In the development of autonomous sensory controlled systems, image understanding of sensory data is a difficult but important topic. Due to the unpredictable and uncertain nature of the environment, current image processing and computer vision approaches are not adequate to provide the capabilities needed by the systems. Thus, new approaches are required in the overall system design, including sophisticated reasoning processes, uncertainty management and adaptable architectures. This general issue is addressed by Thomas M Strat and Grahame B Smith. Lashon B Booker discusses the Bayesian approach in plausible reasoning for classification of complex ship images based on incomplete and uncertain evidence. Dynamic scene analysis is treated by Seetharaman Gunasekaran and Tzay Y Young. A spherical perspective approach is introduced to overcome some limitations of the current vision systems by Michael Penna and Su-shing Chen. Finally, Markov image models and their pixel-level approaches are extended to global approaches, through Dempster-Shafer and other techniques, by Mingchuan Zhang and Su-shing Chen.

"The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures."--BOOK JACKET.

The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.

Issues in Computer Engineering / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Computer Engineering. The editors have built Issues in Computer Engineering: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Computer Engineering in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Computer Engineering: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

"The main theme of the 1988 workshop, the 18th in this DARPA sponsored series of meetings on Image Understanding and Computer Vision, is to cover new vision techniques in prototype vision systems for manufacturing, navigation, cartography, and photointerpretation." P. v.

This thesis is concerned with the theoretical and practical development of reliable and robust localisation algorithms for autonomous land vehicles operating at high speeds in unstructured, expansive and harsh environments. Localisation is the ability of a vehicle to determine its position and orientation within an operating environment. The need for such a localisation system is motivated by the requirement of developing autonomous vehicles in applications such as mining, agriculture and cargo handling. The main drivers in these applications are for safety, efficiency and productivity. The approach taken to the localisation problem in this thesis guarantees that the safety and reliability requirements imposed by such applications are achieved. The approach also aims to minimise the engineering or modification of the environment, such as adding artificial landmarks or other infrastructure. This is a key driver in the practical implementation of a localisation algorithm. In pursuit of these objectives, this thesis makes the following principal contributions: 1. The development of an Iterative Closest Point - Extended Kalman Filter (ICP-EKF) algorithm - a map-based iconic algorithm that utilises measurements from a scanning laser rangefinder to achieve localisation. The ICP-EKF algorithm entails the development of a map-building algorithm. The main attraction of the map-based localisation algorithm is that it works directly on sensed data and thus does not require extraction and matching of features. It also explicitly takes into account the uncertainty associated with measurements and has the ability to include measurements from a variety of different sensors. 2. The development and implementation of an entropy-based metric to evaluate the information content of measurements. This metric facilitates the augmentation of landmarks to the ICP-EKF algorithm thus guaranteeing reliable and robust localisation. 3. The development and adaptation of a view-invariant Curvature Scale Space (CSS) landmark extraction algorithm. The algorithm is sufficiently robust to sensor noise and is capable of reliably detecting and extracting landmarks that are naturally present in the environment from laser rangefinder scans. 4. The integration of the information metric and the CSS and ICP-EKF algorithms to arrive at a unified localisation framework that uses measurements from both artificial and natural landmarks, combined with dead-reckoning sensors, to deliver reliable vehicle position estimates. The localisation framework developed is sufficiently generic to be used on a variety of other autonomous land vehicle systems. This is demonstrated by its implementation using field data collected from three different trials on three different vehicles. The first trial was carried out on a four-wheel drive vehicle in an underground mine tunnel. The second trial was conducted on a Load-Haul-Dump (LHD) truck in a test tunnel constructed to emulate an underground mine. The estimates of the proposed localisation algorithms are compared to the ground truth provided by an artificial landmark-based localisation algorithm that uses bearing measurements from a laser. To demonstrate the feasibility and reliability of both the natural landmark extraction and localisation algorithms, these are also implemented on a utility vehicle in an outdoor area within the University's campus. The results demonstrate the robustness of the proposed localisation algorithms in producing reliable and accurate position estimates for autonomous vehicles operating in a variety of unstructured domains.

The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form "find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. " Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj.

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results.

This volume presents the proceedings of the 10th International Workshop on Combinatorial Image Analysis, held December 1–3, 2004, in Auckland, New Zealand. Prior meetings took place in Paris (France, 1991), Ube (Japan, 1992), Washington DC (USA, 1994), Lyon (France, 1995), Hiroshima (Japan, 1997), Madras (India, 1999), Caen (France, 2000), Philadelphia (USA, 2001), and - lermo (Italy, 2003). For this workshop we received 86 submitted papers from 23 countries. Each paper was evaluated by at least two independent referees. We selected 55 papers for the conference. Three invited lectures by Vladimir Kovalevsky (Berlin), Akira Nakamura (Hiroshima), and Maurice Nivat (Paris) completed the program. Conference papers are presented in this volume under the following topical part titles: discrete tomography (3 papers), combinatorics and computational models (6), combinatorial algorithms (6), combinatorial mathematics (4), d- ital topology (7), digital geometry (7), approximation of digital sets by curves and surfaces (5), algebraic approaches (5), fuzzy image analysis (2), image s- mentation (6), and matching and recognition (7). These subjects are dealt with in the context of digital image analysis or computer vision.