{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/77536"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/77536","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Urban scene parsing via low-rank texture patches","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_html":"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_has_math":false,"creators":["Lan, Cyril"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Yi Ma and William T. 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