Master of Science / Department of Statistics / James J. Higgins / A simulation study was done to compare the Type I error and power of standard analysis of variance (ANOVA), the aligned rank transform procedure (ART), and the aligned rank transform procedure where alignment is done using medians (ART + Median). The methods were compared in the context of a balanced two-way factorial design with interaction when errors have a normal distribution and outliers are present in the data and when errors have the Cauchy distribution. The simulation results suggest that the nonparametric methods are more outlier-resistant and valid when errors have heavy tails in comparison to ANOVA. The ART + Median method appears to provide greater resistance to outliers and is less affected by heavy-tailed distributions than the ART method and ANOVA.

Unmanned vehicles (UVs) are seeing more widespread use in military, scientific, and civil sectors in recent years. These UVs range from unmanned air and ground vehicles to surface and underwater vehicles. Each of these different UVs has its own inherent strengths and weaknesses, from payload to freedom of movement. Research in this field is growing primarily because of the National Defense Act of 2001 mandating that one-third of all military vehicles be unmanned by 2015. Research using small UVs, in particular, is a growing because small UVs can go places that may be too dangerous for humans. Because of the limitations inherent in small UVs, including power consumption and payload, the selection of light weight and low power sensors and processors becomes critical. Low power CMOS cameras and real-time vision processing algorithms can provide fast and reliable information to the UVs. These vision algorithms often require computational power that limits their use in traditional general purpose processors using conventional software. The latest developments in field programmable gate arrays (FPGAs) provide an alternative for hardware and software co-design of complicated real-time vision algorithms. By tracking features from one frame to another, it becomes possible to perform many different high-level vision tasks, including object tracking and following. This thesis describes a vision guidance system for unmanned vehicles in general and the FPGA hardware implementation that operates vision tasks in real-time. This guidance system uses an object following algorithm to provide information that allows the UV to follow a target. The heart of the object following algorithm is real-time rank transform, which transforms the image into a more robust image that maintains the edges found in the original image. A minimum sum of absolute differences algorithm is used to determine the best correlation between frames, and the output of this correlation is used to update the tracking of the moving target. Control code can use this information to move the UV in pursuit of a moving target such as another vehicle.

Stereo vision is a very useful, yet challenging technology for a wide variety of applications. One of the greatest challenges is meeting the computational demands of stereo vision applications that require real-time performance. The FPGA (Field Programmable Gate Array) is a readily-available technology that allows many stereo vision methods to be implemented while meeting the strict real-time performance requirements of some applications. Some of the best results have been obtained using non-parametric stereo correlation methods, such as the rank and census transform. Yet relatively little work has been done to study these methods or to propose new algorithms based on the same principles for improved stereo correlation accuracy or reduced resource requirements. This dissertation describes the sparse census and sparse rank transforms, which significantly reduce the cost of implementation while maintaining and in some case improving correlation accuracy. This dissertation also proposes the generalized census and generalized rank transforms, which opens up a new class of stereo vision transforms and allows the stereo system to be even more optimized, often reducing the hardware resource requirements. The proposed stereo methods are analyzed, providing both quantitative and qualitative results for comparison to existing algorithms. These results show that the computational complexity of local stereo methods can be significantly reduced while maintaining very good correlation accuracy. A hardware architecture for the implementation of the proposed algorithms is also described and the actual resource requirements for the algorithms are presented. These results confirm that dramatic reductions in hardware resource requirements can be achieved while maintaining high stereo correlation accuracy. This work proposes the multi-bit census, which provides improved pixel discrimination as compared to the census, and leads to improved correlation accuracy with some stereo configurations. A rotation-invariant census transform is also proposed and can be used in applications where image rotation is possible.