Automatic Recognition of Palaeobios Images Under Microscope Based on Machine Learning

Xu, Yuxuan; Dai, Zongyang; Juebo, Wang; Yang, Li; Wang, Haoyang

doi:10.1109/access.2020.3024819

Cited by 13 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After processing the information, the device interacts with neighbor agents to gradually achieve the goal. erefore, multiagent systems are usually able to deal with the problems of mutual cooperation in complex environments, especially for complex problems with spatial distribution characteristics that other methods cannot match [4]. An image is a collection of graphics and images and is an information carrier that contains rich content that humans touch.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition and Simulation Based on Distributed Artificial Intelligence

Fan

2021

Complexity

View full text Add to dashboard Cite

This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients (HOG) feature extraction and Support Vector Machine (SVM) classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis (PCA) to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition experiments, the results show that the image detection efficiency is slightly improved, and the detection speed is also improved. This article analyzes the reason for these changes because PCA mainly uses the useful feature information in HOG features. The parallelization processing of HOG features on graphics processing unit (GPU) is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. Using GPU for parallelization of HOG features can make the calculation speed of HOG features improved. We use image experiments for the parallelized HOG feature algorithm. Experimental simulations show that the speed of distributed artificial intelligence image recognition is greatly improved. By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified.

show abstract

Section: Introductionmentioning

confidence: 99%

Image Recognition and Simulation Based on Distributed Artificial Intelligence

Fan

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Although there have been several online databases, presentations (Loeblich and Tappan, 1994;Foraminifera.eu;WoRMS Editorial Board, 2023), and a few software studies on foraminifera (Görmüş and Meriç, 2012;Deveciler and Akiska, 2018) in the literature, the description of foraminifera using the CNN method has not been well-documented. In fact, neural networks can be used in many areas, including geology for classifying fossil specimens, fossil segmentation and detection, and more (Zhong et al 2017;Ge et al 2017;Xu et al 2020;Mitra et al 2019;Gutiérrez et al 2018;Johansen and Sørensen, 2020;Hu et al 2020;Carvalho et al 2020).…”

Section: Literaturementioning

confidence: 99%

“…Artificial intelligence is in a very good place at the moment in image identification. These studies have also begun on fossils (Zhong, Ge, Kanakiya, Marchitto and Lobaton, 2017;Ge, Zhong, Kanakiya, Mitra, Marchitto and Lobaton, 2017;Xu, Dai, Wang, Li and Wang, 2020;Mitra et al, 2019;Gutiérrez, Nouboud, Chalifour, and Voisin, 2018;Johansen and Sørensen, 2020;Hu, Limaye and Lu, 2020;Carvalho et al, 2020). It is developing more and more every day.…”

Section: Introductionmentioning

confidence: 99%

Benti̇k Forami̇ni̇fer Görüntü Siniflamasi Ve Tanimlamalarinda Evri̇şi̇mli̇ Si̇ni̇r Aği (Cnn) Tabanli Yeni̇ Bi̇r Model

YAYAN

Yayan

2023

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

View full text Add to dashboard Cite

Fossil studies are of great importance in order to observe the change of living species over the years, to make inferences by using the information provided by the observed species, and to understand the developing and changing structure of the world we live in over the years. However, the examination and interpretation of fossil specimens is a complex and long process. Artificial intelligence studies have begun to be applied to this field in order to facilitate the working methods of paleontologists. The detection and classification of fossil specimens with the aid of computers simplifies this process as much as possible compared to manual classification processes and reduces foreign dependency for fossil assemblages for which paleontologists are not experts. To achieve this, 9 benthic foraminiferal species and non-foraminiferal sample photographs from a selected dataset were used. In this study, a new method developed for the classification of benthic foraminifera using deep convolutional neural networks, reaching higher accuracy than the results in the literature, is presented. With this method, at least 70% accuracy rates were achieved in the test results of the trained system. This study, which reached high accuracy rates with a new method, has created a successful development for the branch of paleontology in the use of artificial intelligence in microfossil identification.

