigates the use or Bayesian multiscale techniques for image analysis and automatic target recognition. We have developed two new techniques. First, we have develop a wavelet-based approach to image restoration and deconvolution problems using Bayesian image models and an alternating-maximation method. Second, we have developed a wavelet-based framework for target modeling and recognition that we call TEMPLAR (TEMPlate Learning from Atomic Representations) . TEMPLAR is can he used to automatically extract low-dimensional wavelet representations (or templates) or target objects from observation data, providing robust and computationally efficient target classifiers. On a more theoretical level, we have developed a framework for multiresolution analysis or likelihood functions, which extends wavelet-like analysis to a wide class or non-Gaussian processes. In another line of investigation, we are exploring a new imaging application known as network tomography. The goal of this work is to characterize the internal performance of communication networks based only on external measurements at the edge (sources and receivers) of the network. In the coming year, we plan to focus on four key research areas. First, we will develop theoretical hounds on the performance of multiscale/wavelet estimators in non-Gaussian environments including Poisson imaging applications. Second, we will study the use of complex wavelets in image restoration and target recognition problems. Third, we will develop automatic methods for segmenting imagery (SAR, FLIR, LADAR) based on complexity-regularization methods. Fourth, we will continue to develop a unified framework for communication network tomography and investigate new tools for network performance visualization.

Download or read book Wavelet Based Signal and Image Processing for Target Recognition written by and published by . This book was released on 2002 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the initial year of the project new wavelet based signal and image processing algorithms were developed, specifically directed towards usefulness in target recognition applications. Classical spatial and frequency domain image processing algorithms were generalized to process discrete wavelet transform (DWT) data. Results include adaptation of classical filtering, smoothing and interpolation techniques to DWT. From 2003 the research direction changed, in keeping with changes in the direction of ONR's Probability and Statistics Program. A sabbatical leave was devoted to broadening the P.I.'s expertise in aspects of Pattern Recognition. Research was also done on-site at the Naval Surface Warfare Center, Dahlgren, Virginia, where collaborations were formed with NSWC scientists. These resulted, inter alia, in the development of a new tracking algorithm for laser guided weapons. While at NSWC, the P.I. presented tutorial courses and seminars to NSWC scientists. The grant supported 4 graduate students who performed software development and theoretical derivations. During the grant period, 8 peer-reviewed papers were published.

Download or read book Automatic Target Recognition written by and published by . This book was released on 2007 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Wavelets in Signal and Image Analysis written by A.A. Petrosian and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite their novelty, wavelets have a tremendous impact on a number of modern scientific disciplines, particularly on signal and image analysis. Because of their powerful underlying mathematical theory, they offer exciting opportunities for the design of new multi-resolution processing algorithms and effective pattern recognition systems. This book provides a much-needed overview of current trends in the practical application of wavelet theory. It combines cutting edge research in the rapidly developing wavelet theory with ideas from practical signal and image analysis fields. Subjects dealt with include balanced discussions on wavelet theory and its specific application in diverse fields, ranging from data compression to seismic equipment. In addition, the book offers insights into recent advances in emerging topics such as double density DWT, multiscale Bayesian estimation, symmetry and locality in image representation, and image fusion. Audience: This volume will be of interest to graduate students and researchers whose work involves acoustics, speech, signal and image processing, approximations and expansions, Fourier analysis, and medical imaging.

Download or read book Bayesian Inference in Wavelet Based Models written by Peter Müller and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional wavelet decomposition in spatial modeling. Chapters in Part VI discuss the use of empirical Bayes estimation in wavelet based models. Part VII concludes the volume with a discussion of case studies using wavelet based Bayesian approaches. The cooperation of all contributors in the timely preparation of their manuscripts is greatly recognized. We decided early on that it was impor tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support.

