TITLE:
Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes
AUTHORS:
Marcos R. de A. Conceição, Luis F. F. de Mendonça, Carlos A. D. Lentini
KEYWORDS:
Digital Image Processing, Image Segmentation, Standard Deviation, Python, Machine Learning
JOURNAL NAME:
Computational Water, Energy, and Environmental Engineering,
Vol.9 No.3,
July
30,
2020
ABSTRACT: 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 naive MWSD, by making the
complexity of the computational process fall
from O(N2n2) to O(N2n), where N is a square input array side, and n is the moving
window’s side length. The Numba python
compiler was used to make python a competitive high-performance computing language in our optimizations. Our
results show efficiency benchmars