Analysis of Multiple Orientations

Estimation of local orientations in multivariate signals is an important problem in image processing and computer vision. This general problem formulation also covers optical flow estimation, which can be regarded as orientation estimation in space-time-volumes. Modelling a signal using only a singl...

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Bibliographic Details
Published in:IEEE transactions on image processing 2009-07, Vol.18 (7), p.1424-1437
Main Authors: Muhlich, M., Aach, T.
Format: Article
Language:English
Subjects:
Additive superposition
Additives
Algorithms
Applied sciences
Artificial intelligence
Color
Computer science
control theory
systems
Computer Simulation
Computer vision
eigensystem analysis
Exact sciences and technology
Image motion analysis
Image processing
Image Processing, Computer-Assisted - methods
Image texture analysis
Information, signal and communications theory
local orientation
Mathematical model
Mathematical models
Miscellaneous
mixed orientation parameters
Modelling
Models, Theoretical
multiply oriented patterns
Multivariate Analysis
occluding superposition
Orientation
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
Signal analysis
Signal processing
Signal Processing, Computer-Assisted
Software
structure tensor
Telecommunications and information theory
Tensile stress
tensor decomposition
texture analysis
Veronese map
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Summary:Estimation of local orientations in multivariate signals is an important problem in image processing and computer vision. This general problem formulation also covers optical flow estimation, which can be regarded as orientation estimation in space-time-volumes. Modelling a signal using only a single orientation, however, is often too restrictive, since occlusions and transparencies occur frequently, thus necessitating the modelling and analysis of multiple orientations . We, therefore, develop a unifying mathematical model for multiple orientations: Beyond describing an arbitrary number of orientations in scalar- and vector-valued image data such as color image sequences, it allows the unified treatment of additively and occludingly superimposed oriented structures as well as of combinations of these. Based on this model, we describe estimation schemes for an arbitrary number of additively or occludingly superimposed orientations in images. We confirm the performance of our framework on both synthetic and real image data.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2009.2019307