View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics

Large Scale Surface Reconstruction based on Point Visibility

Katedra kybernetik

Doppler Encoded Excitation Patterning (DEEP) Microscopy

Traditional optical imaging systems rely on lenses and spatially-resolved detection to probe distinct locations on the object. We develop a novel computational approach to 2D and 3D imaging that instead measures the object\u27s spatial Fourier transform using a single-element detector and without requiring precision optics. This wide-field technique can be used to image biological and synthetic structures in fluoresced or scattered light using coherent or broadband illumination. It employs dynamic structured illumination, acousto-optics, RF electronics, and tomographic algorithms to circumvent several trade-offs in conventional imaging, such as the dependence of the optical transfer function on the imaging lenses and the coupling of resolution and depth of field. We use Fourier optics concepts to derive the dynamic optical transfer function, evaluate different Fourier sampling strategies, and investigate and compare tomographic algorithms for 2D and 3D image synthesis. We also develop conceptual and analytical models to describe imaging of fluorescent as well as amplitude and phase scattering objects, the effects of broadband and spatially-incoherent illumination, and nonlinear wide-field super-resolution imaging. We consider sources of noise, analyze and simulate SNR behavior for several types of noise and Fourier sampling strategies, and compare the sensitivity of the technique to conventional imaging. We describe several experimental proof-of-concept systems and present two-dimensional high-resolution tomographic image reconstructions in both scattered and fluoresced light demonstrating a thousandfold improvement in the depth of field compared to conventional lens-based microscopy. Finally, we explore approaches for high-speed Fourier sampling and propose several related sensing techniques, including wide-field fluorescence imaging in scattering media

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Multi-scale multi-dimensional imaging and characterization of oil shale pyrolysis

In recent years, oil shale has attracted renewed attention as an unconventional energy resource, with vast and largely untapped reserves. Oil shale is a fine-grained sedimentary rock containing a sufficiently high content of immature organic matter from which shale oil and combustible gas can be extracted through pyrolysis. Several complex physical and chemical changes occur during the pyrolysis of oil shale where macromolecular network structures of kerogen are thermally decomposed. The pyrolysis of oil shale leads to the formation of a microscopic pore network in which the oil and gas products flow. The pore structure and the connectivity are significant characteristics which determine fluid flow and ultimate hydrocarbon recovery. In this thesis, a state-of-the-art multi-scale multi-dimensional workflow was applied to image and quantify the Lacustrine Eocene Green River (Mahogany Zone) formation, the world’s largest oil shale deposit. Samples were imaged before, during and after pyrolysis using laboratory and synchrotron-based X-ray Micro-tomography (µCT), Optical Microscopy, Automated Ultra-High Resolution Scanning Electron Microscopy (SEM), MAPS Mineralogy (Modular Automated Processing System) and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). Results of image analysis using optical (2-D), SEM (2-D), and µCT (3-D) reveal a complex fine-grained microstructure dominated by organic-rich parallel laminations in a tightly bound heterogeneous mineral matrix. MAPS Mineralogy combined with ultrafast measurements highlighted mineralogic textures dominated by dolomite, calcite, K-feldspar, quartz, pyrite and illitic clays. From high resolution backscattered electron (BSE) images, intra-organic, inter-organic-mineral, intra and inter-mineral pores were characterised with varying sizes and geometries. A detailed X-ray µCT study with increasing pyrolysis temperature (300-500°C) at 12 µm, 2 µm and 0.8 µm voxel sizes illuminated the evolution of pore structure, which is shown to be a strong function of the spatial distribution of organic content. In addition, FIB-SEM 3-D visualisations showed an unconnected pore space of 0.5% with pores sizes between 15 nm and 22 nm for the un-pyrolysed sample and a well-connected pore space of 18.2% largely with pores of equivalent radius between 1.6 µm and 2.0 µm for the pyrolysed sample. Synchrotron 4-D results at a time resolution of 160 seconds and a voxel size of 2 µm revealed a dramatic change in porosity accompanying pyrolysis between 390-400°C with the formation of micron-scale heterogeneous pores followed by interconnected fracture networks predominantly along the organic-rich laminations. Combining these techniques provides a powerful tool for quantifying petrophysical properties before, during and after oil shale pyrolysis. Quantitative 2-D, 3-D and 4-D imaging datasets across nm-µm-mm length scales are of great value to better understand, predict and model dynamics of pore structure change and hydrocarbon transport and production during oil shale pyrolysis.Open Acces

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE