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Artificial Intelligence Identification of Multiple Microfossils from the Cambrian Kuanchuanpu Formation in Southern Shaanxi, China

Artificial Intelligence Identification of Multiple Microfossils from the Cambrian Kuanchuanpu... The Cambrian Kuanchuanpu Formation in southern Shaanxi, China is a critical window for the understanding of the Cambrian explosion, because of abundant and various exceptionally preserved metazoans and embryo fossils yielded. The efficiency of traditional sample manually selecting with microscopes is quite low and hinder the discoveries of new species, thus recognition and classification of microfossils by artificial intelligence (AI) is substantially in the request. In this paper, we develop a procedure for fossil area segmentation in common multi‐typed mixed photos by improved watershed algorithm. And for better fossil recognition, previous histogram of oriented grandient (HOG) algorithm is replaced by scale invariant feature transform (SIFT), which is feasible for the segmented images and increase the accuracy significantly. Thus, the scope of application of AI fossil recognition can be extended form single fossil image to multi‐typed mixed images and the reliability is also secured, as the result of our test presents a high (at least 84%) accuracy of fossil recognition. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Geologica Sinica (English Edition) Wiley

Artificial Intelligence Identification of Multiple Microfossils from the Cambrian Kuanchuanpu Formation in Southern Shaanxi, China

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References (42)

Publisher
Wiley
Copyright
© 2020 Geological Society of China
ISSN
1000-9515
eISSN
1755-6724
DOI
10.1111/1755-6724.14498
Publisher site
See Article on Publisher Site

Abstract

The Cambrian Kuanchuanpu Formation in southern Shaanxi, China is a critical window for the understanding of the Cambrian explosion, because of abundant and various exceptionally preserved metazoans and embryo fossils yielded. The efficiency of traditional sample manually selecting with microscopes is quite low and hinder the discoveries of new species, thus recognition and classification of microfossils by artificial intelligence (AI) is substantially in the request. In this paper, we develop a procedure for fossil area segmentation in common multi‐typed mixed photos by improved watershed algorithm. And for better fossil recognition, previous histogram of oriented grandient (HOG) algorithm is replaced by scale invariant feature transform (SIFT), which is feasible for the segmented images and increase the accuracy significantly. Thus, the scope of application of AI fossil recognition can be extended form single fossil image to multi‐typed mixed images and the reliability is also secured, as the result of our test presents a high (at least 84%) accuracy of fossil recognition.

Journal

Acta Geologica Sinica (English Edition)Wiley

Published: Feb 1, 2020

Keywords: ; ; ;

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