Component analysis of spatial and spectral patterns in multispectral images. I. Basis.
A new (to our knowledge) theory of component pattern analysis in multispectral images is developed by using the methods of principal component analysis and nonlinear optimization with a nonnegativity constraint. Given images of a scene in different color bands, we estimate both the spectral curves of components included in the image and the spatial pattern corresponding to each spectral curve. In this method, neither spatial nor spectral features of the components are necessary, but the physical rule of nonnegative absorptivity and density nonnegativity is used for any material of any optical frequency at any position in the image. Experimental results of component analysis with real microscopic image data are shown to demonstrate the effectiveness of the proposed method.
Kawata
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S
Department of Applied Physics, Osaka University, Japan.
Sasaki
K
K
Minami
S
S
D005556:Form Perception; D006801:Humans; D008433:Mathematics; D008954:Models, Biological; D010364:Pattern Recognition, Visual; D012160:Retina / Q000502:physiology; D013028:Space Perception; D014785:Vision, Ocular