Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/17302
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dc.titleOrdinal depth from SFM and its application in robust scene recognition
dc.contributor.authorLI SHIMIAO
dc.date.accessioned2010-06-03T18:00:08Z
dc.date.available2010-06-03T18:00:08Z
dc.date.issued2009-09-04
dc.identifier.citationLI SHIMIAO (2009-09-04). Ordinal depth from SFM and its application in robust scene recognition. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/17302
dc.description.abstractUnder the purposive vision paradigm, visual data sensing, space representation and visual processing are task driven. Visual information in this paradigm can be weak or qualitative as long as it successfully subserves some vision task, but it should be easy and robust to recover. In this thesis, we propose the qualitative structure information ? ordinal depth as a computationally robust way to represent 3D geometry obtained from motion cues and in particular, advocate it as an informative and powerful component in the task of robust scene recognition. The first part of this thesis analyzes the computational property of ordinal depth when being recovered from the motion cues and proposes an active camera control method - the biomimetic TBL motion as a strategy to robustly recover ordinal depth. The second part of this thesis proposes a scene recognition strategy that integrates the appearance-based local SURF features and the geometry-based 3D ordinal constraint to recognize different views of a scene, possibly under different illumination and subject to various dynamic changes common in natural scenes.
dc.language.isoen
dc.subjectscene recognition, ordinal depth, structure from motion, TBL motion, 3D ordinal constraint, qualitative vision
dc.typeThesis
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.supervisorCHEONG LOONG FAH
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Ph.D Theses (Open)

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