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Seth Teller. Finman, Ross Edward Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2013-06-17T19:48:23Z 2013-06-17T19:48:23Z 2012 2013 http://hdl.handle.net/1721.1/79218 844753203 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2013. "February 2013." Cataloged from PDF version of thesis. Includes bibliographical references (p. 77-80). This thesis looks at the problem of large object category recognition for use in robotic systems. While many algorithms exist for object recognition, category recognition remains a challenge within robotics, particularly with the robustness and real-time constraints within robotics. Our system addresses category recognition by treating it as a segmentation problem and using the resulting segments to learn and detect large objects based on their 3D characteristics. The first part of this thesis examines how to efficiently do unsupervised segmentation of an RGB-D image in a way that is consistent across wide viewpoint and scale variance, and creating features from the resulting segments. The second part of this thesis explores how to do robust data association to keep temporally consistent segments between frames. Our higher-level module filters and matches relevant segments to a learned database of categories and outputs a pixel-accurate, labeled object mask. Our system has a run time that is nearly linear with the number of RGB-D samples and we evaluate it in a real-time robotic application. by Ross Edward Finman. S.M. 80 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Real-time large object category recognition using robust RGB-D segmentation features Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6b1a068e-5f43-45b6-8877-f18165d8baa5"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Real-time large object category recognition using robust RGB-D segmentation features</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Finman, Ross Edward</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis looks at the problem of large object category recognition for use in robotic systems. While many algorithms exist for object recognition, category recognition remains a challenge within robotics, particularly with the robustness and real-time constraints within robotics. Our system addresses category recognition by treating it as a segmentation problem and using the resulting segments to learn and detect large objects based on their 3D characteristics. The first part of this thesis examines how to efficiently do unsupervised segmentation of an RGB-D image in a way that is consistent across wide viewpoint and scale variance, and creating features from the resulting segments. The second part of this thesis explores how to do robust data association to keep temporally consistent segments between frames. Our higher-level module filters and matches relevant segments to a learned database of categories and outputs a pixel-accurate, labeled object mask. Our system has a run time that is nearly linear with the number of RGB-D samples and we evaluate it in a real-time robotic application.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>