The Principal Components of Natural Images Revisited

Heidemann G (2006)
IEEE Trans. on Pattern Analysis and Machine Intelligence 28(5): 822-826.

Journal Article | Published | English

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This paper investigates the principal components (PCs) of natural gray and color images. A horizontal and vertical typology of PCs is found which leads to the identification of groups of basis functions for steerable bandpass filters. Using this system, the contribution of spatio-chromatic structure to the total variance can be quantified for selected spatial frequencies.
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Heidemann G. The Principal Components of Natural Images Revisited. IEEE Trans. on Pattern Analysis and Machine Intelligence. 2006;28(5):822-826.
Heidemann, G. (2006). The Principal Components of Natural Images Revisited. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(5), 822-826.
Heidemann, G. (2006). The Principal Components of Natural Images Revisited. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 822-826.
Heidemann, G., 2006. The Principal Components of Natural Images Revisited. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(5), p 822-826.
G. Heidemann, “The Principal Components of Natural Images Revisited”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, 2006, pp. 822-826.
Heidemann, G.: The Principal Components of Natural Images Revisited. IEEE Trans. on Pattern Analysis and Machine Intelligence. 28, 822-826 (2006).
Heidemann, Gunther. “The Principal Components of Natural Images Revisited”. IEEE Trans. on Pattern Analysis and Machine Intelligence 28.5 (2006): 822-826.
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