On Robust Corner-Preserving Smoothingin Image Processing; .
(Sprache: Englisch)
Removing a high amount of noise and preserving most structureare desirable properties of an image smoother. Unfortunately, theyseem to be contradictory: usually one can only improve one propertyat the cost of the other one. This thesis shows how this can be...
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Removing a high amount of noise and preserving most structureare desirable properties of an image smoother. Unfortunately, theyseem to be contradictory: usually one can only improve one propertyat the cost of the other one. This thesis shows how this can be resolved:for a deeper understanding of the problem, consistency, robustness anddiscontinuity-preserving issues of M-kernel estimators in one-and two-dimensional regression are treated in detail. To identify edge- andcorner-preserving properties, a new theory based on differential geometryis developed. Finally, by combining M-smoothing and leastsquares-trimming, the TM-smoother is introduced unifying cornerpreservingproperties and outlier robustness.
Autoren-Porträt von Martin Hillebrand
Hillebrand, MartinMartin Hillebrand studied Mathematics and Economics in Göttingen(Germany) and Uppsala (Sweden). After attaining his PhD in Mathematicsin Oldenburg in 2003, he held a Postdoc position at the TechnicalUniversity of Munich. Since 2007, he is working as Quantitative Analystin Frankfurt.
Bibliographische Angaben
- Autor: Martin Hillebrand
- 2008, 108 Seiten, Maße: 15 x 22 cm, Kartoniert (TB), Englisch
- Verlag: VDM Verlag Dr. Müller e.K.
- ISBN-10: 3639002652
- ISBN-13: 9783639002652
Sprache:
Englisch
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