%0 Journal Article
%T Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes
%A Marcos R. de A. Concei£¿£¿o
%A Luis F. F. de Mendon£¿a
%A Carlos A. D. Lentini
%J Computational Water, Energy, and Environmental Engineering
%P 75-85
%@ 2168-1570
%D 2020
%I Scientific Research Publishing
%R 10.4236/cweee.2020.93006
%X Two additional features are particularly useful in pixelwise satellite
data segmentation using neural networks: one results from local window
averaging around each pixel (MWA) and another uses a standard deviation
estimator (MWSD) instead of the average. While the former¡¯s complexity has
already been solved to a satisfying minimum, the latter did not. This article
proposes a new algorithm that can substitute a <i><span style=\"font-family:Verdana;\">naive</span></i><span style=\"font-family:Verdana;\"> MWSD, by making the
complexi</span><span><span style=\"font-family:Verdana;\">ty of the computational process fall
from </span><i><span style=\"font-family:Verdana;\">O</span></i><span style=\"font-family:Verdana;\">(</span><i><span style=\"font-family:Verdana;\">N</span></i><sup><span style=\"font-family:Verdana;\">2</span></sup><i><span style=\"font-family:Verdana;\">n</span></i><sup><span style=\"font-family:Verdana;\">2</span></sup><span style=\"font-family:Verdana;\">) to </span><i><span style=\"font-family:Verdana;\">O</span></i><span><span style=\"font-family:Verdana;\">(</span><i><span style=\"font-family:Verdana;\">N</span></i></span><sup><span style=\"font-family:Verdana;\">2</span></sup><i><span style=\"font-family:Verdana;\">n</span></i><span style=\"font-family:Verdana;\">)</span><span style=\"font-family:Verdana;\">, where </span><i><span style=\"font-family:Verdana;\">N</span></i><span style=\"font-family:Verdana;\"> is a square</span></span><span style=\"font-family:Verdana;\"> input array side, and </span><i><span style=\"font-family:Verdana;\">n</span></i><span style=\"font-family:Verdana;\"> is the moving
window¡¯s side length. The Num</span><span style=\"font-family:Verdana;\">ba python
compiler was used to make python a competitive high-performance</span> <span style=\"font-family:Verdana;\">computing language in our optimizations. Our
results show efficiency benchmars</span>
%K Digital Image Processing
%K Image Segmentation
%K Standard Deviation
%K Python
%K Machine Learning
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=101849