Computer Vision Three Dimensional Reconstruction Techniques
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Computer Vision: Three-dimensional Reconstruction Techniques
From facial recognition to self-driving cars, the applications of computer vision are vast and ever-expanding. Geometry plays a fundamental role in this discipline, providing the necessary mathematical framework to understand the underlying principles of how we perceive and interpret visual information in the world around us. This text explores the theories and computational techniques used to determine the geometric properties of solid objects through images. It covers the basic concepts and provides the necessary mathematical background for more advanced studies. The book is divided into clear and concise chapters covering a wide range of topics including image formation, camera models, feature detection and 3D reconstruction. Each chapter includes detailed explanations of the theory as well as practical examples to help the reader understand and apply the concepts presented. The book has been written with the intention of being used as a primary resource for students on university courses in computer vision, particularly final year undergraduate or postgraduate computer science or engineering courses. It is also useful for self-study and for those who, outside the academic field, find themselves applying computer vision to solve practical problems. The aim of the book is to strike a balance between the complexity of the theory and its practical applicability in terms of implementation. Rather than providing a comprehensive overview of the current state of the art, it offers a selection of specific methods with enough detail to enable the reader to implement them.
Multi View Three Dimensional Reconstruction
Author: Fouad Sabry
language: en
Publisher: One Billion Knowledgeable
Release Date: 2024-05-14
What is Multi View Three Dimensional Reconstruction 3D reconstruction from multiple images is the creation of three-dimensional models from a set of images. It is the reverse process of obtaining 2D images from 3D scenes. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: 3D Reconstruction from Multiple Images Chapter 2: Fundamental Matrix (Computer Vision) Chapter 3: Triangulation (Computer Vision) Chapter 4: Perspective-n-Point Chapter 5: Image Stitching Chapter 6: Active Contour Model Chapter 7: Bundle Adjustment Chapter 8: Scale-Invariant Feature Transform Chapter 9: 3D Object Recognition Chapter 10: Camera Auto-Calibration (II) Answering the public top questions about multi view three dimensional reconstruction. (III) Real world examples for the usage of multi view three dimensional reconstruction in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Multi View Three Dimensional Reconstruction.
3D Computer Vision
Author: Christian Wöhler
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-07-28
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.