Series In Machine Perception And Artificial Intelligence - Böcker

This volume contains 18 papers of high quality, selected to represent the work that is being developed by Spanish research groups in pattern recognition and image analysis. It is partly the result of the efforts of the Spanish Association for Pattern Recognition and Image Analysis (AERFAI). It is hoped that this volume will increase awareness of Spanish work in the international scientific community and initiate contacts with research groups worldwide.

This book contains 31 papers carefully selected from among those presented at the 7th Scandinavian Conference on Image Analysis. The authors have extended their papers to give a more in-depth discussion of the theory, or of the experimental validation of the method they have proposed. The topics covered are current and wide-ranging and include both 2D- and 3D-vision, and low to high level vision.

The neural network paradigm with its various advantages might be the next promising bridge between artificial intelligence and pattern recognition that will help with the conceptualization of new computational artifacts. This volume contains ten papers which represent some of the work being done in the field, such as in computational neuroscience, pattern recognition, computational vision, and applications.

Presents essays on such topics as three-dimensional object representation by array grammars, stochastic puzzle grammars, parallel recognition of high dimensional images, two-dimensional uniquely parsable isometric array grammars, and replicated image algorithms and their analyses on SIMD machines.

Pattern recognition is an active area of research with many applications, some of which have reached commercial maturity. Structural and syntactic methods are very powerful. They are based on symbolic data structures together with matching, parsing, and reasoning procedures that are able to infer interpretations of complex input patterns.This book gives an overview of the latest developments and achievements in the field.

Thinning is a technique widely used in the pre-processing stage of a pattern recognition system to compress data and to enhance feature extraction in the subsequent stage. It reduces a digitized pattern to a skeleton so that all resulting branches are 1 pixel thick. The method seems easy at first and has many advantages, however after two decades of intensive research, it has been found to be very challenging due to the difficulties in programming computers to do it.This collection of 15 papers by leading scientists working in the area examines the theoretical and experimental aspects of thinning methodologies. The authors have addressed the problems faced, compared their performance results with others, and assessed the challenges ahead. Researchers will find the volume helpful in shedding light on difficult issues and stimulating further research in the area.

This monograph provides a detailed record of the “GRUFF” research project. The goal of the GRUFF project is to develop techniques for robotic vision systems to recognize objects by reasoning about their intended function rather than matching to a pre-defined database of 2-D object appearances or 3-D object shapes. The contributions of this work are: a demonstration of the feasibility of the “form and function” approach to reasoning about 3-D shapes; a demonstration of the concept of using a small number of knowledge primitives as component building blocks in creating a function-based definition of an object category; and an indexing mechanism to make processing for recognition more efficient without any substantial decrease in correctness of classification. Results are given for the analysis of over 500 3-D shape descriptions created with a solid modeling tool and over 200 shape descriptions extracted from real laser range finder images.

This book provides a detailed assessment of the state of the art in automated techniques for the analysis of digital mammogram images. Topics covered include a variety of approaches for image processing and pattern recognition aimed at assisting the physician in the task of detecting tumors from evidence in mammogram images. The chapters are written by recognized experts in the field and are revised versions of papers selected from those presented at the “First International Workshop on Mammogram Image Analysis” held in San Jose as part of the 1993 Biomedical Image Processing conference.

To fully appreciate new methods developed in the area of machine vision it is necessary to have facilities which allow experimental verification of such methods. Experimental research is typically a very expensive task in terms of manpower, and consequently it is desirable to adopt standard facilities/methods which allow more efficient experimental investigations. In this volume a range of different experimental environments which facilitate construction and integration of machine vision systems is described. The environments presented cover areas such as robotics, research in individual machine vision methods, system integration, knowledge representation, and distributed computing. The set of environments covered include commercial systems, public domain software and laboratory prototype, showing the diversity of the problem of experimental research in machine vision and providing the reader with an overview of the area.

This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (PINs). The PIN, which forms the heart of the system and which is based on Bayesian probabilistic networks, exhibits potential for application in several areas of computer vision as well as a range of other spatial reasoning tasks. The text includes a highly comprehensive, classificatory review of prior work in perceptual organization and, within that framework, identifies key areas for future work by the computer vision research community.

This volume highlights some of the multidisciplinary aspects of automatic signature verification. The first two chapters serve as an introduction. The first constitutes a review of the literature of the past five years. The second addresses the problem of parallel strategies to construct and optimize feature vectors to describe a signature. The remaining six chapters are divided into two sections: research on static systems and research on dynamic systems. The section on off-line systems describes a system, based on cooperative neural networks for the automatic processing of signatures on checks, for background removal as well as a model-based system that segments the signature into elements which are then processed locally and globally to evaluate the similarity between two specimens. The three papers on on-line systems compare three verification methods, based on statistical models of signature features using the same benchmark, describe a step-wise verification method based on the analysis of the signature components and describe the different design phases of an operational system, focusing on the various decisions that have to be made throughout the development of such a prototype.

This review volume contains a selection of papers by leading experts in the areas of Parallel Image Analysis, 2-D, 3-D Grammars and Automata and Neural Nets and Learning.

Interest in the automatic processing and analysis of document images has been rapidly increasing during the past few years. This book addresses the different subfields of document image analysis, including preprocessing and segmentation, form processing, handwriting recognition, line drawing and map processing, and contextual processing.

The 1st International Workshop on Document Analysis Systems (DAS94,) is a full workshop on research and development of systems for the analysis of document images.This volume will be of use to academic and industrial researchers, end-users, students and principal investigators.

