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Cartoon-Residual Image Decompositions with Application in Fingerprint Recognition

dc.contributor.advisorHuckemann, Stephan Prof. Dr.
dc.contributor.authorRichter, Robin
dc.date.accessioned2019-12-11T11:34:11Z
dc.date.available2019-12-11T11:34:11Z
dc.date.issued2019-12-11
dc.identifier.urihttp://hdl.handle.net/21.11130/00-1735-0000-0005-12CB-2
dc.identifier.urihttp://dx.doi.org/10.53846/goediss-7762
dc.identifier.urihttp://dx.doi.org/10.53846/goediss-7762
dc.language.isoengde
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc510de
dc.titleCartoon-Residual Image Decompositions with Application in Fingerprint Recognitionde
dc.typedoctoralThesisde
dc.contributor.refereeHuckemann, Stephan Prof. Dr.
dc.date.examination2019-11-06
dc.description.abstractengImage decompositions into a piecewise smooth part - called a cartoon - and a residual part containing oscillating patterns and/or noise, proved to be very useful in automated image processing, for example in applications such as segmentation and classification, denoising and deblurring, or shape- and edge-detection. A major challenge poses the selection of an appropriate decomposition model for a specific application at hand. To tackle this problem, this thesis proposes a generalized cartoon-residual decomposition algorithm that features a high-dimensional set of continuous parameters. The goal is to obtain a highly flexible algorithm such that choosing a decomposition model can be reformulated as a parameter selection problem. The proposed generalization contains multiple well known models such as the Rudin-Osher-Fatemi model as special cases, whilst also including a range of novel cartoon-residual decompositions. This thesis provides existence, convergence and uniqueness results for fixed points of the generalized algorithm for varying subfamilies of parameters, respectively, laying a foundation for possible tuning or training. Furthermore, as a proof of concept, first experimental results for denosing and texture-removal are presented, illustrating potential benefits of the novel decompositions. As an application, a new fingerprint quality estimator based on an existing cartoon-texture-residual decomposition by Thai and Gottschlich is proposed.de
dc.contributor.coRefereePlonka-Hoch, Gerlind Prof. Dr.
dc.subject.engmathematical imagingde
dc.subject.engcontinuous optimizationde
dc.subject.engfingerprint analysisde
dc.subject.engimage decompositionsde
dc.subject.engalternating direction method of multipliersde
dc.subject.engtotal variation in imagingde
dc.identifier.urnurn:nbn:de:gbv:7-21.11130/00-1735-0000-0005-12CB-2-4
dc.affiliation.instituteFakultät für Mathematik und Informatikde
dc.subject.gokfullMathematics (PPN61756535X)de
dc.identifier.ppn1685364713


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