by Martin Lettner, Paul Kammerer, Robert Sablatnig
Abstract:
In this practical work texture analysis for painted strokes is rep orted. The work presents a study of stroke classification in which two classes of strokes are identified: fluid and dry strokes. The discrimination is done with a feature vector which is extracted from the stroke texture by the help of texture analysis metho ds. To find an adequate texture analysis metho d for this application, three different texture analysis metho ds are executed on test images from painted strokes. The metho ds applied are based on statistical features of first and second order and on the discrete wavelet transformation, whereas the statistical features of second order are extracted from the co-o ccurrence matrix. The results are compared and it turns out that the wavelet based texture analysis metho d yields the b est discrimination rate for this application
Reference:
Texture Analysis of Painted Strokes (Martin Lettner, Paul Kammerer, Robert Sablatnig), Technical report, PRIP, TU Wien, 2004.
Bibtex Entry:
@TechReport{TR089,
author = "Martin Lettner and Paul Kammerer and Robert
Sablatnig",
title = "Texture Analysis of Painted Strokes",
institution = "PRIP, TU Wien",
number = "PRIP-TR-089",
year = "2004",
url = "https://www.prip.tuwien.ac.at/pripfiles/trs/tr89.pdf",
abstract = " In this practical work texture analysis for painted
strokes is rep orted. The work presents a study of
stroke classification in which two classes of
strokes are identified: fluid and dry strokes. The
discrimination is done with a feature vector which
is extracted from the stroke texture by the help of
texture analysis metho ds. To find an adequate
texture analysis metho d for this application, three
different texture analysis metho ds are executed on
test images from painted strokes. The metho ds
applied are based on statistical features of first
and second order and on the discrete wavelet
transformation, whereas the statistical features of
second order are extracted from the co-o ccurrence
matrix. The results are compared and it turns out
that the wavelet based texture analysis metho d
yields the b est discrimination rate for this
application ",
}