Download or read book Automatic Target Detection And Recognition A Wavelet Based Approach written by and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wavelet based target detection and identification algorithms for radar applications are presented and tested and evaluated on computer simulated data. The algorithms make use of a scale sequential and/or scale recursive paradigm where computations are performed within and across scales in a multiresolution analysis (MRA) of the sensor data relative to a compactly supported discrete orthonormal wavelet basis. It is argued that such procedures are computationally efficient and offer promise of yielding near optimal performance with a minimum CPU time burden. Specific applications considered in the report include automatic target identification from high range resolution radar (HRR), target detection in the presence of fractal noise and the integration of multisensor data in the tracking of aircraft. Other applications addressed include scale recursive optimal filtering and the synthesis of parallel architectures for the 1-D discrete wavelet transform. The report includes a full discussion of the theory behind the various detection and identification algorithms plus results from Monte Carlo simulations.

Download or read book Automatic Target Recognition Using Wavelet Based Vector Quantization written by Lipchen Chan and published by . This book was released on 1997 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: An automatic target recognition classifier is described that uses a set of dedicated vector quantizers (VQs) in the wavelet domain. The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition is used to split this region into several subbands. A dedicated VQ codebook is then generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. Finally, a path selector was designed to speed up the recognition process at the expense of a tolerable degradation in the recognition rate.

Download or read book Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing written by Jean-Francois Giovannelli and published by John Wiley & Sons. This book was released on 2015-02-16 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is on "ill-posed inverse problems". These problems cannot be solved only on the basis of observed data. The building of solutions involves the recognition of other pieces of a priori information. These solutions are then specific to the pieces of information taken into account. Clarifying and taking these pieces of information into account is necessary for grasping the domain of validity and the field of application for the solutions built. For too long, the interest in these problems has remained very limited in the signal-image community. However, the community has since recognized that these matters are more interesting and they have become the subject of much greater enthusiasm. From the application field’s point of view, a significant part of the book is devoted to conventional subjects in the field of inversion: biological and medical imaging, astronomy, non-destructive evaluation, processing of video sequences, target tracking, sensor networks and digital communications. The variety of chapters is also clear, when we examine the acquisition modalities at stake: conventional modalities, such as tomography and NMR, visible or infrared optical imaging, or more recent modalities such as atomic force imaging and polarized light imaging.

Download or read book Medical Image Analysis Methods written by Lena Costaridou and published by CRC Press. This book was released on 2005-07-13 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: To successfully detect and diagnose disease, it is vital for medical diagnosticians to properly apply the latest medical imaging technologies. It is a worrisome reality that due to either the nature or volume of some of the images provided, early or obscured signs of disease can go undetected or be misdiagnosed. To combat these inaccuracies, diagno

Download or read book Wavelet Based Feature Extraction for Target Recognition and Minefield Detection written by and published by . This book was released on 2002 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project produced advances in the theory of wavelets and two-channel filter banks, and the development of new algorithms for the generation of wavelet filters and the wavelet based processing of image data, with a view towards their usefulness in image analysis for target recognition. These results include implementation of simulated annealing and Discrete Wavelet Transform algorithms, derivation of parameterizations for various useful spaces of wavelets, derivation of expressions for frequency and spatial uncertainty in wavelets, generation of wavelets optimized for different balances between spatial and frequency uncertainties, and development of wavelet transform domain denoising algorithms for feature detection algorithms. Much of the research was done on-site at the Naval Surface Warfare Center, Dahlgren, VA. Several collaborations were formed with NSWC scientists, and these produced accomplishments in addition to those in the grant proposal. Also, the P.I. presented tutorial courses and seminars to NSWC personnel. Some of the research was performed during visits to universities in South Africa, resulting in further useful and on-going collaborations. The grant supported a total of 6 graduate students (one Doctoral and 5 Masters) who performed software development and some theoretical derivations. During the period of the grant, 13 peer-reviewed papers were published (3 in journals and 10 at conferences).

