An image-based technique for passive acquisition of three-dimensional (3-D) range data is proposed. The distance is extracted, in this technique, from the estimation of focus conditions on images produced through a monocular imaging system under natural illumination. The image taken from a 3-D object is generally out-of-focus (defocused). For each surface point, the severity of defocus on the image depends upon how far away the point is from the imaging system and how camera (optical) parameters are adjusted. Each setting of the parameters can be recorded physically, and associated in object-space with the inverse of a distance that corresponds to the position for the sharpest imaging under this setting. Therefore, for a given surface point the defocus severity is a function of such an inverse object-distance. It can be shown that this function is symmetrical to, and monotonic on both sides of, a point corresponding to the inverse distance of the surface point. To estimate the parameters of the function (one of which is the inverse distance of the surface point), 3~4 images need to be taken under different camera settings with known associated inverse distances in object-space, determined through a once-for-all calibration procedure. Defocus severity is evaluated from a calculation on the window image that corresponds to a small area around the surface point, and the inverse variance in the window is suggested in this technique for the best performance. The 3-D surface geometry is acquired by applying the algorithm, in parallel, to all surface points in the field of view. Various aspects of the technique are discussed and several algorithms are developed. The technique is implemented on an opto-digital imaging system and evaluated under different conditions. A number of objects are tested to demonstrate its performance. / Thesis / Master of Engineering (ME)

Robotic automation has been a part of state-of-the-art manufacturing for many decades. Robotic manipulators are used for such tasks as welding, painting, pick and place tasks etc. Robotic manipulators are quite flexible and adaptable to new tasks, but a typical robot-based production cell requires extensive specification of the robot motion and construction of tools and fixtures for material handling. This incurs a large effort both in time and monetary expenses. The task of a vision system in this setting is to simplify the control and guidance of the robot and to reduce the need for supporting material handling machinery. This dissertation examines performance and properties of the current state-of-the-art local features within the setting of object pose estimation. This is done through an extensive set of experiments replicating various potential problems to which a vision system in a robotic cell could be subjected. The dissertation presents new local features which are shown to increase the performance of object pose estimation. A new local descriptor details how to use log-polar sampled image patches for truly rotational invariant matching. This representation is also extended to use a scale-space interest point detector which in turn makes it very competitive in our experiments. A number of variations of already available descriptors are constructed resulting in new and competitive features, among them a scale-space based Patch-duplet. In this dissertation a successful vision-based object pose estimation system is extended for multi-cue integration, yielding increased robustness and accuracy. Robustness is increased through algorithmic multi-cue integration, combining the individual strengths of multiple local features. Increased accuracy is achieved by utilizing manipulator movement and applying temporal multi-cue integration. This is implemented using a real flexible robotic manipulator arm. Besides work done on local features for ordinary image data a number of local features for range data has also been developed. This dissertation describes the theory behind and the application of the scene tensor to the problem of object pose estimation. The scene tensor is a fourth order tensor representation using projective geometry. It is shown how to use the scene tensor as a detector as well as how to apply it to the task of object pose estimation. The object pose estimation system is extended to work with 3D data. A novel way of handling sampling of range data when constructing a detector is discussed. A volume rasterization method is presented and the classic Harris detector is adapted to it. Finally, a novel region detector, called Maximally Robust Range Regions, is presented. All developed detectors are compared in a detector repeatability test.

This is a thesis written for a master's degree at the Computer Vision Laboratory, University of Linköping. An abstract outer product is defined and used as a bridge to reach 2:nd and 4:th order tensors. Some applications of these in geometric analysis of range data are discussed and illustrated. In idealized setups, simple geometric objects, like spheres or polygons, are successfully detected. Finally, the generalization to n:th order tensors for storing and analysing geometric information is discussed.

<p>This is a thesis written for a master's degree at the Computer Vision Laboratory, University of Linköping. An abstract outer product is defined and used as a bridge to reach 2:nd and 4:th order tensors. Some applications of these in geometric analysis of range data are discussed and illustrated. In idealized setups, simple geometric objects, like spheres or polygons, are successfully detected. Finally, the generalization to n:th order tensors for storing and analysing geometric information is discussed.</p>

