IMAGE PROCESSING, PATTERN RECOGNITION
Algebraic models and methods of computer image processing. Part 1. Multiplet models of multichannel images
V. G. Labunetsa, E. V. Kokha, E. Ostheimer (Rundblad)b
a Ural State Forest Engineering University, Ekaterinburg, Russia
b Capricat LLC, Pompano Beach, Florida, USA
We present a new theoretical framework for multichannel image processing using commutative hypercomplex algebras. Hypercomplex algebras generalize the algebras of complex numbers. The main goal of the work is to show that hypercomplex algebras can be used to solve problems of multichannel (color, multicolor, and hyperspectral) image processing in a natural and effective manner. In this work, we suppose that the animal brain operates with hypercomplex numbers when processing multichannel retinal images. In our approach, each multichannel pixel is considered not as an K–D vector, but as an K–D hypercomplex number, where K is the number of different optical channels. The aim of this part is to present algebraic models of subjective perceptual color, multicolor and multichannel spaces.
multichannel images, hypercomplex algebra, image processing.
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V. G. Labunets, E. V. Kokh, E. Ostheimer (Rundblad), “Algebraic models and methods of computer image processing. Part 1. Multiplet models of multichannel images”, Computer Optics, 42:1 (2018), 84–95
Citation in format AMSBIB
\by V.~G.~Labunets, E.~V.~Kokh, E.~Ostheimer (Rundblad)
\paper Algebraic models and methods of computer image processing. Part 1. Multiplet models of multichannel images
\jour Computer Optics
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