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Artificial Intelligence Identification of Multiple Microfossils from the Cambrian Kuanchuanpu Formation in Southern Shaanxi,China
Authors:ZHANG Tao  WANG Bin  LI Dedong  NIU Ben  SUN Jie  SUN Yifei  YANG Xiaoguang  LUO Juan  HAN Jian
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.
Keywords:watershed segmentation   scale invariant feature transform   visual vocabulary   support vector machine
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