Binary image 2D shape learning and recognition based on lattice computing (LC) techniques – Journal of Mathematical Imaging and Vision, vol. 42, no. 2-3

Abstract

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.

Citation

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 Vision, vol. 42, no. 2-3, pp. 118-133, 2012 (Special Issue on Hybrid Artificial Intelligent Systems. Guest Editors: Manuel Graña, Emilio Corchado, Michal Wozniak).

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