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Kernel-based Methods and Applications

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Kernel-based Methods and Applications

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Academic year 2020/2021

Teachers
Prof. Roberto Cavoretto
Prof. Alessandra De Rossi
Teaching period
Apr-July
Type
Basic
Credits/Recognition
Durata: 30 hours [6 CFU]
Course disciplinary sector (SSD)
MAT/08 - analisi numerica
Delivery
Formal authority
Language
English
Attendance
Obligatory
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Sommario del corso

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Course objectives

 

 

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Program

• Scattered data interpolation.
• Kernel methods and radial basis functions.
• Partition of unity methods.
• Generation of scattered data sets on the plane and on the sphere.
• Spherical interpolation.
• Application of radial basis functions in image processing.
• Fast algorithms for interpolation of large scattered data sets.
• Numerical linear algebra methods in scattered data approximation.

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Suggested readings and bibliography

• M. D. Buhmann, Radial Basis Functions: Theory and Implementation, Cambridge Monogr. Appl. Comput. Math., vol. 12, Cambridge Univ. Press, Cambridge, 2003.
• G. E. Fasshauer, Meshfree Approximation Methods with Matlab, World Scientific Publishers, Singapore, 2007.
• G. Fasshauer, M. McCourt, Kernel-based Approximation Methods using Matlab, World Scientific, Singapore, 2015.
• J. Modersitzki, Numerical Methods for Image Registration, Oxford Univ. Press, New York, 2004.
• K. Rohr, Landmark-Based Image Analysis, Using Geometric and Intensity Models, Kluwer Academic Publishers, Norwell, MA, 2001.
• H. Wendland, Scattered Data Approximation, Cambridge Monogr. Appl. Comput. Math., vol. 17, Cambridge Univ. Press, Cambridge, 2005.

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Notes


Availability to provide the course online
Lessons between: March - May 2021

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Last update: 30/07/2020 18:32
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