Browsing by Subject "Registration"
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Item Automated Registration of Point Clouds with High Resolution Photographs and Rendering Under Novel Illumination Conditions(2010-12) Srisinroongruang, Rattasak; Sinzinger, Eric D.; Hoo, Karlene A.; Youn, Eunseog; Lakhani, GopalWith the increased computing power of modern technology, it has become feasible to digitally capture real world scenes and objects, preserving the scenes and objects indefinitely. Additionally, digitally capturing a scene provides the flexibility to re- visualize it under novel illumination conditions that may never occur at the scene’s real location. These two tools, scene capture and redisplay, are at the focal point of this proposal. Scene capture requires recording the spatial and intensity data of a real world scene. This is accomplished using LIDAR (a method of laser positioning) and pho- tographic cameras respectively. Once acquired, the data sets need to be registered together. This is the computation of a mathematical transform to that maps the photographic images onto the spatial data. Typically, this has been done using a significant amount of user interventation or requires the placement of distinguishing markers in the real scene. To remove these requirements and handle large data sets, the performed research submits methods to automatically compute the mathematical transforms between data sets with minimal manual intervention typically required in the current state of the art. This will be accomplished by posing the problem as an optimization problem with an objective function based upon a novel error metric. The redisplay portion of the research submits a novel rendering equation that is able to take cues from a photograph and realistically insert a synthetic object into the novel environment depicted in the photograph. This rendering equation allows the object to react realistically to the illumination conditions in the environment which may be substantially different from the environment conditions when the object or scene was captured.Item Spectral recomposition and multicomponent seismic image registration(2012-05) Cai, Yihua, 1978-; Fomel, Sergey B.; Hardage, Bob A.; Wilson, Clark R.Spectral recomposition splits a seismic spectrum into Ricker components. It provides a tool for imaging and mapping temporal bed thicknesses and geologic discontinuities. I propose an application of separable, nonlinear, least-squares estimation in spectral recomposition. Employing the Gauss-Newton method, this approach estimates fundamental signal parameters such as peak frequencies and amplitudes. I applied spectral recomposition to multicomponent seismic data, which provides new perspectives of seismic attributes and multicomponent data interpretation. Correlating S-wave reflection with P -wave reflection is one of the very first steps in multicomponent data interpretation. In a given stratigraphic interval of a geologic section, registration correlates P and S-wave profiles to determine ts /tp ratios, which are equivalent to Vp /Vs ratios for vertical propagation paths. The registration process is largely driven by the availability of dipole sonic logs. However, dipole sonic logs are not as common as standard sonic logs and tend to be affected by various borehole factors. Therefore, new techniques are needed for accurate P P and P S correlation and registration. Assuming P P and P S reflection events have been correctly positioned laterally in migrated images, and the difference between P P wave image and P S wave image can be explained only by vertical transformation, I adopt a multistep approach to register PP and PS images automatically. Setting PP time as a coordinate system, I was able to squeeze P S traces accordingly while keeping the signal pattern of P S wave data. Local seismic attributes, such as the local similarity, help improve registration accuracy.