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Lattice Algebra Approach to Color Image Segmentation
GONZALO JORGE URCID SERRANO
JUAN CARLOS VALDIVIEZO NAVARRO
Acceso Abierto
Atribución-NoComercial-SinDerivadas
Color image segmentation
Color spaces
Convex sets
Lattice auto-associative memories
Linear mixing model
Pixel based segmentation
Unsupervised clustering
This manuscript describes a new technique for segmenting color images in different color spaces based on geometrical properties of lattice auto-associative memories. Lattice associative memories are artificial neural networks able to store a finite set X of n-dimensional vectors and recall them when a noisy or incomplete input vector is presented. The canonical lattice auto-associative memories include the min memory Wₓₓ and the max memory Mₓₓ, both defined as square matrices of size n × n. The column vectors of Wₓₓ and Mₓₓ, scaled additively by the components of the minimum and maximum vector bounds of X, are used to determine a set of extreme points whose convex hull encloses X. Specifically, since color images form subsets of a finite geometrical space, the scaled column vectors of each memory will correspond to saturated color pixels. Thus, maximal tetrahedrons do exist that enclose proper subsets of pixels in X and such that other color pixels are considered as linear mixtures of extreme points determined from the scaled versions of Wₓₓ and Mₓₓ. We provide illustrative examples to demonstrate the effectiveness of our method including comparisons with alternative segmentation methods from the literature as well as color separation results in four different color spaces.
Journal of Mathematical Imaging and Vision
09-06-2011
Artículo
Inglés
Estudiantes
Investigadores
Público en general
Urcid, Gonzalo, et al., (2011), Lattice Algebra Approach to Color Image Segmentation, Journal of Mathematical Imaging and Vision. Vol. 42(2-3):150–162
ÓPTICA
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