File Name: computational geometry in computer vision and computer graphics .zip
- Computational Geometry
- Computational Geometry
- Computational Geometry and its Application to Computer Graphics
- Computational geometry and computer graphics
VGE Acad. Subject specific learning outcomes and competences. Generic learning outcomes and competences. To get acquainted with the typical problems of computational geometry and existing solutions. To get deeper knowledge of mathematics in relation to computer graphics and to understand the foundations of geometric algebra.
To learn how to apply basic algorithms and methods in this field to problems in computer graphics and machine vision. This course focuses on topics and classical problems which, in small variations, students meet in other courses e.
These topics are rather marginal with respect to the content of these courses, so there is typically not enough time to discuss them in more detail. However, their knowledge is needed in practice. Prerequisite kwnowledge and skills. Syllabus - others, projects and individual work of students.
Course inclusion in study plans. Deputy Guarantor. Language of instruction. Examination written. Time span. Assessment points. Student will get acquaint with the typical problems of computational geometry. Student will understand the existing solutions and their applications in computer graphics and machine vision. Student will get deeper knowledge of mathematics. Student will learn the principles of geometric algebra including its application in graphics and vision related tasks.
Student will learn terminology in English language. Student will also improve his programming skills and his knowledge of development tools. Learning objectives. Why is the course taught. Basic knowledge of linear algebra and geometry. Good knowledge of computer graphics principles. Good knowledge of basic abstract data types and fundamental algorithms. Study literature. Csaba D.
Fundamental literature. Syllabus of lectures. Introduction to computational geometry: typical problems in computer graphics and machine vision, algorithm complexity and robustness, numerical precision and stability. Overview of linear algebra and geometry, coordinate systems, homogeneous coordinates, affine and projective geometry. An example from 3D vision. Range searching and space partitioning methods: range tree; quad tree, k-d tree, BSP tree.
Applications in machine vision. Coordinate systems and homogeneous coordinates. Applications in computer graphics. Collision detection and the algorithm GJK. Proximity problem: closest pair; nearest neighbor; Voronoi diagrams. Affine and projective geometry. Epipolar geometry. Applications in 3D machine vision. Principle of duality and its applications. Surface reconstruction from point clouds and volumetric data.
Surface simplification, mesh smoothing and re-meshing. Basics and of geometric algebra. More computational geometry problems and modern trends.
Linear programming: basic notion and applications; half-plane intersection. Team or individually assigned projects. Progress assessment. Preparing for lectures readings : up to 18 points Realized and defended project: up to 31 points Written final exam: up to 51 points Minimum for final written examination is 17 points. Minimum to pass the course according to the ECTS assessment - 50 points.
Controlled instruction. The evaluation includes mid-term test, individual project, and the final exam.
VGE Acad. Subject specific learning outcomes and competences. Generic learning outcomes and competences. To get acquainted with the typical problems of computational geometry and existing solutions. To get deeper knowledge of mathematics in relation to computer graphics and to understand the foundations of geometric algebra. To learn how to apply basic algorithms and methods in this field to problems in computer graphics and machine vision. This course focuses on topics and classical problems which, in small variations, students meet in other courses e.
Help Advanced Search. We gratefully acknowledge support from the Simons Foundation and member institutions. CG Help Advanced Search. Computational Geometry Authors and titles for cs. Subjects: Computational Geometry cs.
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering , it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring , processing , analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images.
As this was happening, Baumgart developed the winged edge data structure [Ba] to support his research in computer vision. This structure would prove useful as a.
Computational Geometry and its Application to Computer Graphics
Advances in Computer Graphics V pp Cite as. The area of computational geometry deals with the study of algorithms for problems concerning geometric objects like e. Since its introduction in by Shamos the field has developed rapidly and nowadays there are special conferences and journals devoted to the topic. Unable to display preview. Download preview PDF.
The fields of graphics, vision and imaging increasingly rely on one another. This unique and timely MSc provides training in computer graphics, geometry processing, virtual reality, machine vision and imaging technology from world-leading experts, enabling students to specialise in any of these areas and gain a grounding in the others. Note on fees: The tuition fees shown are for the year indicated above.
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Computational geometry and computer graphics
The goal of this tutorial is to introduce Computational Geometry tools and highlight their potential in Computer Vision. Computational Geometry is a branch of Computer Science devoted to the study of geometric algorithms and data structures, has been used successfully in many fields including CAD, Computer Vision and Graphics. The proposed educational course will comprehensively review the Computational Geometry tools that complement and leverage the OpenCV library. The most relevant and important applications for the CVPR community are superpixel segmentations and graph reconstructions.
Computational geometry considers problems with geometric input, and its goal is to design efficient algorithms and to study the computational complexity of such problems. A typical input to a problem is some set of points or segments in the Euclidean plane or higher dimensional Euclidean space. Examples of problems include computing the convex hull of the point set, finding clusters, or setting up a data structure to find the nearest point to a given query point. Although not the focus of this course, there is a very rich application domain, including computer graphics, computer-aided design and manufacturing, machine learning, robotics, geographic information systems, computer vision, integrated circuit design, and many other fields. The course introduces the most important tools used in the design of computational geomtric algorithms. The larger part of the course will deal with problems that can be solved exacly in near-linear time, which are practically solvable even on very large inputs. Deutsch Location Press.
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