Azriel Rosenfeld – författare

Digital geometry is about deriving geometric information from digital pictures. The field emerged from its mathematical roots some forty-years ago through work in computer-based imaging, and it is used today in many fields, such as digital image processing and analysis (with applications in medical imaging, pattern recognition, and robotics) and of course computer graphics. Digital Geometry is the first book to detail the concepts, algorithms, and practices of the discipline. This comphrehensive text and reference provides an introduction to the mathematical foundations of digital geometry, some of which date back to ancient times, and also discusses the key processes involved, such as geometric algorithms as well as operations on pictures.*A comprehensive text and reference written by pioneers in digital geometry, image processing and analysis, and computer vision*Provides a collection of state-of-the-art algorithms for a wide variety of geometrical picture analysis tasks, including extracting data from digital images and making geometric measurements on the data*Includes exercises, examples, and references to related or more advanced work

The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.

The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.

Digital Picture Processing is a technique-oriented book aiming to teach the more extensive treatments of digital pictures. The book discusses picture processing; the computer representation of pictures; and the mathematical preliminaries involved. The visual perception, the digitization and the different techniques on sampling, and different techniques on compression are also covered. The book also explains the enhancement techniques, such as sharpening and moothing; filtering techniques used in restoration; and the geometry and description of a picture. The text is recommended to students of electrical engineering and computer science, who intend to learn better techniques in picture processing through digital means. The book is also suitable as an advanced undergraduate or a graduate course in picture processing.

In the past five years, the field of electrostatic discharge (ESD) control has under­ gone some notable changes. Industry standards have multiplied, though not all of these, in our view, are realistic and meaningful. Increasing importance has been ascribed to the Charged Device Model (CDM) versus the Human Body Model (HBM) as a cause of device damage and, presumably, premature (latent) failure. Packaging materials have significantly evolved. Air ionization techniques have improved, and usage has grown. Finally, and importantly, the government has ceased imposing MIL-STD-1686 on all new contracts, leaving companies on their own to formulate an ESD-control policy and write implementing documents. All these changes are dealt with in five new chapters and ten new reprinted papers added to this revised edition of ESD from A to Z. Also, the original chapters have been augmented with new material such as more troubleshooting examples in Chapter 8 and a 20-question multiple-choice test for certifying operators in Chapter 9. More than ever, the book seeks to provide advice, guidance, and practical ex­ amples, not just a jumble of facts and generalizations. For instance, the added tailored versions of the model specifications for ESD-safe handling and packaging are actually in use at medium-sized corporations and could serve as patterns for many readers.

Biological visual systems employ parallel processing to perform real-world visual tasks in real time. A key to this performance seems to be that biological systems construct representations of their visual image data at multiple scales. This text describes a multiscale, or "pyramid", approach to vision, including its theoretical foundations, a set of pyramid-based modules for image processing, object detection, texture discrimination, contour detection and processing, feature detection and description, and motion detection and tracking. It also shows how these modules can be efficiently implemented on hypercube-connected processor networks. The text is intended for both students of vision and vision system designers. It provides a general approach to vision systems design as well as a set of robust, efficient vision modules.

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvi­ ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re­ searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam­ pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy­ sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvi­ ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re­ searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam­ pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy­ sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.

Biological visual systems employ massively parallel processing to perform real-world visual tasks in real time. A key to this remarkable performance seems to be that biological systems construct representations of their visual image data at multiple scales. A Pyramid Framework for Early Vision describes a multiscale, or `pyramid', approach to vision, including its theoretical foundations, a set of pyramid-based modules for image processing, object detection, texture discrimination, contour detection and processing, feature detection and description, and motion detection and tracking. It also shows how these modules can be implemented very efficiently on hypercube-connected processor networks. A Pyramid Framework for Early Vision is intended for both students of vision and vision system designers; it provides a general approach to vision systems design as well as a set of robust, efficient vision modules.

In the past five years, the field of electrostatic discharge (ESD) control has under­ gone some notable changes. Industry standards have multiplied, though not all of these, in our view, are realistic and meaningful. Increasing importance has been ascribed to the Charged Device Model (CDM) versus the Human Body Model (HBM) as a cause of device damage and, presumably, premature (latent) failure. Packaging materials have significantly evolved. Air ionization techniques have improved, and usage has grown. Finally, and importantly, the government has ceased imposing MIL-STD-1686 on all new contracts, leaving companies on their own to formulate an ESD-control policy and write implementing documents. All these changes are dealt with in five new chapters and ten new reprinted papers added to this revised edition of ESD from A to Z. Also, the original chapters have been augmented with new material such as more troubleshooting examples in Chapter 8 and a 20-question multiple-choice test for certifying operators in Chapter 9. More than ever, the book seeks to provide advice, guidance, and practical ex­ amples, not just a jumble of facts and generalizations. For instance, the added tailored versions of the model specifications for ESD-safe handling and packaging are actually in use at medium-sized corporations and could serve as patterns for many readers.

Biological visual systems employ massively parallel processing to perform real-world visual tasks in real time. A key to this remarkable performance seems to be that biological systems construct representations of their visual image data at multiple scales. A Pyramid Framework for Early Vision describes a multiscale, or `pyramid'', approach to vision, including its theoretical foundations, a set of pyramid-based modules for image processing, object detection, texture discrimination, contour detection and processing, feature detection and description, and motion detection and tracking. It also shows how these modules can be implemented very efficiently on hypercube-connected processor networks. A Pyramid Framework for Early Vision is intended for both students of vision and vision system designers; it provides a general approach to vision systems design as well as a set of robust, efficient vision modules.