show abstract

“…Techniques in computer vision have also been applied in the field of microfossil research for the tasks of classification and detection. The classification of microfossils was first attempted by obtaining key morphological parameters from microfossil images (Marmo et al, 2006;Yu et al, 1996), with support vector machines (SVMs) contributing to their classification according to the acquired values (Apostol et al, 2016;Bi et al, 2015;Hu and Davis, 2005;Solano et al, 2018;Xu et al, 2020). Owing to 6 the development of convolutional neural networks (CNNs), deep learning based classification models have successfully been used to determine the taxa of various microfossils including foraminifera and radiolarians (Carvalho et al, 2020;Hsiang et al, 2019;Itaki et al, 2020;Keçeli et al, 2017;Marchant et al, 2020;Mitra et al, 2019;Pires de Lima et al, 2020;Xu et al, 2020;Tetard et al, 2020).…”

mentioning

confidence: 99%

“…The classification of microfossils was first attempted by obtaining key morphological parameters from microfossil images (Marmo et al, 2006;Yu et al, 1996), with support vector machines (SVMs) contributing to their classification according to the acquired values (Apostol et al, 2016;Bi et al, 2015;Hu and Davis, 2005;Solano et al, 2018;Xu et al, 2020). Owing to 6 the development of convolutional neural networks (CNNs), deep learning based classification models have successfully been used to determine the taxa of various microfossils including foraminifera and radiolarians (Carvalho et al, 2020;Hsiang et al, 2019;Itaki et al, 2020;Keçeli et al, 2017;Marchant et al, 2020;Mitra et al, 2019;Pires de Lima et al, 2020;Xu et al, 2020;Tetard et al, 2020). Although some of these classification models achieve an accuracy of > 85% (Hsiang et al, 2019;Itaki et al, 2020;Marchant et al, 2020;Tetard et al, 2020), large training datasets are often required, which creates the challenge of generating a large number of images for each microfossil species.…”

mentioning

confidence: 99%

Automated detection of microfossil fish teeth from slide images using combined deep learning models

Mimura¹,

Minabe²,

Nakamura³

et al. 2022

Preprint

View full text Add to dashboard Cite

Microfossil fish teeth, known as ichthyoliths, provide a key constraint on the depositional age and environment of deep-sea sediments, especially pelagic clays where siliceous and calcareous microfossils are rarely observed. However, traditional methods for the observation of ichthyoliths require considerable time and manual labor, which can hinder their wider application. In this study, we constructed a system to automatically detect ichthyoliths in microscopic images by combining two open source deep learning models. First, the regions for ichthyoliths within the microscopic images are predicted by the instance segmentation model Mask R-CNN. All the detected regions are then re-classified using the image classification model EfficientNet-V2 to determine the classes more accurately. Compared with only using the Mask R-CNN model, the combined system offers significantly higher performance (89.0% precision, 78.6% recall, and an F1 score of 83.5%), demonstrating the utility of the system. Our system can also predict the lengths of the teeth that have been detected, with more than 90% of the predicted lengths being within ± 20% of measured length. This system provides a novel, automated, and reliable approach for the detection and length measurement of ichthyoliths from microscope images that can be applied in a range of paleoceanographic and paleoecological contexts.

show abstract

Automatic Recognition of Palaeobios Images Under Microscope Based on Machine Learning

Cited by 13 publications

References 29 publications

Image Recognition and Simulation Based on Distributed Artificial Intelligence

Image Recognition and Simulation Based on Distributed Artificial Intelligence

Benti̇k Forami̇ni̇fer Görüntü Siniflamasi Ve Tanimlamalarinda Evri̇şi̇mli̇ Si̇ni̇r Aği (Cnn) Tabanli Yeni̇ Bi̇r Model

Automated detection of microfossil fish teeth from slide images using combined deep learning models

Contact Info

Product

Resources

About