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Pixel Classification Based on Gray Level and Local ``Busyness''
January 1982 (vol. 4 no. 1)
pp. 79-84
ASCII Text
x 
 
Philip A. Dondes, Azriel Rosenfeld, "Pixel Classification Based on Gray Level and Local ``Busyness''," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, no. 1, pp. 79-84, January, 1982.
 
 
BibTex
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@article{ 10.1109/TPAMI.1982.4767200,
author = {Philip A. Dondes and Azriel Rosenfeld},
title = {Pixel Classification Based on Gray Level and Local ``Busyness''},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {4},
number = {1},
issn = {0162-8828},
year = {1982},
pages = {79-84},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.1982.4767200},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Pixel Classification Based on Gray Level and Local ``Busyness''
IS - 1
SN - 0162-8828
SP79
EP84
EPD - 79-84
A1 - Philip A. Dondes,
A1 - Azriel Rosenfeld,
PY - 1982
VL - 4
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Philip A. Dondes, Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
Azriel Rosenfeld, Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
An image can be segmented by classifying its pixels using local properties as features. Two intuitively useful properties are the gray level of the pixel and the ``busyness,'' or gray level fluctuation, measured in its neighborhood. Busyness values tend to be highly vari-able in busy regions; but great improvements in classification accuracy can be obtained by smoothing these values prior to classifying. An alternative possibility is to classify probabilistically and use relaxation to adjust the probabilities.
Citation:
Philip A. Dondes, Azriel Rosenfeld, "Pixel Classification Based on Gray Level and Local ``Busyness''," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, no. 1, pp. 79-84, Jan. 1982, doi:10.1109/TPAMI.1982.4767200
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