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EigenMoments〔Pew-Thian Yap, Raveendran Paramesran, Eigenmoments, Pattern Recognition, Volume 40, Issue 4, April 2007, Pages 1234-1244, ISSN 0031-3203, 10.1016/j.patcog.2006.07.003.〕 is a set of orthogonal, noise robust, invariant to rotation, scaling and translation and distribution sensitive moments. Their application can be found in signal processing and computer vision as descriptors of the signal or image. The descriptors can later be used for classification purposes. It is obtained by performing orthogonalization, via eigen analysis on geometric moments.〔M. K. Hu, "Visual Pattern Recognition by Moment Invariants", IRE Trans. Info. Theory, vol. IT-8, pp.179–187, 1962〕 == Framework summary == EigenMoments are computed by performing eigen analysis on the moment space of an image by maximizing Signal to Noise Ratio in the feature space in form of Rayleigh quotient. This approach has several benefits in Image processing applications: # Dependency of moments in the moment space on the distribution of the images being transformed, ensures decorrelation of the final feature space after eigen analysis on the moment space. # The ability of EigenMoments to take into account distribution of the image makes it more versatile and adaptable for different genres. # Generated moment kernels are orthogonal and therefore analysis on the moment space becomes easier. Transformation with orthogonal moment kernels into moment space is analogous to projection of the image onto a number of orthogonal axes. # Nosiy components can be removed. This makes EigenMoments robust for classification applications. # Optimal information compaction can be obtained and therefore a few number of moments are needed to characterize the images. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「EigenMoments」の詳細全文を読む スポンサード リンク
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