Simple filter design for first and second order derivatives by a double filtering approach
2014 (English)In: Pattern Recognition Letters, ISSN 0167-8655, Vol. 42, 65-71 p.Article in journal (Refereed) Published
Spline filters are usually implemented in two steps, where in the first step the basis coefficients are computed by deconvolving the sampled function with a factorized filter and the second step reconstructs the sampled function. It will be shown how separable spline filters using different splines can be constructed with fixed kernels, requiring no inverse filtering. Especially, it is discussed how first and second order derivatives can be computed correctly using cubic or trigonometric splines by a double filtering approach giving filters of length 7.
Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 42, 65-71 p.
Cubic and trigonometric splines; Convolution filters; Separable kernels; First and second order derivatives
Computer Vision and Robotics (Autonomous Systems)
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-218920DOI: 10.1016/j.patrec.2014.01.014ISI: 000333451300008OAI: oai:DiVA.org:uu-218920DiVA: diva2:697995
• We show how to compute image derivatives using fitted splines.
• Trigonometric splines are more exact than cubic splines using kernels of length 4.
• Double filtering can be combined into a single kernel of length 7.
• Pre blurring can be incorporated using approximating splines.
• Matlab code is provided.2014-01-312014-02-192014-04-29Bibliographically approved