Result: Texture characterization based on the Kolmogorov―Smirnov distance

Title:
Texture characterization based on the Kolmogorov―Smirnov distance
Authors:
SWIDERSKI, Bartosz, OSOWSKI, Stanislaw, KRUK, Michal, KUREK, Jaroslaw
Source:
Expert systems with applications. 42(1):503-509
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence linéaire, régression, Linear inference, regression, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse cooccurrence, Cooccurrence analysis, Análisis coocurrencia, Analyse donnée, Data analysis, Análisis datos, Analyse régression, Regression analysis, Análisis regresión, Analyse statistique, Statistical analysis, Análisis estadístico, Anneau, Ring, Anillo, Contenu image, Image content, Contenido imagen, Equation Kolmogorov, Kolmogorov equation, Ecuación Kolmogorov, Fonction régression, Regression function, Función regresión, Fouille donnée, Data mining, Busca dato, Galerie, Gallery, Galería, Pixel, Reconnaissance image, Image recognition, Reconocimiento imagen, Régression linéaire, Linear regression, Regresión lineal, Sol, Soils, Suelo, Système descripteur, Descriptor system, Sistema descriptor, Vision ordinateur, Computer vision, Visión ordenador, Texture image, Image texture, Textura de imagen, KS statistical distance, Numerical descriptor of the texture
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Warsaw University of Life Sciences, The Faculty of Applied Informatics and Mathematics, Nowoursynowska 159, 02-796 Warsaw, Poland
Warsaw University of Technology, Faculty of Electrical Engineering, Koszykowa 75, Warsaw, Poland
Military University of Technology, Faculty of Electronics, Kaliskiego 2, 00-908 Warsaw, Poland
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28843418
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.28843418
Database:
PASCAL Archive

Further Information

The paper proposes the new numerical descriptor of the texture based on the Kolmogorov-Smirnov (KS) statistical distance. In this approach to feature generation we consider the distribution of the pixel intensity placed in equal circular distances from the central point. In this statistical analysis each pixel of the image takes the role of the central point and KS statistics is estimated for the whole image. We determine the KS distance of pixel intensity corresponding to the coaxial rings of the increasing distance from the center. The slope of the linear regression function applied for approximating the characteristics presenting KS distance versus the geometrical distance of these rings, forms the proposed statistical descriptor of the image. We show the application of this numerical description for recognition of the set of images of soil of different type and show that it behaves very well as the diagnostic feature, better than texture Haralick features.