Extensions of scale-space filtering to machine-sensing systems

Major components of scale-space theory are Gaussian filtering, and the use of zero-crossing thresholders and Laplacian operators. Properties of scale-space filtering may be useful for data analysis in multiresolution machine-sensing systems. However, these systems typically violate the Gaussian filt...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 1990-09, Vol.12 (9), p.868-882
Main Authors: Zuerndorfer, B., Wakefield, G.H.
Format: Article
Language:English
Subjects:
Applied sciences
Computer vision
Exact sciences and technology
Filtering
Geologic measurements
Image processing
Information, signal and communications theory
Laplace equations
Noise measurement
Pollution measurement
Sensor phenomena and characterization
Sensor systems
Signal processing
Signal resolution
Spatial resolution
Telecommunications and information theory
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Summary:Major components of scale-space theory are Gaussian filtering, and the use of zero-crossing thresholders and Laplacian operators. Properties of scale-space filtering may be useful for data analysis in multiresolution machine-sensing systems. However, these systems typically violate the Gaussian filter assumption, and often the data analyses used (e.g. trend analysis and classification) are not consistent with zero-crossing thresholders and Laplacian operators. The authors extend the results of scale-space theory to include these more general conditions. In particular, it is shown that relaxing the requirement of linear scaling allows filters to have non-Gaussian spatial characteristics, and that relaxing of the scale requirements (s to 0) of the impulse response allows the use of scale-space filters with limited frequency support (i.e. bandlimited filters). Bandlimited scale-space filters represent an important extension of scale-space analysis for machine sensing.< >
ISSN:0162-8828
1939-3539
DOI:10.1109/34.57682