Data Management and Internet Computing for Image/Pattern Analysis focuses on the data management issues and Internet computing aspect of image processing and pattern recognition research. The book presents a comprehensive overview of the state of the art, providing detailed case studies that emphasize how image and pattern (IAP) data are distributed and exchanged on sequential and parallel machines, and how the data communication patterns in low- and higher-level IAP computing differ from general numerical computation, what problems they cause and what opportunities they provide. The studies also describe how the images and matrices should be stored, accessed and distributed on different types of machines connected to the Internet, and how Internet resource sharing and data transmission change traditional IAP computing. Data Management and Internet Computing for Image/Pattern Analysis is divided into three parts: the first part describes several software approaches to IAP computing, citing several representative data communication patterns and related algorithms; the second part introduces hardware and Internet resource sharing in which a wide range of computer architectures are described and memory management issues are discussed; and the third part presents applications ranging from image coding, restoration and progressive transmission. Data Management and Internet Computing for Image/Pattern Analysis is an excellent reference for researchers and may be used as a text for advanced courses in image processing and pattern recognition.

In recent yearswe haveseen considerableadvances in the development of - manoid robots, that is robots with an anthropomorphic design. Such robots should be capable of autonomously performing tasks for their human users in changing environments by adapting to these and to the circumstances at hand. To do so, they as well as any kind of autonomous robot need to have some way of understanding the world around them. We humans do so by our senses, both our far senses vision and hearing (smelling too) and our near senses touch and taste. Vision plays a special role in the way it simulta- ously tells us "where" and "what" in a direct way. It is therefore an accepted factthatto developautonomousrobots,humanoidornot,itisessentialto- clude competent systems for visual perception. Such systems should embody techniques from the ?eld of computer vision, in which sophisticated com- tational methods for extracting information from visual imagery have been developed over a number of decades. However, complete systems incorpor- ing such advanced techniques, while meeting the requirements of real-time processing and adaptivity to the complexity that even our everyday envir- ment displays, are scarce. The present volume takes an important step for ?lling this gap by presenting methods and a system for visual perception for a humanoid robot with speci?c applications to manipulation tasks and to how the robot can learn by imitating the human.

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition comprises papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems.

The articles are divided into four main parts. The first one consists of papers that propose new fuzzy and bio-inspired models to solve general problems. The second part deals with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques. The third part contains papers that apply hybrid intelligent systems to the problem of time series analysis and prediction, while the fourth one shows papers dealing with bio-inspired models in optimization and robotics applications. An edited book in which both theoretical and application aspects are covered.

The realistic generation of virtual doubles of real-world actors has been the focus of computer graphics research for many years. However, some problems still remain unsolved: it is still time-consuming to generate character animations using the traditional skeleton-based pipeline, passive performance capture of human actors wearing arbitrary everyday apparel is still challenging, and until now, there is only a limited amount of techniques for processing and modifying mesh animations, in contrast to the huge amount of skeleton-based techniques. In this thesis, we propose algorithmic solutions to each of these problems. First, two efficient mesh-based alternatives to simplify the overall character animation process are proposed. Although abandoning the concept of a kinematic skeleton, both techniques can be directly integrated in the traditional pipeline, generating animations with realistic body deformations. Thereafter, three passive performance capture methods are presented which employ a deformable model as underlying scene representation. The techniques are able to jointly reconstruct spatio-temporally coherent time-varying geometry, motion, and textural surface appearance of subjects wearing loose and everyday apparel. Moreover, the acquired high-quality reconstructions enable us to render realistic 3D Videos. At the end, two novel algorithms for processing mesh animations are described. The first one enables the fully-automatic conversion of a mesh animation into a skeletonbased animation and the second one automatically converts a mesh animation into an animation collage, a new artistic style for rendering animations. The methods described in the thesis can be regarded as solutions to specific problems or important building blocks for a larger application. As a whole, they form a powerful system to accurately capture, manipulate and realistically render realworld human performances, exceeding the capabilities of many related capture techniques. By this means, we are able to correctly capture the motion, the timevarying details and the texture information of a real human performing, and transform it into a fully-rigged character animation, that can be directly used by an animator, or use it to realistically display the actor from arbitrary viewpoints.