Download or read book Acquisition of Equipment for Research in Bayesian Automated Target Recognition written by and published by . This book was released on 2001 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research reported here involves Bayesian ATR: development of statistical models and algorithms for statistical inference. The specific items that were covered are: (1) Statistical Models for Thermal Variation in Prediction of IR Images: We are interested in statistical tools for predicting IR images of a known target in a new, previously unobserved thermal state. This prediction is based on a partial observation of the new state and the database of previous observation. We have developed a linear regression framework for estimating the temperature field, associated with the new state, and using it for predicting IR images from arbitrary perspectives. (2) Statistical Models for Clutter: We have derived analytical forms, called Bessel forms, to model the marginal densities of the filtered images, filtered using a set of Gabor filters. These analytical forms are easy to compute and match well with the observed histograms. In addition, a closed-form expression for the L(sup A)2 metric, on the space of these densities, provides a measure of closeness between natural images, with applications in clutter classification. (3) Nonlinear filtering for Tracking of Manifold-Valued Parameters: In ATR and other signal/image processing applications, we are often interested in tracking parameters that are constrained to be manifold-valued. We have applied a sequential Monte Carlo algorithm to solve the nonlinear, non-Euclidean filtering problem on these manifolds. (4) Asymptotic Performance Analysis: Bayesian ATR corresponds to selection of hypothesis in the presence of nuisance variables. Using Laplace's approximation to integrate out the nuisance variables (pose, location, motion etc.), we have derived analytical forms for the probabilities of error in ATR. Additionally, we have quantified the relation between nuisance estimation errors and the ATR performance.

Download or read book Energy Minimization Methods in Computer Vision and Pattern Recognition written by Edwin R. Hancock and published by Springer. This book was released on 2003-07-31 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'99, held in York, UK in July 1999. The book presents 11 revised full papers together with 11 papers presented at the meeting as posters. Those papers were selected from a total of 33 submissions. The book is divided in sections on shape, minimum description length, Markov random fields, contours, search and consistent labeling, tracking and video, and biomedical applications.

Download or read book Automatic Target Recognition written by and published by . This book was released on 2003 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Target Recognition in Hyperspectral Images Using Wavelet Decomposition and Neural Ldentifier written by 蘇鴻昇 and published by . This book was released on 2001 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Image Processing and Analysis written by Tony F. Chan and published by SIAM. This book was released on 2005-09-01 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops the mathematical foundation of modern image processing and low-level computer vision, bridging contemporary mathematics with state-of-the-art methodologies in modern image processing, whilst organizing contemporary literature into a coherent and logical structure. The authors have integrated the diversity of modern image processing approaches by revealing the few common threads that connect them to Fourier and spectral analysis, the machinery that image processing has been traditionally built on. The text is systematic and well organized: the geometric, functional, and atomic structures of images are investigated, before moving to a rigorous development and analysis of several image processors. The book is comprehensive and integrative, covering the four most powerful classes of mathematical tools in contemporary image analysis and processing while exploring their intrinsic connections and integration. The material is balanced in theory and computation, following a solid theoretical analysis of model building and performance with computational implementation and numerical examples.

Download or read book Bayesian Inference and Wavelet Methods in Image Processing written by Sharad Deep Silwal and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This report addresses some mathematical and statistical techniques of image processing and their computational implementation. Fundamental theories have been presented, applied and illustrated with examples. To make the report as self-contained as possible, key terminologies have been defined and some classical results and theorems are stated, in the most part, without proof. Some algorithms and techniques of image processing have been described and substantiated with experimentation using MATLAB. Several ways of estimating original images from noisy image data and their corresponding risks are discussed. Two image processing concepts selected to illustrate computational implementation are: "Bayes classification" and "Wavelet denoising". The discussion of the latter involves introducing a specialized area of mathematics, namely, wavelets. A self-contained theory for wavelets is built by first reviewing basic concepts of Fourier Analysis and then introducing Multi-resolution Analysis and wavelets. For a better understanding of Fourier Analysis techniques in image processing, original solutions to some problems in Fourier Analysis have been worked out. Finally, implementation of the above-mentioned concepts are illustrated with examples and MATLAB codes.

Download or read book Automatic Target Recognition Using Wavelet Transforms and Hidden Markov Model for High Range Resolution Profiles written by Radhika Prakash and published by . This book was released on 2001 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: