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1.0 2 The attention then turned to the problem of "Body separation", i.e. separation of occluding bodies in a scene (See [Guzman), [Falk), and [Waltz)). Grape ([Grape)) combined the separation of bodies with recognition, by removing parts of the scene recognized as belonging to a known body. All of these techniques were designed to work with polyhedral objects only, and extensively use the properties of edges and vertices. Though some impressive results have been reported ([Waltz], [Grape)), and perhaps some useful abstractions can be made, the specific techniques used fail to generalize to a wider class of objects. Among previous work on curved objects, B.K.P. Horn ([Horn)) presented techniques for extracting three dimensional depth data from a TV image, using reflection characteristics of the surface. Krakauer ([Krakauer]) represented objects by connections of brightness contours. Ambler et al ([Ambler)) describe experiments with simple shapes, including curved objects, using relations within a two-dimensional image. However, none of these efforts really addresses the problem of "shape" representation and description. Work on outdoor scene analysis is also concerned with non-polyhedral objects ([Bajcsy], [Yakimovsky]), but again no attention has been paid to shape analysis.
This volume contains the proceedings of the NATO Advanced Study Institute on "Image Sequence Processing and Dynamic Scene Analysis" held 21 June - 2 July, 1982 in Hotel Maritim, Braunlage/Harz, Federal Republic of Germany. The organizing eommittee of the institute consists of T.S. Huang (Director), H.G. Musmann (Co Director), H.H. Nagel (Consultant), and C.E. Liedtke and W. Geuen (Local 'arrangement). This Institute was devoted to the rapidly emerging field of image sequence processing and dynamic scene analysis which has man! important applications in cluding target tracking, television bandwidth compression, highway traffic moni toring, and analysis of heart wall motion for medical diagnosis. The lectures and discussions in this Institute fell into three overlapping categories: Motion estimation; pattern recognition and artificial intelligence techniques in dynamic scene analysis; and, applications. 1) Motion estimation - One of the most important problems in image sequence analysis and dynamic scene analysis is displacement and motion estimation. For example, in interframe coding using temporal DPCM, displacement estimation and compensation can improve efficiency significantly. Also, estimated motion parameters can be powerful cues in target segmentation, detection, and classification. In this Institute, a number of recently developed techniques for displacement and motion estimation were discussed.
The field of computer vision combines techniques from physics, mathematics, psychology, artificial intelligence, and computer science to examine how machines might construct meaningful descriptions of their surrounding environment. The editors of this volume, prominent researchers and leaders of the SRI International AI Center Perception Group, have selected sixty papers, most published since 1980, with the viewpoint that computer vision is concerned with solving seven basic problems: - Reconstructing 3D scenes from 2D images - Decomposing images into their component parts - Recognizing and assigning labels to scene objects - Deducing and describing relations among scene objects - Determining the nature of computer architectures that can support the visual function - Representing abstractions in the world of computer memory - Matching stored descriptions to image representation Each chapter of this volume addresses one of these problems through an introductory discussion, which identifies major ideas and summarizes approaches, and through reprints of key research papers. Two appendices on crucial assumptions in image interpretation and on parallel architectures for vision applications, a glossary of technical terms, and a comprehensive bibliography and index complete the volume.
This work provides an introduction to the foundations of three-dimensional c- puter vision and describes recent contributions to the ?eld, which are of methodical and application-speci?c nature. Each chapter of this work provides an extensive overview of the corresponding state of the art, into which a detailed description of new methods or evaluation results in application-speci?c systems is embedded. Geometric approaches to three-dimensional scene reconstruction (cf. Chapter 1) are primarily based on the concept of bundle adjustment, which has been developed more than 100 years ago in the domain of photogrammetry. The three-dimensional scene structure and the intrinsic and extrinsic camera parameters are determined such that the Euclidean backprojection error in the image plane is minimised, u- ally relying on a nonlinear optimisation procedure. In the ?eld of computer vision, an alternative framework based on projective geometry has emerged during the last two decades, which allows to use linear algebra techniques for three-dimensional scene reconstructionand camera calibration purposes. With special emphasis on the problems of stereo image analysis and camera calibration, these fairly different - proaches are related to each other in the presented work, and their advantages and drawbacks are stated. In this context, various state-of-the-artcamera calibration and self-calibration methods as well as recent contributions towards automated camera calibration systems are described. An overview of classical and new feature-based, correlation-based, dense, and spatio-temporal methods for establishing point c- respondences between pairs of stereo images is given.
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.
This thesis is dedicated to the problem of object recognition in the three-dimensional space. Instead of using exclusively the information typically transported by a two-dimensional image, the concept of this work additionally incorporates the third dimension, namely the depth. The depth data itself is captured by sensors capable of measuring the distance from the device's position to those objects residing inside its field of view. The actual recognition process is implemented in analogy to the Path Similarity Skeleton Graph Matching (PSSGM). Basically, this method represents a 2D object by its skeleton and uses the idea of shortest paths to describe it. Finally, the similarity between two objects is calculated based on the Hungarian method. The contribution of the current work maps this approach into the three-dimensional space and applies it to 3D objects. While one of the experiments aims at the recognition of 3D chairs and tables, another one is devoted to the registration of fully segmented vascular structures. Excellent and promising recognition results are achieved in challenging evaluation setups showing that the 3D version of the PSSGM has the potential to solve complex recognition tasks.
Techniques for 3-D Machine Perception
"The Encyclopedia of Microcomputers serves as the ideal companion reference to the popular Encyclopedia of Computer Science and Technology. Now in its 10th year of publication, this timely reference work details the broad spectrum of microcomputer technology, including microcomputer history; explains and illustrates the use of microcomputers throughout academe, business, government, and society in general; and assesses the future impact of this rapidly changing technology."
The purpose of computer vision is to make computers capable of understanding environments from visual information. Computer vision has been an interesting theme in the field of artificial intelligence. It involves a variety of intelligent information processing: both pattern processing for extraction of meaningful symbols from visual information and symbol processing for determining what the symbols represent. The term "3D computer vision" is used if visual information has to be interpreted as three-dimensional scenes. 3D computer vision is more challenging because objects are seen from limited directions and some objects are occluded by others. In 1980, the author wrote a book "Computer Vision" in Japanese to introduce an interesting new approach to visual information processing developed so far. Since then computer vision has made remarkable progress: various rangefinders have become available, new methods have been developed to obtain 3D informa tion, knowledge representation frameworks have been proposed, geometric models which were developed in CAD/CAM have been used for computer vision, and so on. The progress in computer vision technology has made it possible to understand more complex 3 D scenes. There is an increasing demand for 3D computer vision. In factories, for example, automatic assembly and inspection can be realized with fewer con straints than conventional ones which employ two-dimensional computer vision.
This book contains the 61 papers that were accepted for presenta tion at the 1992 British Machine Vision Conference. Together they provide a snapshot of current machine vision research throughout the UK in 24 different institutions. There are also several papers from vision groups in the rest of Europe, North America and Australia. At the start of the book is an invited paper from the first keynote speaker, Robert Haralick. The quality of papers submitted to the conference was very high and the programme committee had a hard task selecting around half for presentation at the meeting and inclusion in these proceedings. It is a positive feature of the annual BMV A conference that the entire process from the submission deadline through to the conference itself and publication of the proceedings is completed in under 5 months. My thanks to members of the programme committee for their essential contribution to the success of the conference and to Roger Boyle, Charlie Brown, Nick Efford and Sue Nemes for their excellent local organisation and administration of the conference at the University of Leeds.