HYPERCOMPLEX ALGEBRAS AS UNIFIED LANGUAGE FOR IMAGE PROCESSING AND PATTERN RECOGNITION PART 1. CLIFFORDEAN MODELS OF MULTICHANNEL IMAGES: chapter in book

Valeriy G. Labunets, Juriy G. Smetanin, Victor P. Chasovskikh, Ekaterina Ostheimer

Resultado de pesquisa: Chapterrevisão de pares

Resumo

In this work, we assume that a brain in the visual cortex (VC) operates with Clifford numbers when it calculates hypercomplex-valued invariants of an image as it recognizes it. Clifford algebras generalize the algebras of complex numbers, quaternions and octonions. Of course, the algebraic nature of hypercomplex numbers must correspond to the spaces with respect to geometrically perceivable properties. For recognition of 2-D (bichromatic), 3-D (color), and n-D (multi-channel) images, we turn the perceptual spaces into corresponding Clifford algebras (and call them the VC-perceptual algebras). This approach gives full representation of how algebraic structures can possess image features and how algebraic structures can be used in different visual systems. It is our aim to show that the use of Clifford algebras fits more naturally to the tasks of recognition of multicolor patterns than does the use of color vector spaces. One can argue that nature has, through evolution, also learned to utilize properties of Cliffordean numbers.
Idioma originalEnglish
Título da publicação do anfitriãoADVANCES IN INFORMATION TECHNOLOGIES, TELECOMMUNICATION, AND RADIOELECTRONICS
Subtítulo da publicação do anfitriãoсборник статей
EditoresS. Kumkov, S. Shabunin, S. Singellakis
Local da publicaçãoCham
EditoraSpringer
Páginas3-19
Número de páginas17
ISBN (impresso)978-3-030-37513-3
DOIs
Estado da publicaçãoPublished - 2020

Série de publicação

NomeСер. Innovation and Discovery in Russian Science and Engineering (IDRSE)

GRNTI

  • 27.17.00

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