A Class of Algorithms for Fast Digital Image Registration

The automatic determination of local similarity between two structured data sets is fundamental to the disciplines of pattern recognition and image processing. A class of algorithms, which may be used to determine similarity in a far more efficient manner than methods currently in use, is introduced...

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Bibliographic Details
Published inIEEE transactions on computers Vol. C-21; no. 2; pp. 179 - 186
Main Authors Barnea, Daniel I., Silverman, Harvey F.
Format Journal Article
LanguageEnglish
Published IEEE 01.02.1972
Subjects
Algorithm design and analysis
Correlation
Data mining
Image processing
Manganese
Noise
Probability density function
Registration efficiency
sequential similarity detection algorithms
spatial cross correlation
spatial registration of digital images
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Summary:The automatic determination of local similarity between two structured data sets is fundamental to the disciplines of pattern recognition and image processing. A class of algorithms, which may be used to determine similarity in a far more efficient manner than methods currently in use, is introduced in this paper. There may be a saving of computation time of two orders of magnitude or more by adopting this new approach. The problem of translational image registration, used for an example throughout, is discussed and the problems with the most widely used method-correlation explained. Simple implementations of the new algorithms are introduced to motivate the basic idea of their structure. Real data from ITOS-1 satellites are presented to give meaningful empirical justification for theoretical predictions.
ISSN:0018-9340
1557-9956
DOI:10.1109/TC.1972.5008923