@InProceedings{DornellesHira:2015:SeWiWo,
author = "Dornelles, Marta Magda and Hirata, Nina Sumiko Tomita",
affiliation = "Department of Exact and Technological Sciences, Universidade
Estadual de Santa Cruz and Institute of Mathematics and
Statistics, University of S{\~a}o Paulo",
title = "Selection of windows for W-operator combination from entropy based
ranking",
booktitle = "Proceedings...",
year = "2015",
editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim,
Ricardo Guerra and Farrell, Ryan",
organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "binary image, morphological operator design, W-operator
combination, conditional entropy, sequential forward selection.",
abstract = "When training morphological operators that are locally defined
with respect to a neighborhood window, one must deal with the
tradeoff between window size and statistical precision of the
learned operator. More precisely, too small windows result in
large restriction errors due to the constrained operator space
and, on the other hand, too large windows result in large variance
error due to often insufficient number of samples. A two-level
training method that combines a number of operators designed on
distinct windows of moderate size is an effective way to mitigate
this issue. However, in order to train combined operators, one
must specify not only how many operators will be combined, but
also the windows for each of them. To date, a genetic algorithm
that searches for window combinations has produced the best
results for this problem. In this work we propose an alternative
approach that is computationally much more efficient. The proposed
method consists in efficiently reducing the search space by
ranking windows of a collection according to an entropy based
measure estimated from input- output joint probabilities.
Computational efficiency comes from the fact that only few
operators need to be trained. Experimental results show that this
method produces results that outperform the best results obtained
with manually selected combinations and are competitive with
results obtained with the genetic algorithm based solution. The
proposed approach is, thus, a promising step towards fully
automating the process of binary morphological operator design.",
conference-location = "Salvador, BA, Brazil",
conference-year = "26-29 Aug. 2015",
doi = "10.1109/SIBGRAPI.2015.41",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2015.41",
language = "en",
ibi = "8JMKD3MGPBW34M/3JNMEF5",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JNMEF5",
targetfile = "PID3771543.pdf",
urlaccessdate = "2024, May 02"
}