This work introduces a Type-II fuzzy lattice reasoning (FLRtypeII) scheme for learning/generalizing novel 2D shape representations. A 2D shape is represented as an element—induced from populations of three different shape descriptors—in the product lattice (F 3,⪯), where (F,⪯) denotes the lattice of Type-I intervals’ numbers (INs). Learning is carried out by inducing Type-II INs, i.e. intervals in (F,⪯). Our proposed techniques compare well with alternative classification methods from the literature in three benchmark classification problems. Competitive advantages include an accommodation of granular data as well as a visual representation of a class. We discuss extensions to gray/color images, etc.


V.G. Kaburlasos, S.E. Papadakis, A. Amanatiadis, “Binary image 2D shape learning and recognition based on lattice computing (LC) techniques”,Journal of Mathematical Imaging and Visionvol. 42, no. 2-3, pp. 118-133, 2012 (Special Issue on Hybrid Artificial Intelligent SystemsGuest Editors: Manuel Graña, Emilio Corchado, Michal Wozniak).

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