Computer systems that analyze images are critical to a wide variety of applications such as visual inspections systems for various manufacturing processes, remote sensing of the environment from space-borne imaging platforms, and automatic diagnosis from X-rays and other medical imaging sources. Professor Azriel Rosenfeld, the founder of the field of digital image analysis, made fundamental contributions to a wide variety of problems in image processing, pattern recognition and computer vision. Professor Rosenfeld's previous students, postdoctoral scientists, and colleagues illustrate in Foundations of Image Understanding how current research has been influenced by his work as the leading researcher in the area of image analysis for over two decades. Each chapter of Foundations of Image Understanding is written by one of the world's leading experts in his area of specialization, examining digital geometry and topology (early research which laid the foundations for many industrial machine vision systems), edge detection and segmentation (fundamental to systems that analyze complex images of our three-dimensional world), multi-resolution and variable resolution representations for images and maps, parallel algorithms and systems for image analysis, and the importance of human psychophysical studies of vision to the design of computer vision systems. Professor Rosenfeld's chapter briefly discusses topics not covered in the contributed chapters, providing a personal, historical perspective on the development of the field of image understanding. Foundations of Image Understanding is an excellent source of basic material for both graduate students entering the field and established researchers who require a compact source for many of the foundational topics in image analysis.

Spatial reasoning and planning is a core constituent in robotics, graphics, computer-aided design, and geographic information systems.

After a review of previous work in the related areas, Liu and Daneshmend present here a unified framework for qualitative spatial representation and reasoning, which enables the generation of solutions to spatial problems where the geometric knowledge is imprecise. The approach utilizes qualitative spatial representation and reasoning integrated with a quantitative search procedure based on simulated annealing. Many graphical illustrations and detailed algorithm descriptions help the readers to comprehend the solution paths and to develop their own applications.

The book is written as a self-contained text for researchers and graduate students in computer science and related engineering disciplines. The methodologies, algorithmic details, and case studies presented can be used as course material as well as a convenient reference.

Computer vision is one of the most complex and computationally intensive problem. Like any other computationally intensive problems, parallel pro cessing has been suggested as an approach to solving the problems in com puter vision. Computer vision employs algorithms from a wide range of areas such as image and signal processing, advanced mathematics, graph theory, databases and artificial intelligence. Hence, not only are the comput ing requirements for solving vision problems tremendous but they also demand computers that are efficient to solve problems exhibiting vastly dif ferent characteristics. With recent advances in VLSI design technology, Single Instruction Multiple Data (SIMD) massively parallel computers have been proposed and built. However, such architectures have been shown to be useful for solving a very limited subset of the problems in vision. Specifically, algorithms from low level vision that involve computations closely mimicking the architec ture and require simple control and computations are suitable for massively parallel SIMD computers. An Integrated Vision System (IVS) involves com putations from low to high level vision to be executed in a systematic fashion and repeatedly. The interaction between computations and information dependent nature of the computations suggests that architectural require ments for computer vision systems can not be satisfied by massively parallel SIMD computers."

The two-volume proceedings, LNCS 6927 and LNCS 6928, constitute the papers presented at the 13th International Conference on Computer Aided Systems Theory, EUROCAST 2011, held in February 2011 in Las Palmas de Gran Canaria, Spain. The total of 160 papers presented were carefully reviewed and selected for inclusion in the books. The contributions are organized in topical sections on concepts and formal tools; software applications; computation and simulation in modelling biological systems; intelligent information processing; heurist problem solving; computer aided systems optimization; model-based system design, simulation, and verification; computer vision and image processing; modelling and control of mechatronic systems; biomimetic software systems; computer-based methods for clinical and academic medicine; modeling and design of complex digital systems; mobile and autonomous transportation systems; traffic behaviour, modelling and optimization; mobile computing platforms and technologies; and engineering systems applications.

This book constitutes the thoroughly refereed post-conference proceedings of the 18th Annual International Workshop on Selected Areas in Cryptography, SAC 2011, held in Toronto, Canada in August 2011. The 23 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on cryptanalysis of hash functions, security in clouds, bits and randomness, cryptanalysis of ciphers, cryptanalysis of public-key crypthography, cipher implementation, new designs and mathematical aspects of applied cryptography.

Shape Analysis and Retrieval of Multimedia Objects provides a comprehensive survey of the most advanced and powerful shape retrieval techniques used in practice today. In addition, this monograph addresses key methodological issues for evaluation of the shape retrieval methods. Shape Analysis and Retrieval of Multimedia Objects is designed to meet the needs of practitioners and researchers in industry, and graduate-level students in Computer Science.