This text features a broad array of research efforts in computer vision including low level processing, perceptual organization, object recognition and active vision. The volume's nine papers specifically report on topics such as sensor confidence, low level feature extraction schemes, non-parametric multi-scale curve smoothing, integration of geometric and non-geometric attributes for object recognition, design criteria for a four degree-of-freedom robot head, a real-time vision system based on control of visual attention and a behavior-based active eye vision system. The scope of the book provides an excellent sample of current concepts, examples and applications from multiple areas of computer vision.

This edited and reviewed volume consists of papers that were originally presented at a workshop in the Scientific Center at Schloss Dagstuhl, Germany. It gives an overview of the field and presents the latest developments in the areas of modeling and planning for sensor based robots. The particular topics addressed include active vision, sensor fusion, environment modeling, motion planning, robot navigation, distributed control architectures, reactive behavior, and others.

This book covers parallel algorithms and architectures and VLSI chips for a range of problems in image processing, computer vision, pattern recognition and artificial intelligence. The specific problems addressed include vision and image processing tasks, Fast Fourier Transforms, Hough Transforms, Discrete Cosine Transforms, image compression, polygon matching, template matching, pattern matching, fuzzy expert systems and image rotation. The collection of papers gives the reader a good introduction to the state-of-the-art, while for an expert this serves as a good reference and a source of some new contributions in this field.

Traditional methods for image scene interpretation and understanding are based mainly on such single-threaded procedural paradigms as hypothesize-and-test or syntactic parsing. As a result, these systems are unable to carry out tasks that require concurrent hypothesis testing.This book explores a method for symbolically interpreting images based upon a parallel implementation of a network-of-frames suggested, for example, by Minsky (1975), to describe intelligent processing. The system has been implemented in an object-oriented environment in the logic programming language Parlog++ and includes the propagation of uncertainty through each frame using Baldwin's (1986) formulation. The system is tested with several scenarios of increasing complexity, culminating with legal interpretation of traffic intersection images.

This volume deals with the following topics: 2-D, 3-D automata and grammars, parallel architecture for image processing, parallel digital geometry algorithms, data allocation strategies for parallel image processing algorithms, complexity analysis of parallel image operators. The contributions are written by leading experts in the fields of models, algorithms and architectures for parallel image processing.

Machine vision technology has created a strong interest among research organizations, resulting in many innovative products. Despite this end users have been very skeptical towards machine vision and its robustness in harsh industrial environments. This book presents the results of a national machine vision technology program aimed at boosting research and putting research results to work in practical industrial applications. The topics to be covered include image acquisition, analysis of surface color and texture, applications of machine vision in surface inspection and process control, 3-D measurements, and CAD-based machine vision.

As perception stands for the acquisition of a real world representation by interaction with an environment, learning is the modification of this internal representation. This book highlights the relation between perception and learning and describes the influence of the learning in the interaction with the environment. Besides, this volume contains a series of applications of both machine learning and perception, where the former is often embedded in the latter and vice-versa. Among the topics covered, there are visual perception for autonomous robots, model generation of visual patterns, attentional reasoning, genetic approaches and various categories of neural networks.

More than ten years have passed since the untimely death of King-Sun Fu, one of the great pioneers in the field of pattern recognition. It was he, more than any other single individual, who nurtured the field during its formative years, and set the tone and tempo for others to follow. This book is dedicated to his memory.This book contains 11 chapters by authors who knew King-Sun Fu and in varying degrees interacted with him. The articles span the field of pattern recognition in its current state, and cover such diverse topics as neural nets, covariance propagation, genetic selection, shape description, characteristic views for 3D modeling, face recognition, speech recognition, and machine translation. In tone they vary from the highly theoretical to the applied. Their presentation here is a testimonial, by his former colleagues and friends, to the pioneer who did so much to bring pattern recognition to its position as a recognized discipline world-wide.

The use of genetic algorithms as a training method for neural networks is described in this book. After introducing neural networks and genetic algorithms, it gives a number of examples to demonstrate the use of the proposed techniques. Moreover, a comparison of the results with the back-propagation algorithm is made.

This book is the result of a special workshop on Spatial Computing which brought together experts in computer vision, visualization, multimedia and geographic information systems to discuss common problems and applications. The common theme of the workshop was the need to integrate human perception and domain knowledge with developing representations and solutions to problems which necessarily involve the interpretation of sensed data. The overwhelming conclusion was that these different areas of spatial computing should be communicating more than is done at present and that such workshops and publications would help this process.

This book provides an overview of the state of the art in research and development of systems for document image analysis. Topics covered include a variety of systems and architectures for processing document images as well as methods for converting those images into formats that can be manipulated by a computer. The chapters are written by recognized experts in the field and describe Systems and Architectures, Recognition Techniques, Graphics Analysis, Document Image Retrieval, and World Wide Web Applications.

Rapid advances in sensors, computers, and algorithms continue to fuel dramatic improvements in intelligent robots. In addition, robot vehicles are starting to appear in a number of applications. For example, they have been installed in public settings to perform such tasks as delivering items in hospitals and cleaning floors in supermarkets; recently, two small robot vehicles were launched to explore Mars.This book presents the latest advances in the principal fields that contribute to robotics. It contains contributions written by leading experts addressing topics such as Path and Motion Planning, Navigation and Sensing, Vision and Object Recognition, Environment Modeling, and others.

The widespread use of bankchecks in daily life makes the development of check-reading systems of fundamental relevance to banks and other financial institutions. This will improve productivity and allow advanced customer services. Therefore, many industrial companies and academic research laboratories have recently been attracted to this field, which involves several aspects, like image acquisition and preprocessing, layout analysis, preprinted data identification and recognition, user-entered data extraction, recognition of handwritten characters and words, and signature verification.The contributions collected in this book present the state of the art in the field of complete systems for bankcheck recognition, and explore the most promising trends in key aspects of this research field.