Digitizing objects and environments from real world has become an important part of creating realistic computer graphics. Through the use of structured lighting and laser time-of-flight measurements the capturing of geometric models is now a common process. The result are visualizations where viewers gain new possibilities for both visual and intellectual experiences. This thesis presents the reconstruction of the Parthenon temple and its environment in Athens, Greece by using a 3D laser-scanning technique. In order to reconstruct a realistic model using 3D scanning techniques there are various phases in which the acquired datasets have to be processed. The data has to be organized, registered and integrated in addition to pre and post processing. This thesis describes the development of a suitable and efficient data processing pipeline for the given data. The approach differs from previous scanning projects considering digitizing this large scale object at very high resolution. In particular the issue managing and processing huge datasets is described. Finally, the processing of the datasets in the different phases and the resulting 3D model of the Parthenon is presented and evaluated.

With arrival of affordable digital technology we are increasingly coming into contact with digital images. Cameras are no longer dedicated devices, but part of almost every mobile phone, PDA and laptop. This paper discusses methods for enhancing quality of digital images with focus on removing noise, creating high dynamic range (HDR) images and extending depth of field (DOF). It contains familiarization with technical means for acquiring digital image, explains origin of image noise. Further attention is drawn to HDR, from explaining the term, physical basis, difference between HDR sensing and HDR displaying, to survey and historical development of methods dealing with creating HDR images. The next part is explaining DOF when displaying, physical basis of this phenomenon and review of methods used for DOF extension. The paper mentions problem of acquiring images needed for solving given tasks and designs method for acquiring images. Using it a database of test images for each task was created. Part of the paper also deals with design of a program, that implements discussed methods, for solving the given tasks. With help of proposed class imgmap, quality of output images is improved, by modifying maps of input images. The paper describes methods, improvements, means of setting parameters and their effects on algorithms and control of program using proposed GUI. Finally, comparison with free software for extending DOF takes place. The proposed software provides at least comparable results, the correct setting of parameters for specific cases allows to achieve better properties of the resulting image. Time requirements of image processing are worse because designed software was not optimised.

LADAR (Laser Detection and Ranging) systems constitue a direct extension of the conventional radar techniques. Because they operate at much shorter wavelengths, LADARs have the unique capability to generate 3D images of objects. These laser systems have many applications in both the civilian and the defence fields concerning target detection and identification. The extraction of these features depends on the processing algorithms, target properties and 3D images quality. In order to support future LADAR hardware device developments and system engineering studies, it is necessary to understand the influences of the phenomena leading to the final image. Hence, the modelling of the laser pulse, propagations effects, reflection properties, detection technique and receiver signal processinghave to be taken into account. A complete simulator has been developed consisting of a graphical user interface and a simulation program. The computer simulation produces simulated 3D images for a direct detection pulse LADAR under a wide variety of conditions. Each stage from the laser source to the 3D image generation has been modelled. It yields an efficient simulation tool which will be of help in the design of the future LADAR systems and gauge their performances. This master’s thesis contains the theoretical background about laser used to build the simulation program. The latter is described schematically in order to provide an insight for the reader. The graphical interface is then presented as a short user’s manual. Finally, in order to illustrate the possibilities of the simulator, a collection of selected simulations concludes the report.