This book constitutes the thoroughly refereed post-proceedings of four workshops held as satellite events of the JSAI International Symposia on Artificial Intelligence 2010, in Tokyo, Japan, in November 2010. The 28 revised full papers with four papers for the following four workshops presented were carefully reviewed and selected from 70 papers. The papers are organized in sections Logic and Engineering of Natural Language Semantics (LENLS), Juris-Informatics (JURISIN), Advanced Methodologies for Bayesian Networks (AMBN), and Innovating Service Systems (ISS).

Although there has been much progress in developing theories, models and systems in the areas of Natural Language Processing (NLP) and Vision Processing (VP), there has heretofore been little progress on integrating these two subareas of Artificial Intelligence (AI). This book contains a set of edited papers addressing theoretical issues and the grounding of representations in NLP and VP from philosophical and psychological points of view. The papers focus on site descriptions such as the reasoning work on space at Leeds, UK, the systems work of the ILS (Illinois, U.S.A.) and philosophical work on grounding at Torino, Italy, on Schank's earlier work on pragmatics and meaning incorporated into hypermedia teaching systems, Wilks' visions on metaphor, on experimental data for how people fuse language and vision and theories and computational models, mainly connectionist, for tackling Searle's Chinese Room Problem and Harnad's Symbol Grounding Problem. The Irish Room is introduced as a mechanism through which integration solves the Chinese Room. The U.S.A., China and the EU are well reflected, showing the fact that integration is a truly international issue. There is no doubt that all of this will be necessary for the SuperInformationHighways of the future.

Recently, much attention has been paid to image processing with multiresolution and hierarchical structures such as pyramids and trees. This volume deals with recursive pyramids, which combine the advantages of available multiresolution structures and which are convenient both for global and local image processing. Recursive pyramids are based on regular hierarchical (recursive) structures containing data on image fragments of different sizes. Such an image representation technique enables the effective manipulation of pictorial information as well as the development of special hardware or data structures. The major aspects of this book are two original mathematical models of greyscale and binary images represented by recursive structures. Image compression, transmission and processing are discussed using these models. A number of applications are presented, including optical character recognition, expert systems and special computer architecture for pictorial data processing. The majority of results are presented as algorithms applicable to discrete information fields of arbitrary dimensions (e.g. 2-D or 3-D images). The book is divided into six chapters: Chapter 1 provides a brief introduction. Chapter 2 then deals with recursive structures and their properties. Chapter 3 introduces pyramidal image models. Image coding and the progressive transmission of images with gradual refinement are discussed in Chapter 4. Chapters 5 and 6 are devoted to image processing with pyramidal-recursive structures and applications. The volume concludes with a comprehensive bibliography. For applied mathematicians and computer scientists whose work involves computer vision, information theory and other aspects of image representation techniques.

Image analysis is one of the most challenging areas in today's computer sci ence, and image technologies are used in a host of applications. This book concentrates on image textures and presents novel techniques for their sim ulation, retrieval, and segmentation using specific Gibbs random fields with multiple pairwise interaction between signals as probabilistic image models. These models and techniques were developed mainly during the previous five years (in relation to April 1999 when these words were written). While scanning these pages you may notice that, in spite of long equa tions, the mathematical background is extremely simple. I have tried to avoid complex abstract constructions and give explicit physical (to be spe cific, "image-based") explanations to all the mathematical notions involved. Therefore it is hoped that the book can be easily read both by professionals and graduate students in computer science and electrical engineering who take an interest in image analysis and synthesis. Perhaps, mathematicians studying applications of random fields may find here some less traditional, and thus controversial, views and techniques.

Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. High-order networks require little training data (hence, short training times) and have been used to perform transformation-invariant recognition of relatively simple visual patterns, achieving very high recognition rates. The successful results of these methods provided inspiration to address more practical problems which have grayscale as opposed to binary patterns (e.g., alphanumeric characters, aircraft silhouettes) and are also more complex in nature as opposed to purely edge-extracted images - human face recognition is such a problem. Human Face Recognition Using Third-Order Synthetic Neural Networks serves as an excellent reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns.

This book constitutes the thoroughly refereed post-conference proceedings of the 6th International ICST Conference on Mobile Multimedia Communications (MOBIMEDIA 2010) held in Lisbon, Portugal, in September 2010, which was accompanied by the First International Workshop on Cognitive Radio and Cooperative Strategies for POWER Saving (C2POWER 2010), the Workshop on Impact of Scalable Video Coding on Multimedia Provisioning (SVCVision 2010), and the First International Workshop on Energy-efficient and Reconfigurable Transceivers (EERT 2010). The 59 revised full papers presented were carefully reviewed and selected from numerous submissions and are organized in topical sections on advanced techniques for video transmission; multimedia distribution; modelling of wireless systems; cellular networks; mobility concepts for IMT-advances (MOBILIA); media independent handovers (MIH-4-MEDIA); and IP-based emergency applications and services for next generation networks (PEACE).

