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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| Published in | IEEE transactions on computers Vol. C-21; no. 2; pp. 179 - 186 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.02.1972
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Barnea, Daniel I. Silverman, Harvey F. |
| Author_xml | – sequence: 1 givenname: Daniel I. surname: Barnea fullname: Barnea, Daniel I. organization: IBM T. J. Watson Research Center, Yorktown Heights, N. Y. 10598.; Eljim, Holon, Israel – sequence: 2 givenname: Harvey F. surname: Silverman fullname: Silverman, Harvey F. organization: IBM T. J. Watson Research Center, Yorktown Heights, N. Y. 10598 |
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| CODEN | ITCOB4 |
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| DeliveryMethod | fulltext_linktorsrc |
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| References | anuta (ref1) 1970; ge 8 ref2 goodman (ref4) 1968 papoulis (ref3) 1965 ref5 |
| References_xml | – year: 1968 ident: ref4 publication-title: Introduction to Fourier Optics contributor: fullname: goodman – year: 1965 ident: ref3 publication-title: Probability random variables and stochastic processes contributor: fullname: papoulis – ident: ref5 doi: 10.1109/TAU.1967.1161904 – volume: ge 8 start-page: 353 year: 1970 ident: ref1 article-title: spatial registration of multispectral and multitemporal digital imagery using fast fourier transform techniques publication-title: IEEE Trans Geosci Electron doi: 10.1109/TGE.1970.271435 contributor: fullname: anuta – ident: ref2 doi: 10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2 |
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| Snippet | The automatic determination of local similarity between two structured data sets is fundamental to the disciplines of pattern recognition and image processing.... |
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| SubjectTerms | 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 |
| Title | A Class of Algorithms for Fast Digital Image Registration |
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