Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Using Domain-Specific Information in Image Processing(2014) Cash, Brianna Rose; O'Leary, Dianne P; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the increasing availability of high resolution imaging tools, even in our pockets (i.e. smartphones), everyday users can do far more than simply digitally capturing a family moment. The ease of new applications available in these portable forms, linked with users who have expert knowledge about the images and tasks, opens the door to new possibilities. With this in mind we propose two new approaches that utilize the user's knowledge for improved results. We apply these approaches to real life problems in medical and scientific image applications. In the first approach, we introduce a class of linear and nonlinear methods which we call Domain-Specific Grayscale (DSGS) methods. A DSGS method transforms a color image into an image analogous to a grayscale image, where user-specified information is used to optimize a specified image processing task and reduce the computational complexity. We introduce new methods based on projection into the space of single-coordinate images, and we adapt support vector machines by using their scores to create a DSGS image. We apply these methods to applications in dermatology, analyzing images of skin tests and skin lesions, and demonstrate their usefulness. In the second approach, we introduce a tool for improved image deblurring that safeguards against bias that can easily be introduced by a user favoring a particular result. This is particularly important in scientific and medical applications used for discovery or diagnosis. We provide real-time results of choices of regularization methods and parameter selection, and we check the statistical plausibility of the results, using three statistical diagnostics, allowing a user to see the results of the choices. Our work demonstrates the utility of domain-specific information, supplied by the user, in improving the results of image processing algorithms.Item Calibration and Metrology Using Still and Video Images(2007-08-03) Guo, Feng; Chellappa, Rama; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Metrology, the measurement of real world metrics, has been investigated extensively in computer vision for many applications. The prevalence of video cameras and sequences has led to the demand for fully automated systems. Most of the existing video metrology methods are simple extensions of still-image algorithms, which have certain limitations, requiring constraints such as parallelism of lines. New techniques are needed in order to achieve accurate results for broader applications. An important preprocessing step and a closely related topic to metrology is calibration using planar patterns. Existing approaches lack exibility and robustness when extended to video sequences. This dissertation advances the state of the art in calibration and video metrology in three directions: (1) the concept of partial rectification is proposed along with new calibration techniques using a circle with diverse types of constraints; (2) new calibration methods for video sequences using planar patterns undergoing planar motion are proposed; and (3) new algorithms to extend video metrology to a wide range of applications are presented. A fully automated system using the new technique has been built for measuring the wheelbases of vehicles.