This thesis considers the Simultaneous Localization and Mapping (SLAM) problem using a set of perspective cameras arranged such that there is no overlap in their fields-of-view. With the known and fixed extrinsic calibration of each camera within the cluster, a novel real-time pose estimation system is presented that is able to accurately track the motion of a camera cluster relative to an unknown target object or environment and concurrently generate a model of the structure, using only image-space measurements. A new parameterization for point feature position using a spherical coordinate update is presented which isolates system parameters dependent on global scale, allowing the shape parameters of the system to converge despite the scale parameters remaining uncertain. Furthermore, a flexible initialization scheme is proposed which allows the optimization to converge accurately using only the measurements from the cameras at the first time step. An analysis is presented identifying the configurations of the cluster motions and target structure geometry for which the optimization solution becomes degenerate and the global scale is ambiguous. Results are presented that not only confirm the previously known critical motions for a two-camera cluster, but also provide a complete description of the degeneracies related to the point feature constellations. The proposed algorithms are implemented and verified in experiments with a camera cluster constructed using multiple perspective cameras mounted on a quadrotor vehicle and augmented with tracking markers to collect high-precision ground-truth motion measurements from an optical indoor positioning system. The accuracy and performance of the proposed pose estimation system are confirmed for various motion profiles in both indoor and challenging outdoor environments.

The combination of low-cost imaging chips and high-performance, multicore, embedded processors heralds a new era in portable vision systems. Early vision algorithms have the potential for highly data-parallel, integer execution. However, an implementation must operate within the constraints of embedded systems including low clock rate, low-power operation and with limited memory. This dissertation explores new approaches to adapt novel pixel-based vision algorithms for tomorrow's multicore embedded processors. It presents : - An adaptive, multimodal background modeling technique called Multimodal Mean that achieves high accuracy and frame rate performance with limited memory and a slow-clock, energy-efficient, integer processing core. - A new workload partitioning technique to optimize the execution of early vision algorithms on multi-core systems. - A novel data transfer technique called cat-tail dma that provides globally-ordered, non-blocking data transfers on a multicore system. By using efficient data representations, Multimodal Mean provides comparable accuracy to the widely used Mixture of Gaussians (MoG) multimodal method. However, it achieves a 6.2x improvement in performance while using 18% less storage than MoG while executing on a representative embedded platform. When this algorithm is adapted to a multicore execution environment, the new workload partitioning technique demonstrates an improvement in execution times of 25% with only a 125 ms system reaction time. It also reduced the overall number of data transfers by 50%. Finally, the cat-tail buffering technique reduces the data-transfer latency between execution cores and main memory by 32.8% over the baseline technique when executing Multimodal Mean. This technique concurrently performs data transfers with code execution on individual cores, while maintaining global ordering through low-overhead scheduling to prevent collisions.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1990. / Includes bibliographical references (leaves 124-130) / Microfiche. / x, 130 leaves, bound ill. 29 cm

Over the last few years, commodity Graphics Processing Units (GPUs) have evolved from fixed graphics pipeline processors into more flexible and powerful data-parallel processors. These stream processors are capable of sustaining computation rates of greater than ten times that of a single-core CPU. GPUs are inexpensive and are becoming ubiquitous in a wide variety of computer architectures including desktop and laptop computers, PDAs and cell phones. This research works investigates possible ways to use modern GPUs for real-time computer vision and pattern classification tasks. Special attention is paid to algorithms, where the power of the CPU is a limiting factor. This is in particular the case for real-time tracking algorithms on video streams, where many candidate regions must be evaluated at once to allow stable tracking of features. They impose a high computational burdon on sequential processing units such as the CPU. The proposed implementation presented in this thesis is considering standard PC platforms rather than expensive special dedicated hardware to allow a broad variety of users to benefit from powerful computer vision applications. In particular, this thesis includes following topics: 1. First, we present a framework for computer vision on the GPU, which is used as a foundation for the implementation of computer vision methods. 2. We continue with the discussion of GPU-based implementation of Kernel Methods, including Support Vector Machines and Kernel PCA. 3. Finally, we propose GPU-accelerated implementations of two tracking algorithms. The first algorithm uses geometric templates in a gradient vector field. The second algorithm is a color-based approach in a particle filter framework. Both are able to track objects in a video stream. This thesis concludes with a final discussion of the presented methods and will propose directions for further research work. It will also briefly present the features of the next generation of GPUs.

This thesis addresses the use of monocular eye-in-hand machine vision to control the position of a robot manipulator for dynamically challenging tasks. Such tasks are defined as those where the robot motion required approaches or exceeds the performance limits stated by the manufacturer. / Computer vision systems have been used for robot control for over two decades now, but have rarely been used for high-performance visual closed-loop control. This has largely been due to technological limitations in image processing, but since the mid 1980sadvances have made it feasible to apply computer vision techniques at a sufficiently high rate to guide a robot or close a feedback control loop. Visual servoing is the use of computer vision for closed-loop control of a robot manipulator, and has the potential to solve a number of problems that currently limit the potential of robots in industry and advanced applications. / This thesis introduces a distinction between visual kinematic and visual dynamic control. The former is well addressed in the literature and is concerned with how the manipulator should move in response to perceived visual features. The latter is concerned with dynamic effects due to the manipulator and machine vision sensor which limit performance and must be explicitly addressed in order to achieve high-performance control. This is the principle focus of the thesis. / In order to achieve high-performance it is necessary to have accurate models of the system to be controlled (the robot) and the sensor (the camera and vision system).Despite the long history of research in these areas individually, and combined in visual servoing, it is apparent that many issues have not been addressed in sufficient depth, and that much of the relevant information is spread through a very diverse literature. Another contribution of this thesis is to draw together this disparate information and present it in a systematic and consistent manner. This thesis also has a strong theme of experimentation. Experiments are used to develop realistic models which are used for controller synthesis, and these controllers are then verified experimentally.

With the development of technologies, robots have played an important role in many fields of the society. They help people to deal with a large amount of work, especially operate in the extremely dangerous environment instead of people. For a robot, effective obstacle avoidance is still a challenge in the development of robot. The existing systems sometimes combine with multi-devices to conquer this challenge so that the expensive cost has been as a negative factor that cumbers the application of robot. For this purpose, find a way with the low equipment requirement but still having the high accuracy is essential. Optic flow as another algorithm coming from bee vision has been used to help robots avoid obstacles for many years. And it owns many advantages. This study presents a system based on the optic flow is developed to avoid obstacle in the view-field of the robot. The main point in this thesis is to show how the system works under an assumed environment for robot navigation, and compare the results to Thomas’ to see whether the low equipment requirement can also achieve the purpose of avoiding obstacles.

This thesis describes research on the use of Java to develop cross-platform computer vision applications for mobile phones with integrated cameras. The particular area of research that we are interested in is Mobile Augmented Reality (AR). Currently there is no computer vision library which can be used for mobile Augmented Reality using the J2ME platform. This thesis introduces the structure of our J2ME computer vision library and describes the implementation of algorithms in our library. We also present several sample applications on J2ME enabled mobile phones and report on experiments conducted to evaluate the compatibility, portability and efficiency of the implemented algorithms.

Thesis (Ph. D.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains xi, 100 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 91-100).

Thesis (M.S.)--University of Nevada, Reno, 2004. / "May, 2004." Includes bibliographical references (leaves 80-82). Online version available on the World Wide Web.

Thesis (Ph.D.) -- University of Adelaide, Dept. of Electrical and Electronic Engineering, 2003. / "August, 2003" Bibliography: p. 261-268.