Performance improvement of a 3-D configuration reconstruction algorithm using a passive secondary target has been focused in this study. In earlier studies, a theoretical development of the 3-D configuration reconstruction algorithm was achieved and it was implemented by a computer program on a system consisting of an optical bench and a digital imaging system. The passive secondary target used was a circle with two internal spots. In order to use this reconstruction algorithm in autonomous systems, an automatic target recognition algorithm has been developed in this study. Starting from a pre-captured and stored 8-bit gray-level image, the algorithm automatically detects the elliptical image of a circular target and determines its contour in the scene. It was shown that the algorithm can also be used for partially captured elliptical images. Another improvement achieved in this study is the determination of internal camera parameters of the vision system.

Inhaltsangabe:Introduction: Reconstruction of real-world scenes from a set of multiple images is a topic in Computer Vision and 3D Computer Graphics with many interesting applications. There is a relation to Augmented and Mixed Reality (AR/MR), Computer-Supported Collaborative Work (CSCW), Computer-Aided industrial/architectural Design (CAD), modeling of the real-world (e.g. computer games, scenes/effects in movies), entertainment (e.g. 3D TV/Video) and recognition/analyzing of real-world characteristics by computer systems and robots. There exists a powerful algorithm theory for shape reconstruction from arbitrary viewpoints, called shape from photo-consistency. However, it is computationally expensive and hence can not be used with applications in the field of 3D video or CSCW as well as interactive 3D model creation. Attempts have been made to achieve real-time framerates using PC cluster systems. While these provide enough performance they are also expensive and less flexible. Approaches that use GPU hardware-acceleration on single workstations achieve interactive framerates for novel-view synthesis, but do not provide an explicit volumetric representation of the whole scene. The proposed approach shows the efforts in developing a GPU hardware-accelerated framework for obtaining the volumetric photo hull of a dynamic 3D scene as seen from multiple calibrated cameras. High performance is achieved by employing a shape from silhouette technique in advance to obtain a tight initial volume for shape from photo-consistency. Also several speed-up techniques are presented to increase efficiency. Since the entire processing is done on a single PC, the framework can be applied to mobile setups, enabling a wide range of further applications. The approach is explained using programmable vertex and fragment processors and compared to highly optimized CPU implementations. It is shown that the new approach can outperform the latter by more than one magnitude. The thesis is organized as follows: Chapter 1 contains an introduction, giving an overview with classification of related techniques, statement of the main problem, novelty of the proposed approach and its fields of application. Chapter 2 surveys related work in the area of dynamic scene reconstruction by shape from silhouette and shape from photo-consistency. The focus lies on high performance reconstruction and hardware-acceleration. Chapter 3 introduces the theoretical basis for the proposed [...]

"This book provides developers and scholars with an extensive collection of research articles in the expanding field of 3D reconstruction, investigating the concepts, methodologies, applications and recent developments in the field of 3D reconstruction"--

Compiled with the help of an internationally acclaimed panel of experts, the Ocean Engineering Handbook is the most complete reference available for professionals. It offers you comprehensive coverage of important areas of the theory and practice of oceanic/coastal engineering and technology. This well organized text includes five major sections: M

This book provides an overview of constructing advanced Autonomous Driving Maps. It includes coverage of such methods as: fusion target perception (based on vehicle vision and millimeter wave radar), cross-field of view object perception, vehicle motion recognition (based on vehicle road fusion information), vehicle trajectory prediction (based on improved hybrid neural network) and the driving map construction method driven by road perception fusion. An Autonomous Driving Map is used for optimization of not only for a single vehicle, but also for the entire traffic system.

One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions

"Deep Learning-Based Single View 3D Reconstruction" is a research area that focuses on creating three-dimensional (3D) models of objects from a single two-dimensional (2D) image using deep learning algorithms. This technique has the potential to revolutionize fields such as computer vision, robotics, and virtual reality. The process of reconstructing 3D models from a single image involves several steps, including feature extraction, camera pose estimation, and depth estimation. Deep learning techniques, particularly convolutional neural networks (CNNs), have shown great promise in improving the accuracy and efficiency of these steps. The use of deep learning in single view 3D reconstruction has several advantages over traditional methods, including the ability to learn and generalize complex features and the ability to handle noisy or incomplete data. Additionally, deep learning techniques can be trained on large datasets, allowing for better generalization and improved performance. Henry Smith is not a known author in this field of study, but there are many researchers and experts working on this topic, including David Novotny, Jiri Sedlar, Andrea Vedaldi, and Kostas Daniilidis, among others.