Showing papers on "Local feature size published in 1997"

Computational Geometry: Algorithms and Applications

Abstract: This introduction to computational geometry focuses on algorithms. Motivation is provided from the application areas as all techniques are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement.

Color Printer Characterization Using a Computational Geometry Approach.

Abstract: We propose a method for the colorimetric characterization of a printer which can also be applied to any other type of digital image reproduction device. The method is based on a computational geometry approach. It uses a 3D triangulation technique to build a tetrahedral partition of th e printer color gamut volume and it generates a surrounding structure enclosing the definition domain. The characterization provides the inverse transformation from the device independent color space CIELAB to the device-dependent color space CMY, taking into account both colorimetric properties of the printer, and color gamut mapping.

Designing a Computational Geometry Algorithms Library

Abstract: Geometric problems arise in many areas. Computer graphics, robotics, manufacturing, and geographic information systems are some examples. Often the same geometric subproblems are to be solved. Hence a library providing solutions for core problems in geometric computing has a wide range of applications and can be very useful. The success of LEDA [16, 19], a library of e cient data types and algorithms, has shown that the existence of a library can make a tremendous di erence for taking advanced techniques in data structures and algorithms from theory to practice. The eld of computational geometry is now very close to a state where it can provide such a library of geometric algorithms. Over the past twenty years many algorithms for geometric problems have been developed by computational geometers. Many of these algorithms clearly have no direct impact for geometric computing in practice, because they are e cient compared to other solutions only for huge problem instances. They are mainly to be considered as contributions to the investigation of the complexity of a geometric problem. Many other algorithms are practical for reasonable problem sizes, but haven't found their way into practice yet. Among the reasons for this are the dissimilarity between fast oating-point arithmetic normally used in practice and exact arithmetic over the real numbers assumed in theoretical papers, the lack of explicit handling of degenerate cases in these papers, and the inherent complexity of many e cient solutions. For these reasons there is a de nite need for correct and e cient implementations of geometric algorithms. Although much progress has been made concerning the implementation of geometric algorithms, see e.g. [15], there is still a lot of theoretical and experimental research to be done to get a robust and e cient library of geometric algorithms. It is one of the goals of the CGAL-project, a common project of the sites Utrecht University (The Netherlands), ETH Z urich (Switzerland), Free University Berlin (Germany), INRIA Sophia-Antipolis (France), Max-Planck-Institute Saarbr ucken (Germany), RISC Linz (Austria), and Tel-Aviv University (Israel), to successfully do this research and to provide reliable and e cient implementations in a library of computational geometry algorithms. The library will be constructed in cooperation with ten industrial companies (mainly) in Europe. Just as the project the library will be called CGAL. In these notes some issues related to the design of a computational geometry algorithms library are discussed. Section 2 contains general remarks on libraries and geometric computing. In Section 3 it is argued that exact geometric computation is the most promising approach to ensure robustness in a geometric algorithms library. Sections 4 and 5 relate modularity to e ciency and generality and to generic code. Finally, the ease of use of a library is discussed in Section 6. The view held in these notes is a personal view, not the o cial view of CGAL. However, many of the presented concepts have been developed jointly in the kernel design group of CGAL [9]. They are also in uenced by discussions in the research group interested in exact geometric

Measuring geometric complexity of 3d models for feature recognition

Abstract: Geometric Feature Recognition in Manufacturing is a large field of research in which many different methods and solutions have been proposed, and yet no single agreed definition of a feature exists. Moreover, no measure of how complex a manufacturable component is, in terms of features, has been developed. This paper presents a Feature Complexity Index, the aim of which is to encapsulate generic feature concepts without explicit feature definitions and thereby quantify a concept which human intuition understands effortlessly. One possible application of the index is to compare the efficacy of the wide range of disparate feature recognition systems currently being developed.