In the past five years, the field of electrostatic discharge (ESD) control has under­ gone some notable changes. Industry standards have multiplied, though not all of these, in our view, are realistic and meaningful. Increasing importance has been ascribed to the Charged Device Model (CDM) versus the Human Body Model (HBM) as a cause of device damage and, presumably, premature (latent) failure. Packaging materials have significantly evolved. Air ionization techniques have improved, and usage has grown. Finally, and importantly, the government has ceased imposing MIL-STD-1686 on all new contracts, leaving companies on their own to formulate an ESD-control policy and write implementing documents. All these changes are dealt with in five new chapters and ten new reprinted papers added to this revised edition of ESD from A to Z. Also, the original chapters have been augmented with new material such as more troubleshooting examples in Chapter 8 and a 20-question multiple-choice test for certifying operators in Chapter 9. More than ever, the book seeks to provide advice, guidance, and practical ex­ amples, not just a jumble of facts and generalizations. For instance, the added tailored versions of the model specifications for ESD-safe handling and packaging are actually in use at medium-sized corporations and could serve as patterns for many readers.

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvi­ ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re­ searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam­ pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy­ sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.

Human and Machine Vision provides information pertinent to an interdisciplinary program of research in visual perception. This book presents a psychophysical study of the human visual system, which provides insights on how to model the flexibility required by a general-purpose visual system. Organized into 17 chapters, this book begins with an overview of how a visual display is segmented into components on the basis of textual differences. This text then proposes three criteria for judging representations of shape. Other chapters consider an increased use of machine vision programs as models of human vision and of data from human vision in developing programs for machine vision. This book discusses as well the diversity and flexibility of systems for representing visual information. The final chapter deals with dot patterns and discusses the process of interring orientation information from collections of them. This book is a valuable resource for psychologists, neurophysiologists, and computer scientists.

Computer Science and Applied Mathematics: Picture Languages: Formal Models for Picture Recognition treats pictorial pattern recognition from the formal standpoint of automata theory. This book emphasizes the capabilities and relative efficiencies of two types of automata—array automata and cellular array automata, with respect to various array recognition tasks. The array automata are simple processors that perform sequences of operations on arrays, while the cellular array automata are arrays of processors that operate on pictures in a highly parallel fashion, one processor per picture element. This compilation also reviews a collection of results on two-dimensional sequential and parallel array acceptors. Some of the analogous one-dimensional results and array grammars and their relation to acceptors are likewise covered in this text. This publication is suitable for researchers, professionals, and specialists interested in pattern recognition and automata theory.

Image Modeling compiles papers presented at a workshop on image modeling in Rosemont, Illinois on August 6-7, 1979. This book discusses the mosaic models for textures, image segmentation as an estimation problem, and comparative analysis of line-drawing modeling schemes. The statistical models for the image restoration problem, use of Markov random fields as models of texture, and mathematical models of graphics are also elaborated. This text likewise covers the univariate and multivariate random field models for images, stochastic image models generated by random tessellations of the plane, and long crested wave models. Other topics include the Boolean model and random sets, structural basis for image description, and structure in co-occurrence matrices for texture analysis. This publication is useful to specialists and professionals working in the field of image processing.

Perspectives in Computing: Human and Machine Vision II compiles papers presented at the second Workshop on Human and Machine Vision held in Montreal, Canada on August 1-3, 1984. This book discusses the perception of transparency in man and machine, human image understanding, and connectionist models and parallelism in high level vision. The theory of the perceived spatial layout of scenes, generative systems of analyzers, and codon constraints on closed 2D shapes are also elaborated. This text likewise covers the environment- and viewer-centered perception of surface orientation, autonomous scene description with range imagery, and pre-attentive processing in vision. This publication is recommended for students and researchers interested in both fields of visual perception and computer vision.

Digital Picture Processing

The generic term "graph-grammars" refers to a variety of methods for specifying (possibly infinite) sets of graphs or sets of maps. The area of graph-grammars originated in the late 60s motivated by considerations concerning pattern recognition - since then the list of areas which have interacted with the development of graph-grammars has grown quite impressively. It includes pattern recognition, software specification and development, VLSI layout schemes, data bases, lambda-calculus, analysis of concurrent systems, massively parallel computer architectures, incremental compilers, computer animation, complexity theory, developmental biology, music composition, representation of physical solids, and many others. This volume is based on the contributions presented at the third international workshop on graph-grammars and their applications, held in Warrenton, Virginia, USA in December 1986. Aiming at the best possible representation of the field not all of the papers presented at the meeting appear in this volume and some of the papers from this volume were not presented at the workshop. The volume consists of two parts: Part I presents tutorial introductions to a number of basic graph and map rewriting mechanisms. Part II contains technical contributions. This collection of papers provides the reader with an up-to-date overview of current trends in graph-grammars.

TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.

TheInternationalConferenceonMachineLearningandDataMining(MLDM)is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classi?cation and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.

This book constitutes the refereed proceedings of the 6th International Workshop on Structural and Syntactical Pattern Recognition, SSPR '96, held in Leipzig, Germany in August 1996.The 36 revised full papers included together with three invited papers were carefully selected from a total of 52 submissions. The papers are organized in topical sections on grammars and languages; morphology and mathematical approaches to pattern recognition; semantic nets, relational models and graph-based methods; 2D and 3D shape recognition; document image analysis and recognition; and handwritten and printed character recognition.