Título
Lattice Algebra Approach to Color Image Segmentation
Autor
GONZALO JORGE URCID SERRANO
JUAN CARLOS VALDIVIEZO NAVARRO
Nivel de Acceso
Acceso Abierto
Materias
Color image segmentation - (COLOR IMAGE SEGMENTATION) Color spaces - (COLOR SPACES) Convex sets - (CONVEX SETS) Lattice auto-associative memories - (LATTICE AUTO-ASSOCIATIVE MEMORIES) Linear mixing model - (LINEAR MIXING MODEL) Pixel based segmentation - (PIXEL BASED SEGMENTATION) Unsupervised clustering - (UNSUPERVISED CLUSTERING) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) FÍSICA - (CTI) ÓPTICA - (CTI) ÓPTICA - (CTI)
Resumen o descripción
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.
Editor
Journal of Mathematical Imaging and Vision
Fecha de publicación
9 de junio de 2011
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
Urcid, Gonzalo, et al., (2011), Lattice Algebra Approach to Color Image Segmentation, Journal of Mathematical Imaging and Vision. Vol. 42(2-3):150–162
Repositorio Orígen
Repositorio Institucional del INAOE
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