The "enhanced spectrum" of an image g[.] is a function h[.] of wavenumber u obtained as follows. A reflection operation Q[.] is applied to g[.]; the power spectral density I G[u]2 of Q[g[.]] is converted to the Log scale and averaged over a suitable arc; the function s[.] of u alone is thus obtained, from which a known function, the "model" m[u], is subtracted: this yields h[u]. Models m(p)[.] used herewith have a roll-off like -1OLog10[uP]. As a consequence spectrum enhancement is a non-linear image filter which is shown to include partial spatial differentiation of Q[g[.]] of suitable order. The function h[.] emphasizes deviations of s[.] from the prescribed behaviour m(p)[.]. The enhanced spectrum is used herewith as the morphological descriptor of the image after polynomial interpolation. Multivariate statistical analysis of enhanced spectra by means of principal components analysis is applied with the objective of maximizing discrimination between classes of images. Recent applications to materials science, cell biology and environmental monitoring are reviewed.
Crosta, G. (2005). The spectrum enhancement algorithm for feature extraction & pattern recognition. In D. Casasent, E. Hall, J. Roening (a cura di), Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision (pp. 60060S-1-60060S-14). Bellingham, WA : SPIE-Int. Soc. Opt. Eng, USA [10.1117/12.629086].
The spectrum enhancement algorithm for feature extraction & pattern recognition
CROSTA, GIOVANNI FRANCO FILIPPO
2005
Abstract
The "enhanced spectrum" of an image g[.] is a function h[.] of wavenumber u obtained as follows. A reflection operation Q[.] is applied to g[.]; the power spectral density I G[u]2 of Q[g[.]] is converted to the Log scale and averaged over a suitable arc; the function s[.] of u alone is thus obtained, from which a known function, the "model" m[u], is subtracted: this yields h[u]. Models m(p)[.] used herewith have a roll-off like -1OLog10[uP]. As a consequence spectrum enhancement is a non-linear image filter which is shown to include partial spatial differentiation of Q[g[.]] of suitable order. The function h[.] emphasizes deviations of s[.] from the prescribed behaviour m(p)[.]. The enhanced spectrum is used herewith as the morphological descriptor of the image after polynomial interpolation. Multivariate statistical analysis of enhanced spectra by means of principal components analysis is applied with the objective of maximizing discrimination between classes of images. Recent applications to materials science, cell biology and environmental monitoring are reviewed.
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