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Optimization of Quadrature Filters Based on the Numerical Integration of Improper Integrals
Dept. Aerodynamics/Fluid Mech., BTU Cottbus, Germany.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
2011 (English)In: Pattern Recognition: 33rd annual DAGM conference, Frankfurt, Germany / [ed] Rudolf Mester and Michael Felsberg, Heidelberg: Springer Berlin , 2011, 91-100 p.Conference paper (Refereed)
Abstract [en]

Convolution kernels are a commonly used tool in computer vision. These kernels are often specified by an ideal frequency response and the actual filter coefficients are obtained by minimizing some weighted distance with respect to the ideal filter. State-of-the-art approaches usually replace the continuous frequency response by a discrete Fourier spectrum with a multitude of samples compared to the kernel size, depending on the smoothness of the ideal filter and the weight function. The number of samples in the Fourier domain grows exponentially with the dimensionality and becomes a bottleneck concerning memory requirements.

In this paper we propose a method that avoids the discretization of the frequency space and makes filter optimization feasible in higher dimensions than the standard approach. The result is no longer depending on the choice of the sampling grid and it remains exact even if the weighting function is singular in the origin. The resulting improper integrals are efficiently computed using Gauss-Jacobi quadrature.

Place, publisher, year, edition, pages
Heidelberg: Springer Berlin , 2011. 91-100 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 (Print) ; 6835
Keyword [en]
Localized kernels, filter optimization, Gauss-Jacobi quadrature
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-69604DOI: 10.1007/978-3-642-23123-0_10ISBN: 978-3-642-23122-3OAI: diva2:429706
Available from: 2011-07-05 Created: 2011-07-05 Last updated: 2016-05-04

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