Narrow directional steerable filters in motion estimation

We extend the mathematical analysis of previous work [M.T. Andersson, Controllable multi-dimensional filters and models in low-level computer vision, Ph.D. Thesis, Department of Electrical Engineering, Linkonping University, Sweden, 1992] and we give rigorous, general, mathematical formulas for the...

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Bibliographic Details
Published inComputer vision and image understanding Vol. 110; no. 2; pp. 192 - 211
Main Authors Alexiadis, Dimitrios S., Sergiadis, George D.
Format Journal Article
LanguageEnglish
Published San Diego, CA Elsevier Inc 01.05.2008
Elsevier
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Computer vision
Exact sciences and technology
Fourier transform
Motion estimation
Pattern recognition. Digital image processing. Computational geometry
Steerable filters
Computer vision
Fourier transform
Motion estimation
Steerable filters
Electrical engineering
Fourier transformation
Image processing
Modeling
Information technology
Quadrature
Mathematical formula
University
Image sequence
Unified theory
Motion analysis
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Summary:We extend the mathematical analysis of previous work [M.T. Andersson, Controllable multi-dimensional filters and models in low-level computer vision, Ph.D. Thesis, Department of Electrical Engineering, Linkonping University, Sweden, 1992] and we give rigorous, general, mathematical formulas for the construction of 3-D steerable directional cosine filters of arbitrary higher order. Furthermore, we present the mathematical analysis for the construction of arbitrary narrow, steerable directional quadrature pairs. Incorporating the “Donut Mechanism” of Simoncelli [E.P. Simoncelli, Distributed representation and analysis of visual motion, Ph.D. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 1993] and extending it for quadrature pairs, we present a unified theory and a simple algorithm for using the constructed filters to estimate the motion in image sequences. Based on simple theoretical analysis, we explain the advantages of using higher order filters. Experimental results on synthetic, realistic, and natural sequences verify the effectiveness of the main algorithm and our arguments.
ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2007.07.002