2026-09-20T02:48:29Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/775362022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Yi Ma and William T. Freeman.
Lan, Cyril
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-03-01T15:27:17Z
2013-03-01T15:27:17Z
2012
2012
http://hdl.handle.net/1721.1/77536
826517958
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 52-55).
Automatic 3-D reconstruction of city scenes from ground, aerial, and satellite imagery is a difficult problem that has seen active research for nearly two decades. The problem is difficult because many algorithms require salient areas in the image to be identified and segmented, a task that is typically done by humans. We propose a pipeline that detects these salient areas using low-rank texture patches. Areas in images such as building facades contain low-rank textures, which are an intrinsic property of the scene and invariant to viewpoint. The pipeline uses these low-rank patches to automatically rectify images and detect and segment out the patches with an energy-minimizing graph cut. The output is then further parameterized to provide useful data to existing 3-D reconstruction methods. The pipeline was evaluated on challenging test images from Microsoft Bing Maps oblique aerial photography and produced an 80% recall and precision with superb empirical results.
by Cyril Lan.
M.Eng.
60 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Urban scene parsing via low-rank texture patches
Facade detection via low-rank textures in urban aerial scenes
Thesis
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Publication
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<Abstract>Automatic 3-D reconstruction of city scenes from ground, aerial, and satellite imagery is a difficult problem that has seen active research for nearly two decades. The problem is difficult because many algorithms require salient areas in the image to be identified and segmented, a task that is typically done by humans. We propose a pipeline that detects these salient areas using low-rank texture patches. Areas in images such as building facades contain low-rank textures, which are an intrinsic property of the scene and invariant to viewpoint. The pipeline uses these low-rank patches to automatically rectify images and detect and segment out the patches with an energy-minimizing graph cut. The output is then further parameterized to provide useful data to existing 3-D reconstruction methods. The pipeline was evaluated on challenging test images from Microsoft Bing Maps oblique aerial photography and produced an 80% recall and precision with superb empirical results.</Abstract>
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