Image-based rendering (IBR) refers to a collection of techniques and representations that allow 3D scenes and objects to be visualized in a realistic way without full 3D model reconstruction. IBR uses images as the primary substrate. The potential for photorealistic visualization has tremendous appeal, and it is thus not surprising that IBR has been receiving increasing attention over the years. Applications such as video games, virtual travel, and E-commerce stand to benefit from this technology.

Image-Based Rendering examines the theory, practice, and applications associated with image-based rendering and modeling. The authors bring together their backgrounds and research experiences in computer graphics, computer vision and signal processing to address the multi-disciplinary nature of IBR research. The topics to be covered vary from IBR basic concepts and representations on the theory side, to signal processing and data compression on the practical side. These theoretical and practical issues are further disseminated in several IBR systems built to-date. However, this book will not focus on the geometrical modeling aspect of IBR, since 3D modeling has been extensively treated elsewhere in the vision literature.

One of the only titles devoted exclusively to the area of IBR, this book is intended for researchers, professionals, and general readers interested in the topics of computer graphics, computer vision, image processing, and video processing. Advanced-level students in EECS studying related disciplines will be able to seriously expand their knowledge about image-based rendering.

More mathematicians have been taking part in the development of digital image processing as a science and the contributions are reflected in the increasingly important role modeling has played solving complex problems. This book is mostly concerned with energy-based models. Most of these models come from industrial projects in which the author was involved in robot vision and radiography: tracking 3D lines, radiographic image processing, 3D reconstruction and tomography, matching, deformation learning. Numerous graphical illustrations accompany the text.

Monitoring of public and private sites has increasingly become a very sensitive issue resulting in a patchwork of privacy laws varying from country to country -though all aimed at protecting the privacy of the citizen. It is important to remember, however, that monitoring and vi sual surveillance capabilities can also be employed to aid the citizen. The focus of current development is primarily aimed at public and cor porate safety applications including the monitoring of railway stations, airports, and inaccessible or dangerous environments. Future research effort, however, has already targeted citizen-oriented applications such as monitoring assistants for the aged and infirm, route-planning and congestion-avoidance tools, and a range of environment al monitoring applications. The latest generation of surveillance systems has eagerly adopted re cent technological developments to produce a fully digital pipeline of digital image acquisition, digital data transmission and digital record ing. The resultant surveillance products are highly-fiexihle, capahle of generating forensic-quality imagery, and ahle to exploit existing Internet and wide area network services to provide remote monitoring capability.

Vision-based mobile robot guidance has proved difficult for classical machine vision methods because of the diversity and real-time constraints inherent in the task. This book describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes these difficulties. ALVINN learns to guide mobile robots using the back-propagation training algorithm. Because of its ability to learn from example, ALVINN can adapt to new situations and therefore cope with the diversity of the autonomous navigation task. But real world problems like vision-based mobile robot guidance present a different set of challenges for the connectionist paradigm. Among them are: * how to develop a general representation from a limited amount of real training data; * how to understand the internal representations developed by artificial neural networks; * how to estimate the reliability of individual networks; * how to combine multiple networks trained for different situations into a single system; * how to combine connectionist perception with symbolic reasoning.Neural Network Perception for Mobile Robot Guidance presents novel solutions to each of these problems. Using these techniques, the ALVINN system can learn to control an autonomous van in under 5 minutes by watching a person drive. Once trained, individual ALVINN networks can drive in a variety of circumstances, including single-lane paved and unpaved roads, and multi-lane lined and unlined roads, at speeds of up to 55 miles per hour. The techniques also are shown to generalize to the task of controlling the precise foot placement of a walking robot.

Perceptual Organization for Artificial Vision Systems is an edited collection of invited contributions based on papers presented at The Workshop on Perceptual Organization in Computer Vision, held in Corfu, Greece, in September 1999. The theme of the workshop was 'Assessing the State of the Community and Charting New Research Directions.' Perceptual organization can be defined as the ability to impose structural regularity on sensory data, so as to group sensory primitives arising from a common underlying cause. This book explores new models, theories, and algorithms for perceptual organization. Perceptual Organization for Artificial Vision Systems includes contributions by the world's leading researchers in the field. It explores new models, theories, and algorithms for perceptual organization, as well as demonstrates the means for bringing research results and theoretical principles to fruition in the construction of computer vision systems. The focus of this collection is on the design of artificial vision systems. The chapters comprise contributions from researchers in both computer vision and human vision.

During the past few years, we have been witnessing the rapid growth of the ap plications of Interactive Digital Video, Multimedia Computing, Desktop Video Teleconferencing, Virtual Reality, and High Definition Television (HDTV). An other information revolution which is tied to Cyberspace is almost within reach. The information, data, text, graphics, video, sound, etc. , in the form of multi media, can be requested, accessed, distributed, and transmitted to potentially every household. This is changing and will continue to change the way of people doing business, functioning in the society, and entertaining. In the foreseeable future, many personalized, portable information terminals, which can be car ried while traveling, will provide the link to central computer network to allow information exchange including videos from a node to node, from a center to a node, or nodes. Facing this opportunity, the question is what are the major significant technical challenges that people have to solve to push the-state-of-the-art for the realiza tion of the above mentioned technology advancement? From our professional judgement We feel that one of the major technical challenges is in Video Data Compression. Video communications in the form of desktop teleconferencing, videophone, network video delivery on demand, even games, are going to be major media traveling in the information super highway, hopping from one node in the Cyberspace to the other.

The key contribution of the approach to x-ray mammographic image analysis developed in this monograph is a representation of the non-fatty compressed breast tissue that we show can be derived from a single mammogram. The importance of the representation, called hint, is that it removes all those changes in the image that are due only to the particular imaging conditions (for example, the film speed or exposure time), leaving just the non-fatty interesting' tissue. Normalising images in this way enables them to be enhanced and matched, and regions in them to be classified more reliably, because unnecessary, distracting variations have been eliminated. Part I of the monograph develops a model-based approach to x-ray mammography, Part II shows how it can be put to work successfully on a range of clinically-important tasks, while Part III develops a model and exploits it for contrast-enhanced MRI mammography. The final chapter points the way forward in a number of promising areas of research. Audience: This book has been written for a wide readership, including medical image analysts, medical physicists, radiologists, breast surgeons, and research students. The mathematics and algorithms have been relegated to boxes so that the book can be read and understood even if the mathematical detail is skipped. Large parts of the monograph will be of interest to clinicians generally and to patients.

This book constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Computational Challenges and Clinical Opportunities in Virtual Colonoscopy and Abdominal Imaging, held in conjunction with MICCAI 2010, in Beijing, China, on September 20, 2010.
The 19 revised full papers presented were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections on CT colonography CAD, abdominal imaging, and virtual colonoscopy.

1. Introduction . 1 2. Areas and Angles . . 6 3. Tessellations and Symmetry 14 4. The Postulate of Closest Approach 28 5. The Coexistence of Rotocenters 36 6. A Diophantine Equation and its Solutions 46 7. Enantiomorphy. . . . . . . . 57 8. Symmetry Elements in the Plane 77 9. Pentagonal Tessellations . 89 10. Hexagonal Tessellations 101 11. Dirichlet Domain 106 12. Points and Regions 116 13. A Look at Infinity . 122 14. An Irrational Number 128 15. The Notation of Calculus 137 16. Integrals and Logarithms 142 17. Growth Functions . . . 149 18. Sigmoids and the Seventh-year Trifurcation, a Metaphor 159 19. Dynamic Symmetry and Fibonacci Numbers 167 20. The Golden Triangle 179 21. Quasi Symmetry 193 Appendix I: Exercise in Glide Symmetry . 205 Appendix II: Construction of Logarithmic Spiral . 207 Bibliography . 210 Index . . . . . . . . . . . . . . . . . . . . 225 Concepts and Images is the result of twenty years of teaching at Harvard's Department of Visual and Environmental Studies in the Carpenter Center for the Visual Arts, a department devoted to turning out students articulate in images much as a language department teaches reading and expressing one self in words. It is a response to our students' requests for a "handout" and to l our colleagues' inquiries about the courses: Visual and Environmental Studies 175 (Introduction to Design Science), YES 176 (Synergetics, the Structure of Ordered Space), Studio Arts 125a (Design Science Workshop, Two-Dimension al), Studio Arts 125b (Design Science Workshop, Three-Dimensional),2 as well as my freshman seminars on Structure in Science and Art."