This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.

Viele Anwendungen des Maschinellen Sehens benötigen die automatische Analyse und Rekonstruktion von statischen und dynamischen Szenen. Deshalb ist die automatische Analyse von dreidimensionalen Szenen und Objekten ein Bereich der intensiv erforscht wird. Die meisten Ansätze konzentrieren sich auf die Rekonstruktion statischer Szenen, da die Rekonstruktion nicht-statischer Geometrien viel herausfordernder ist und voraussetzt, dass dreidimensionale Szeneninformation mit hoher zeitlicher Auflösung verfügbar ist. Statische Szenenanalyse wird beispielsweise in der autonomen Navigation, für die Überwachung und für die Erhaltung des Kulturerbes eingesetzt. Andererseits eröffnet die Analyse und Rekonstruktion nicht-statischer Geometrie viel mehr Möglichkeiten, nicht nur für die bereits erwähnten Anwendungen. In der Produktion von Medieninhalten für Film und Fernsehen kann die Analyse und die Aufnahme und Wiedergabe von vollständig dreidimensionalen Inhalten verwendet werden um neue Ansichten realer Szenen zu erzeugen oder echte Schauspieler durch animierte virtuelle Charaktere zu ersetzen. Die wichtigste Voraussetzung für die Analyse von dynamischen Inhalten ist die Verfügbarkeit von zuverlässigen dreidimensionalen Szeneninformationen. Um die Entfernung von Punkten in der Szene zu bestimmen wurden meistens Stereo-Verfahren eingesetzt, aber diese Verfahren benötigen viel Rechenzeit und erreichen in Echtzeit nicht die benötigte Qualität. In den letzten Jahren haben die so genannten Laufzeitkameras das Stadium der Prototypen verlassen und sind jetzt in der Lage dichte Tiefeninformationen in vernünftiger Qualität zu einem vernünftigen Preis zu liefern. Diese Arbeit untersucht die Eignung dieser Kameras für die Analyse nicht-statischer dreidimensionaler Szenen. Bevor eine Laufzeitkamera für die Analyse eingesetzt werden kann muss sie intern und extern kalibriert werden. Darüber hinaus leiden Laufzeitkameras an systematischen Fehlern bei der Entfernungsmessung, bedingt durch ihr

The study of three-dimensional reconstruction of objects and scenes has been and remains now a widely researched topic. It has been investigated for many applications, for instance, video game development, animation, movies, virtual reality, teleoperating surgery, among other engineering-related applications. In this book, Chapter One reviews preoperative planning and intraoperative navigation based on 3D modeling for retroperitoneal procedures. Chapter Two discusses fringe pattern analysis using phase shifting techniques applied to solid digitalization in Advanced RISC Machine (ARM) architecture. Chapter Three describes the steps for 3D digitalization using a Fourier Transform Profilometry (FTP). Chapter Four compares different wavelet transform for its use in 3D reconstruction.

he problem of analyzing sequences of images to extract three-dimensional T motion and structure has been at the heart of the research in computer vi sion for many years. It is very important since its success or failure will determine whether or not vision can be used as a sensory process in reactive systems. The considerable research interest in this field has been motivated at least by the following two points: 1. The redundancy of information contained in time-varying images can over come several difficulties encountered in interpreting a single image. 2. There are a lot of important applications including automatic vehicle driv ing, traffic control, aerial surveillance, medical inspection and global model construction. However, there are many new problems which should be solved: how to effi ciently process the abundant information contained in time-varying images, how to model the change between images, how to model the uncertainty inherently associated with the imaging system and how to solve inverse problems which are generally ill-posed. There are of course many possibilities for attacking these problems and many more remain to be explored. We discuss a few of them in this book based on work carried out during the last five years in the Computer Vision and Robotics Group at INRIA (Institut National de Recherche en Informatique et en Automatique).

Vladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM with shape priors obtained on-the-fly from several unoccluded frames of the sequence and the new gravitational class of methods for point set alignment represent the primary contributions of this book. About the Author: Vladislav Golyanik is currently a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. The current focus of his research lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of human body and matching problems on point sets and graphs. He is interested in machine learning (both supervised and unsupervised), physics-based methods as well as new hardware and sensors for computer vision and graphics (e.g., quantum computers and event cameras).

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

This book constitutes the thoroughly refereed proceedings of the 7th International Conference, ICIAR 2010, held in Póvoa de Varzin, Portugal in June 2010. The 88 revised full papers were selected from 164 submissions. The papers are organized in topical sections on Image Morphology, Enhancement and Restoration, Image Segmentation, Featue Extraction and Pattern Recognition, Computer Vision, Shape, Texture and Motion Analysis, Coding, Indexing, and Retrieval, Face Detection and Recognition, Biomedical Image Analysis, Biometrics and Applications.