{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/53158"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/53158","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Spectral control of viscous alignment for deformation invariant image matching","abstract":"We present a new approach to deformation invariant image matching. Our approach retains the broad range of linear and nonlinear deformations that viscous alignment methods can model, but introduces a selectivity that is necessary for recognition. Our method models viscous kernels with an over-complete filter basis. The basis is parameterized with a single scalar parameter, the spectral radius r, which selects deformations ranging in complexity from tranlations to \"turbulence.\" The spectral radius is used for cascaded alignment starting from low deformation frequencies and finishing with high deformation frequencies. Cascaded alignment makes deformation invariant matching for recognition feasible and efficient. Because spectral radii map directly to deformation complexity, their contributions are selectively weighed to calculate the template-target similarity. In this way, our model can distinguish deformations by their relevance to recognition, without losing the flexibility of viscous alignment for handling nonlinear deformations. Our approach is applied to recognize flexible bodies of animals, and results indicate that the method is very promising.","abstract_html":"We present a new approach to deformation invariant image matching. Our approach retains the broad range of linear and nonlinear deformations that viscous alignment methods can model, but introduces a selectivity that is necessary for recognition. Our method models viscous kernels with an over-complete filter basis. The basis is parameterized with a single scalar parameter, the spectral radius r, which selects deformations ranging in complexity from tranlations to "turbulence." The spectral radius is used for cascaded alignment starting from low deformation frequencies and finishing with high deformation frequencies. Cascaded alignment makes deformation invariant matching for recognition feasible and efficient. Because spectral radii map directly to deformation complexity, their contributions are selectively weighed to calculate the template-target similarity. In this way, our model can distinguish deformations by their relevance to recognition, without losing the flexibility of viscous alignment for handling nonlinear deformations. Our approach is applied to recognize flexible bodies of animals, and results indicate that the method is very promising.","abstract_has_math":false,"creators":["Yang, Christopher Minzer"],"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":["Sai Ravela."],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009","date_published":"2009","updated_at":"2026-07-22T22:22:11Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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. 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Our approach retains the broad range of linear and nonlinear deformations that viscous alignment methods can model, but introduces a selectivity that is necessary for recognition. Our method models viscous kernels with an over-complete filter basis. The basis is parameterized with a single scalar parameter, the spectral radius r, which selects deformations ranging in complexity from tranlations to \"turbulence.\" The spectral radius is used for cascaded alignment starting from low deformation frequencies and finishing with high deformation frequencies. Cascaded alignment makes deformation invariant matching for recognition feasible and efficient. Because spectral radii map directly to deformation complexity, their contributions are selectively weighed to calculate the template-target similarity. In this way, our model can distinguish deformations by their relevance to recognition, without losing the flexibility of viscous alignment for handling nonlinear deformations. Our approach is applied to recognize flexible bodies of animals, and results indicate that the method is very promising."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Spectral control of viscous alignment for deformation invariant image matching"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sai Ravela."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Yang, Christopher Minzer"],"dc:date.accessioned":["2010-03-25T15:08:26Z"],"dc:date.available":["2010-03-25T15:08:26Z"],"dc:date.issued":["2009"],"dc:description":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.","Includes bibliographical references (p. 55-57)."],"dc:description.abstract":["We present a new approach to deformation invariant image matching. Our approach retains the broad range of linear and nonlinear deformations that viscous alignment methods can model, but introduces a selectivity that is necessary for recognition. Our method models viscous kernels with an over-complete filter basis. The basis is parameterized with a single scalar parameter, the spectral radius r, which selects deformations ranging in complexity from tranlations to \"turbulence.\" The spectral radius is used for cascaded alignment starting from low deformation frequencies and finishing with high deformation frequencies. Cascaded alignment makes deformation invariant matching for recognition feasible and efficient. Because spectral radii map directly to deformation complexity, their contributions are selectively weighed to calculate the template-target similarity. In this way, our model can distinguish deformations by their relevance to recognition, without losing the flexibility of viscous alignment for handling nonlinear deformations. Our approach is applied to recognize flexible bodies of animals, and results indicate that the method is very promising."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/53158"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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. 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