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / NASA and the Air Force are increasing the number of hypersonic and access-to-space programs creating a growing requirement for flight test ranges over large regional areas. A principal challenge facing these extended test ranges is the ability to provide continuous vehicle communications by filling the gaps in coverage between fixed ground stations. Consequently, there is a need for mobile range systems that provide a multitude of communication services under varying circumstances. This paper discusses the functional design and systems capabilities, as well as the mission support criteria, concerning NASA’s Mobile Operations Facility (MOF). The MOF will be deployed to Dugway Proving Grounds (DPG), Utah, in support of the X-33 single-stage-to-orbit (SSTO) demonstrator.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / A system was developed using capabilities from the Range Applications Joint Program Office (RAJPO) GPS tracking system and the ACMI Interface System (ACINTS) to provide tracking data and visual cues to experimenters. The Mobile Advanced Range Data System (ARDS) Control System (MACS) outputs are used to provide research data in support of advanced project studies. Enhanced from a previous system, the MACS expands system capabilities to allow researchers to locate where Digital Terrain Elevation Data (DTED) is available for incorporation into a reference data base. The System Integration Group at Veda Incorporated has been supporting Wright Laboratories in the ground-based tracking and targeting arena since 1989 with the design, development, and integration of four generations of real-time, telemetry-based tracking aids. Commencing in Q3 1995, Veda began developing a mobile, transportable system based on the RAJPO GPS tracking system. The resulting system architecture takes advantage of the front end processor (FEP) used in the three previous generations of interface systems built for Wright Laboratories, thus maximizing hardware and software reuse. The FEP provides a computational interface between the GPS tracking system and the display (operator) system. The end product is a powerful, flexible, fully mobile testbed supporting RDT&E requirements for Wright Laboratories, as well as to other U.S. and foreign research organizations. The system is rapidly reconfigurable to accommodate ground-based tracking systems as well as GPS-based systems, and its capabilities can be extended to include support for mission planning tools, insertion of virtual participants such as DIS entities, and detailed post-mission analysis.

As the requirement for more realistic 3D environments is pushed forward by the computer {graphics | movie | simulation | games} industry, attention turns away from the creation of purely synthetic, artist derived environments towards the use of real world captures from the 3D world in which we live. However, common 3D acquisition techniques, such as laser scanning and stereo capture, are realistically only 2.5D in nature - such that the backs and occluded portions of objects cannot be realised from a single uni-directional viewpoint. Although multi-directional capture has existed for sometime, this incurs additional temporal and computational cost with no existing guarantee that the resulting acquisition will be free of minor holes, missing surfaces and alike. Drawing inspiration from the study of human abilities in 3D visual completion, we consider the automated completion of these hidden or missing portions in 3D scenes originally acquired from 2.5D (or 3D) capture. We propose an approach based on the visual propagation of available scene knowledge from the known (visible) scene areas to these unknown (invisible) 3D regions (i.e. the completion of unknown volumes via visual propagation - the concept of volume completion). Our proposed approach uses a combination of global surface fitting, to derive an initial underlying geometric surface completion, together with a 3D extension of nonparametric texture synthesis in order to provide the propagation of localised structural 3D surface detail (i.e. surface relief). We further extend our technique both to the combined completion of 3D surface relief and colour and additionally to hierarchical surface completion that offers both improved structural results and computational efficiency gains over our initial non-hierarchical technique. To validate the success of these approaches we present the completion and extension of numerous 2.5D (and 3D) surface examples with relief ranging in natural, man-made, stochastic, regular and irregular forms. These results are evaluated both subjectively within our definition of plausible completion and quantitatively by statistical analysis in the geometric and colour domains.

Abstract not available

Recent advances in Light Detection and Ranging (LIDAR) technology and integration have resulted in vehicle-borne platforms for urban LIDAR scanning, such as Terrapoint Inc.'s TITAN system. Such technology has lead to an explosion in ground LIDAR data. The large size of such mobile urban LIDAR data sets, and the ease at which they may now be collected, has shifted the bottleneck of creating abstract urban models for Geographical Information Systems (GIS) from data collection to data processing. While turning such data into useful models has traditionally relied on human analysis, this is no longer practical. This thesis outlines a methodology for automatically recovering the necessary information to create abstract urban models from mobile urban LIDAR data using computer vision methods. As an integral part of the methodology, a novel scale-based interest operator is introduced (Di erence of Normals) that is e cient enough to process large datasets, while accurately isolating objects of interest in the scene according to real-world parameters. Finally a novel localized object recognition algorithm is introduced (Local Potential Well Space Embedding), derived from a proven global method for object recognition (Potential Well Space Embedding). The object recognition phase of our methodology is discussed with these two algorithms as a focus. / Thesis (Master, Computing) -- Queen's University, 2010-03-01 12:26:34.698

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The cost of telemetry collection is significantly reduced by unmanned store and forward systems made possible using 622MHz OC-12 networks. Networks are readily available to telemetry system architects. The in-band control of remote unmanned collection platforms is handled through a Java browser interface. Data from many telemetry channels are collected and temporarily stored on a digital disk system designed around the OC-12 network. The I/O, storage, and network components are configured, set, and initialized remotely. Recordings are started and stopped on command and can be made round-the-clock. Files of stored, time stamped data are delivered at the rate of OC-12 to a distribution center.