Quantifying Human Visible Color Variation from High Definition Digital Images of Orb Web Spiders

Digital processing and analysis of high resolution images of 30 individuals of the orb web spider Verrucosa arenata were performed to extract and quantify human visible colors present on the dorsal abdomen of this species. Color extraction was performed with minimal user intervention using an unsupe...

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Bibliographic Details
Published in:PloS one 2016-11, Vol.11 (11), p.e0166371-e0166371
Main Authors: Tapia-McClung, Horacio, Ajuria Ibarra, Helena, Rao, Dinesh
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Animal coloration
Animals
Araneae
Behavior
Biology and Life Sciences
Carcinus maenas
Color
Color Perception - physiology
Computing Methodologies
Deception
Digital imaging
Digital photography
Energy consumption
Engineering and Technology
Entropy
Female
Genetic aspects
Grapsidae
High definition
Humans
Image Processing, Computer-Assisted - methods
Image resolution
Information processing
Medicine and Health Sciences
Morphology
Physical Sciences
Pigmentation - physiology
Research and Analysis Methods
Silk
Spider webs
Spiders
Visible spectrum
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Summary:Digital processing and analysis of high resolution images of 30 individuals of the orb web spider Verrucosa arenata were performed to extract and quantify human visible colors present on the dorsal abdomen of this species. Color extraction was performed with minimal user intervention using an unsupervised algorithm to determine groups of colors on each individual spider, which was then analyzed in order to quantify and classify the colors obtained, both spatially and using energy and entropy measures of the digital images. Analysis shows that the colors cover a small region of the visible spectrum, are not spatially homogeneously distributed over the patterns and from an entropic point of view, colors that cover a smaller region on the whole pattern carry more information than colors covering a larger region. This study demonstrates the use of processing tools to create automatic systems to extract valuable information from digital images that are precise, efficient and helpful for the understanding of the underlying biology.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0166371