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An Invariant Local Vector for Content-Based Image Retrieval
Barcelona, Spain September 03-September 08
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.90564415th International Conference on Patt ...
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Erwan Bigorgne, Laboratoire des Instruments et Syst?mes
Catherine Achard, Laboratoire des Instruments et Syst?mes
Jean Devars, Laboratoire des Instruments et Syst?mes
In this paper, we present the use of Full-Zernike moments as a local characterization of the image signal. Their computation allows us to construct a locally invariant vector, of which the projection in an index table provides a vote for some model-image. This approach is based on the quasi-invariant theory applied to perspective transformation. Then it requires a characterization being invariant to translation, rotation and change of scale in the image; in other respect, an appropriate normalization of the signal delivers invariance to illuminance conditions.
Citation:
Erwan Bigorgne, Catherine Achard, Jean Devars, "An Invariant Local Vector for Content-Based Image Retrieval," icpr, vol. 1, pp.5019, 15th International Conference on Pattern Recognition (ICPR'00) - Volume 1, 2000
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