ii To my grandparents, Dominick and Agnes Yocius and Norbert and Dorothy Patek, who instilled the value of education from a very early age. iii ACKNOWLEDGMENTS I would like to thank Professor Tom Schnell for advising me throughout my time as a graduate student. His lab, the Operator Performance Laboratory (OPL), allowed me to cut my teeth as an engineer. The scope of the different types of projects that I have had the opportunity to be a part of is an experience that I could have only had at OPL. I would like to thank all of the labmates I have worked with at OPL, especially Matt Cover, Nick Lorch, Carl Richey and Joe Engler. Over the years they have helped teach me software development, aviation concepts, systems design and many other fields. I would also like to thank Professor David Andersen for teaching multiple courses on optics that gave me the background for this project. Thanks for all of the office hours over the years that helped me work through different concepts and discuss outdoor recreation. Professor Er-Wei Bai for teaching signal processing courses in ways that helped me understand concepts that I used in the this dissertation. Professor Thomas Boggess for teaching some of the hardest material I learned in college. Professor Anton Kruger for always stopping me in the hallway to talk about what I was currently working on, from solar bike club to this dissertation. I would like to thank my family for their support throughout this whole process. Thanks for making Iowa City a second home for the Yocius family the last 10 years. With Maggie, Jackie and Danny also being enrolled at the university during parts of that time, my parents have made countless trips to visit. They were all with me throughout this process. I would also like to thank Brittany Borghi for her support during my final push to finish graduate school. I could not have done this without everyone's help. iv ABSTRACT Light field photography offers a new approach to digitally captured images. These commercially available cameras are able to capture the 4D light field in a single image. This allows for a variety of image processing capabilities that traditional cameras do not offer. For example, the image can be digitally refocused after it is captured and its depth can be estimated. In terms of application, these capabilities could be beneficial …

Development of airborne light field photography

Boosting camera calibration performances

Automatic Method of Inspection for Deformation in Digital Aerial Imagery Based on Statistical Characteristics

The geometric distortion of the push-broom digital aerial imagery can be rectified according to the data of the inertial measurement unit (IMU). The low precision of IMU data will cause the undulant wavelike twist deformations of the push-broom digital aerial images after geometric rectification, directly influencing the authenticity and liability of images and their practical applications. At present, the image deformation diagnosis mainly depends on the subjective judgment of human being, which costs much time and manpower. In the paper, an automatic deformation inspection method based on statistical characteristics for digital aerial imagery is proposed to inspect the distortion caused by the low IMU data accuracy. For the undulant wavelike deformation image has the characteristic of pixel displacement in the regularly same direction, there will be a lot of wave curves in the same direction appeared in the image after geometric correction. Therefore, in the method, the positions of the wave curves in the image will be located by the extreme points of curvature of the contour lines, and then the wavelike deformations can be judged automatically through the distribution statistics of the open directions of the wave curves. The specific implement method can be described as follows: firstly, the edges of the image are detected with Canny edge detector and the vector contour lines are obtained by tracing the edges to get contour lines and fitting them with the cubic spline curve method. Then, the extreme points of curvature of the contour lines are calculated, and some of these points are determined to be the vertexes of the wave curves by judging the positional relations between each extreme point and the points around it, thus constituting a vertex set. Afterwards, the perpendicular directions of the tangent of the vertexes are used as the directions of the wave curves, and then the direction histograms of all the wave curves in the image are obtained by statistical analysis. Finally, the existence of the deformation phenomenon in the image due to the low precision of IMU data is able to be judged based on whether the directions of the wave curves are centralized in a certain direction or not. Experimental results showed that the automatic deformation inspection method presented in this paper can detect the deformation of the digital aerial images effectively caused by low accuracy of IMU data with 95% accuracy.

Automatic deformation inspection method method for digital digital aerial aerial imagery based on statistical characteristics

The geometric distortion of the push-broom digital aerial imagery can be rectified according to the data of the inertial measurement unit (IMU). The low precision of IMU data will cause the undulant wavelike twist deformations of the push-broom digital aerial images after geometric rectification, directly influencing the authenticity and liability of images and their practical applications. At present, the image deformation diagnosis mainly depends on the subjective judgement of human being, which costs much time and manpower. In the paper, an automatic deformation inspection method based on statistical characteristics for digital aerial imagery is proposed to inspect the distortion caused by the low IMU data accuracy. For the undulant wavelike deformation image has the characteristic of pixel displacement in the regularly same direction, there will be a lot of wave curves in the same direction appeared in the image after geometric correction. Therefore, in the method, the positions of the wave curves in the image will be located by the extreme points of curvature of the contour lines, and then the wavelike deformations can be judged automatically through the distribution statistics of the open directions of the wave curves. The specific implement method can be described as follows: firstly, the edges of the image are detected with Canny edge detector and the vector contour lines are obtained by tracing the edges to get contour lines and fitting them with the cubic spline curve method. Then, the extreme points of curvature of the contour lines are calculated, and some of these points are determined to be the vertexes of the wave curves by judging the positional relations between each extreme point and the points around it, thus constituting a vertex set. Afterwards, the perpendicular directions of the tangent of the vertexes are used as the directions of the wave curves, and then the direction histograms of all the wave curves in the image are obtained by statistical analysis. Finally, the existence of the deformation phenomenon in the image due to the low precision of IMU data is able to be judged based on whether the directions of the wave curves are centralized in a certain direction or not. Experimental results showed that the automatic deformation inspection method presented in this paper can detect the deformation of the digital aerial images effectively caused by low accuracy of IMU data with 95% accuracy.

https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2-W1/117/2013/isprsarchives-XL-2-W1-117-2013.pdf

Automatic deformation inspection method for digital aerial imagery based on statistical characteristics

It is an efficient way of directly measured exterior orientations using integrated GPS/IMU system. The experiment has shown that the accuracy performance of direct orientation measurement is sufficient to make orthophoto and small or medium scale map. Unfortunately, since the GPS/IMU orientation module is physically separated from the camera to be oriented translational offsets and rotations are existent and have to be considered. This paper firstly discusses the integrated GPS/IMU system and the elements of GPS/IMU supported aerial photogrammetry. Finally, the overall system calibration is investigated through the data obtained from Fengning, southwest China.

Evaluation of GPS/ IMU Supported Aerial Photogrammetry

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Evaluation of GPS/ IMU Supported Aerial Photogrammetry