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This work explores automatic target recognition (ATR). It is divided into sections which look at topics such as: advanced systems for ATR, including airborne video surveillance; multisensor ATR; and adaptive and learning techniques for ATR.
What is Automatic Target Recognition The capacity of an algorithm or device to recognize targets or other objects based on data acquired from sensors is referred to as automatic target recognition, an abbreviation for these capabilities. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Automatic Target Recognition Chapter 2: Computer Vision Chapter 3: Radar Chapter 4: Doppler Radar Chapter 5: Synthetic-aperture Radar Chapter 6: Imaging Radar Chapter 7: Beamforming Chapter 8: Pulse-Doppler Radar Chapter 9: Passive Radar Chapter 10: Inverse Synthetic-aperture Radar (II) Answering the public top questions about automatic target recognition. (III) Real world examples for the usage of automatic target recognition in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Automatic Target Recognition.
This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at www.cambridge.org/9780521830461. A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.
Although the field of sparse representations is relatively new, research activities in academic and industrial research labs are already producing encouraging results. The sparse signal or parameter model motivated several researchers and practitioners to explore high complexity/wide bandwidth applications such as Digital TV, MRI processing, and certain defense applications. The potential signal processing advancements in this area may influence radar technologies. This book presents the basic mathematical concepts along with a number of useful MATLAB® examples to emphasize the practical implementations both inside and outside the radar field. Table of Contents: Radar Systems: A Signal Processing Perspective / Introduction to Sparse Representations / Dimensionality Reduction / Radar Signal Processing Fundamentals / Sparse Representations in Radar
The two-volume set LNCS 3522 and 3523 constitutes the refereed proceedings of the Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005, held in Estoril, Portugal in June 2005. The 170 revised full papers presented were carefully reviewed and selected from 292 submissions. The papers are organized in topical sections on computer vision, shape and matching, image and video processing, image and video coding, face recognition, human activity analysis, surveillance, robotics, hardware architectures, statistical pattern recognition, syntactical pattern recognition, image analysis, document analysis, bioinformatics, medical imaging, biometrics, speech recognition, natural language analysis, and applications.
The chapters in this volume were presented at the July–August 2008 NATO Advanced Study Institute on Unexploded Ordnance Detection and Mitigation. The conference was held at the beautiful Il Ciocco resort near Lucca, in the glorious Tuscany region of northern Italy. For the ninth time we gathered at this idyllic spot to explore and extend the reciprocity between mathematics and engineering. The dynamic interaction between world-renowned scientists from the usually disparate communities of pure mathematicians and applied scientists which occurred at our eight previous ASI’s continued at this meeting. The detection and neutralization of unexploded ordnance (UXO) has been of major concern for very many decades; at least since the First World war. UXO continues to be the subject of intensive research in many ?elds of science, incl- ing mathematics, signal processing (mainly radar and sonar) and chemistry. While today’s headlines emphasize the mayhem resulting from the placement of imp- vised explosive devices (IEDs), humanitarian landmine clearing continues to draw signi?cant global attention as well. In many countries of the world, landmines threaten the population and hinder reconstruction and fast, ef?cient utilization of large areas of the mined land in the aftermath of military con?icts.
The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut