Title:
Vision-based place categorization
Vision-based place categorization
| dc.contributor.advisor | Christensen, Henrik I. | |
| dc.contributor.author | Bormann, Richard Klaus Eduard | en_US |
| dc.contributor.committeeMember | Ahamad, Mustaque | |
| dc.contributor.committeeMember | Copeland, John | |
| dc.contributor.committeeMember | Feamster, Nicholas G. | |
| dc.contributor.committeeMember | Traynor, Patrick | |
| dc.contributor.department | Computing | en_US |
| dc.date.accessioned | 2011-03-04T20:59:30Z | |
| dc.date.available | 2011-03-04T20:59:30Z | |
| dc.date.issued | 2010-11-18 | en_US |
| dc.description.abstract | In this thesis we investigate visual place categorization by combining successful global image descriptors with a method of visual attention in order to automatically detect meaningful objects for places. The idea behind this is to incorporate information about typical objects for place categorization without the need for tedious labelling of important objects. Instead, the applied attention mechanism is intended to find the objects a human observer would focus first, so that the algorithm can use their discriminative power to conclude the place category. Besides this object-based place categorization approach we employ the Gist and the Centrist descriptor as holistic image descriptors. To access the power of all these descriptors we employ SVM-DAS (discriminative accumulation scheme) for cue integration and furthermore smooth the output trajectory with a delayed Hidden Markov Model. For the classification of the variety of descriptors we present and evaluate several classification methods. Among them is a joint probability modelling approach with two approximations as well as a modified KNN classifier, AdaBoost and SVM. The latter two classifiers are enhanced for multi-class use with a probabilistic computation scheme which treats the individual classifiers as peers and not as a hierarchical sequence. We check and tweak the different descriptors and classifiers in extensive tests mainly with a dataset of six homes. After these experiments we extend the basic algorithm with further filtering and tracking methods and evaluate their influence on the performance. Finally, we also test our algorithm within a university environment and on a real robot within a home environment. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/37233 | |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Vision-based place categorization | en_US |
| dc.subject | Multi-class SVM | en_US |
| dc.subject | Multi-class AdaBoost | en_US |
| dc.subject.lcsh | Algorithms | |
| dc.subject.lcsh | Robot vision | |
| dc.subject.lcsh | Robotics | |
| dc.subject.lcsh | Robotics Human factors | |
| dc.title | Vision-based place categorization | en_US |
| dc.type | Text | |
| dc.type.genre | Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Christensen, Henrik I. | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.relation.ispartofseries | Master of Science in Computer Science | |
| relation.isAdvisorOfPublication | afdc727f-2705-4744-945f-e7d414f2212b | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf | |
| relation.isSeriesOfPublication | 3ef9b3be-896e-4b1b-8aa6-e24d540b7d43 |
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