This dissertation focuses on the many issues that arise from the visual rendering problem. Of primary consideration is light transport simulation, which is known to be computationally expensive. Monte Carlo methods represent a simple and general class of algorithms often used for light transport computation. Unfortunately, the images resulting from Monte Carlo approaches generally suffer from visually unacceptable noise artifacts. The result of any light transport simulation is, by its very nature, an image of high dynamic range (HDR). This leads to the issues of the display of such images on conventional low dynamic range devices and the development of data compression algorithms to store and recover the corresponding large amounts of detail found in HDR images. This dissertation presents our contributions relevant to these issues. Our contributions to high dynamic range image processing include tone mapping and data compression algorithms. This research proposes and shows the efficacy of a novel level set based tone mapping method that preserves visual details in the display of high dynamic range images on low dynamic range display devices. The level set method is used to extract the high frequency information from HDR images. The details are then added to the range compressed low frequency information to reconstruct a visually accurate low dynamic range version of the image. Additional challenges associated with high dynamic range images include the requirements to reduce excessively large amounts of storage and transmission time. To alleviate these problems, this research presents two methods for efficient high dynamic range image data compression. One is based on the classical JPEG compression. It first converts the raw image into RGBE representation, and then sends the color base and common exponent to classical discrete cosine transform based compression and lossless compression, respectively. The other is based on the wavelet transformation. It first transforms the raw image data into the logarithmic domain, then quantizes the logarithmic data into the integer domain, and finally applies the wavelet based JPEG2000 encoder for entropy compression and bit stream truncation to meet the desired bit rate requirement. We believe that these and similar such contributions will make a wide application of high dynamic range images possible. The contributions to light transport simulation include Monte Carlo noise reduction, dynamic object rendering and complex scene rendering. Monte Carlo noise is an inescapable artifact in synthetic images rendered using stochastic algorithm. This dissertation proposes two noise reduction algorithms to obtain high quality synthetic images. The first one models the distribution of noise in the wavelet domain using a Laplacian function, and then suppresses the noise using a Bayesian method. The other extends the bilateral filtering method to reduce all types of Monte Carlo noise in a unified way. All our methods reduce Monte Carlo noise effectively. Rendering of dynamic objects adds more dimension to the expensive light transport simulation issue. This dissertation presents a pre-computation based method. It pre-computes the surface radiance for each basis lighting and animation key frame, and then renders the objects by synthesizing the pre-computed data in real-time. Realistic rendering of complex scenes is computationally expensive. This research proposes a novel 3D space subdivision method, which leads to a new rendering framework. The light is first distributed to each local region to form local light fields, which are then used to illuminate the local scenes. The method allows us to render complex scenes at interactive frame rates. Rendering has important applications in mixed reality. Consistent lighting and shadows between real scenes and virtual scenes are important features of visual integration. The dissertation proposes to render the virtual objects by irradiance rendering using live captured environmental lighting. This research also introduces a virtual shadow generation method that computes shadows cast by virtual objects to the real background. We finally conclude the dissertation by discussing a number of future directions for rendering research, and presenting our proposed approaches.

Les techniques de description des éléments caractéristiques d’une image sont omniprésentes dans de nombreuses applications de vision par ordinateur. Nous proposons à travers ce manuscrit une extension, pour décrire (représenter) et apparier les éléments caractéristiques des images. L’extension proposée consiste en une approche originale pour apprendre, ou estimer, la présence sémantique des éléments caractéristiques locaux dans les images. L’information sémantique obtenue est ensuite exploitée, en conjonction avec le paradigme de sac-de-mots, pour construire un descripteur d’image performant. Le descripteur résultant, est la combinaison de deux types d’informations, locale et contextuelle-sémantique. L’approche proposée peut être généralisée et adaptée à n’importe quel descripteur local d’image, pour améliorer fortement ses performances spécialement quand l’image est soumise à des conditions d’imagerie contraintes. La performance de l’approche proposée est évaluée avec des images réelles aussi bien dans les deux domaines, 2D que 3D. Nous avons abordé dans le domaine 2D, un problème lié à l’appariement des éléments caractéristiques dans des images. Dans le domaine 3D, nous avons résolu les problèmes d’appariement et alignement des vues partielles tridimensionnelles. Les résultats obtenus ont montré qu’avec notre approche, les performances sont nettement meilleures par rapport aux autres méthodes existantes. / This manuscript presents an extension of feature description and matching strategies by proposing an original approach to learn the semantic information of local features. This semantic is then exploited, in conjunction with the bag-of-words paradigm, to build a powerful feature descriptor. The approach, ended up by combining local and context information into a single descriptor, is also a generalized method for improving the performance of the local features, in terms of distinctiveness and robustness under geometric image transformations and imaging conditions. The performance of the proposed approach is evaluated on real world data sets as well as in both the 2D and 3D domains. The 2D domain application addresses the problem of image feature matching while in 3D domain, we resolve the issue of matching and alignment of multiple range images. The evaluation results showed our approach performs significantly better than expected results as well as in comparison with other methods.

We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-09-01 11:07:32.084