Author(s)

Marcos R. de A. Conceição¹, Luis F. F. de Mendonça¹, Carlos A. D. Lentini³

¹Oceanography Department, Geoscience Institute of the Federal University of Bahia (UFBA), Salvador, Brazil.
²Earth and Environmental Physics Department, Physics Institute of the Federal University of Bahia (UFBA), Salvador, Brazil.
³Tropical Oceanography Group (GOAT), Salvador, Brazil.

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

Digital Image Processing, Image Segmentation, Standard Deviation, Python, Machine Learning

Conceição, M. , Mendonça, L. and Lentini, C. (2020) Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes. Computational Water, Energy, and Environmental Engineering, 9, 75-85. doi: 10.4236/cweee.2020.93006.