Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.

Our computational developments and analyses on experimental images are designed to evaluate the effectiveness of chemical spraying via unmanned aerial vehicle (UAV). Our evaluations are in accord with the two perspectives of color-complexity: color variety within a color system and color distributio...

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Autores principales:	Shuting Liao, Li-Yu Liu, Ting-An Chen, Kuang-Yu Chen, Fushing Hsieh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/683dd2c78a814b479feb4638946a1d34
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spelling	oai:doaj.org-article:683dd2c78a814b479feb4638946a1d342021-12-02T20:11:20ZColor-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.1932-620310.1371/journal.pone.0251258https://doaj.org/article/683dd2c78a814b479feb4638946a1d342021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251258https://doaj.org/toc/1932-6203Our computational developments and analyses on experimental images are designed to evaluate the effectiveness of chemical spraying via unmanned aerial vehicle (UAV). Our evaluations are in accord with the two perspectives of color-complexity: color variety within a color system and color distributional geometry on an image. First, by working within RGB and HSV color systems, we develop a new color-identification algorithm relying on highly associative relations among three color-coordinates to lead us to exhaustively identify all targeted color-pixels. A color-dot is then identified as one isolated network of connected color-pixel. All identified color-dots vary in shapes and sizes within each image. Such a pixel-based computing algorithm is shown robustly and efficiently accommodating heterogeneity due to shaded regions and lighting conditions. Secondly, all color-dots with varying sizes are categorized into three categories. Since the number of small color-dot is rather large, we spatially divide the entire image into a 2D lattice of rectangular. As such, each rectangle becomes a collective of color-dots of various sizes and is classified with respect to its color-dots intensity. We progressively construct a series of minimum spanning trees (MST) as multiscale 2D distributional spatial geometries in a decreasing-intensity fashion. We extract the distributions of distances among connected rectangle-nodes in the observed MST and simulated MSTs generated under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. This new tree-based p-value evaluation has the capacity to become exact.Shuting LiaoLi-Yu LiuTing-An ChenKuang-Yu ChenFushing HsiehPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251258 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shuting Liao Li-Yu Liu Ting-An Chen Kuang-Yu Chen Fushing Hsieh Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.
description	Our computational developments and analyses on experimental images are designed to evaluate the effectiveness of chemical spraying via unmanned aerial vehicle (UAV). Our evaluations are in accord with the two perspectives of color-complexity: color variety within a color system and color distributional geometry on an image. First, by working within RGB and HSV color systems, we develop a new color-identification algorithm relying on highly associative relations among three color-coordinates to lead us to exhaustively identify all targeted color-pixels. A color-dot is then identified as one isolated network of connected color-pixel. All identified color-dots vary in shapes and sizes within each image. Such a pixel-based computing algorithm is shown robustly and efficiently accommodating heterogeneity due to shaded regions and lighting conditions. Secondly, all color-dots with varying sizes are categorized into three categories. Since the number of small color-dot is rather large, we spatially divide the entire image into a 2D lattice of rectangular. As such, each rectangle becomes a collective of color-dots of various sizes and is classified with respect to its color-dots intensity. We progressively construct a series of minimum spanning trees (MST) as multiscale 2D distributional spatial geometries in a decreasing-intensity fashion. We extract the distributions of distances among connected rectangle-nodes in the observed MST and simulated MSTs generated under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. This new tree-based p-value evaluation has the capacity to become exact.
format	article
author	Shuting Liao Li-Yu Liu Ting-An Chen Kuang-Yu Chen Fushing Hsieh
author_facet	Shuting Liao Li-Yu Liu Ting-An Chen Kuang-Yu Chen Fushing Hsieh
author_sort	Shuting Liao
title	Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.
title_short	Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.
title_full	Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.
title_fullStr	Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.
title_full_unstemmed	Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.
title_sort	color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/683dd2c78a814b479feb4638946a1d34
work_keys_str_mv	AT shutingliao colorcomplexityenabledexhaustivecolordotsidentificationandspatialpatternstestinginimages AT liyuliu colorcomplexityenabledexhaustivecolordotsidentificationandspatialpatternstestinginimages AT tinganchen colorcomplexityenabledexhaustivecolordotsidentificationandspatialpatternstestinginimages AT kuangyuchen colorcomplexityenabledexhaustivecolordotsidentificationandspatialpatternstestinginimages AT fushinghsieh colorcomplexityenabledexhaustivecolordotsidentificationandspatialpatternstestinginimages
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Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images.

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