dc.contributor.advisor | Huckemann, Stephan Prof. Dr. | |
dc.contributor.author | Richter, Robin | |
dc.date.accessioned | 2019-12-11T11:34:11Z | |
dc.date.available | 2019-12-11T11:34:11Z | |
dc.date.issued | 2019-12-11 | |
dc.identifier.uri | http://hdl.handle.net/21.11130/00-1735-0000-0005-12CB-2 | |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-7762 | |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-7762 | |
dc.language.iso | eng | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject.ddc | 510 | de |
dc.title | Cartoon-Residual Image Decompositions with Application in Fingerprint Recognition | de |
dc.type | doctoralThesis | de |
dc.contributor.referee | Huckemann, Stephan Prof. Dr. | |
dc.date.examination | 2019-11-06 | |
dc.description.abstracteng | Image 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.coReferee | Plonka-Hoch, Gerlind Prof. Dr. | |
dc.subject.eng | mathematical imaging | de |
dc.subject.eng | continuous optimization | de |
dc.subject.eng | fingerprint analysis | de |
dc.subject.eng | image decompositions | de |
dc.subject.eng | alternating direction method of multipliers | de |
dc.subject.eng | total variation in imaging | de |
dc.identifier.urn | urn:nbn:de:gbv:7-21.11130/00-1735-0000-0005-12CB-2-4 | |
dc.affiliation.institute | Fakultät für Mathematik und Informatik | de |
dc.subject.gokfull | Mathematics (PPN61756535X) | de |
dc.identifier.ppn | 1685364713 | |