Automatic 3D Pollen Recognition Based on Convolutional Neural Network

The importance of automatic pollen recognition has been examined in several areas ranging from paleoclimate studies to some daily practice such as pollen hypersensitivity forecasting. This paper attempts to present an automatic 3D pollen image recognition method based on convolutional neural network...

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Bibliographic Details
Published in:Scientific programming 2021-07, Vol.2021, p.1-8
Main Authors: Wang, Zhuo, Wang, Zixuan, Wang, Likai
Format: Article
Language:English
Subjects:
Algorithms
Allergens
Artificial neural networks
Automation
Classification
Datasets
Feature extraction
Methods
Microscopy
Neural networks
Object recognition
Occlusion
Pollen
Support vector machines
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Summary:The importance of automatic pollen recognition has been examined in several areas ranging from paleoclimate studies to some daily practice such as pollen hypersensitivity forecasting. This paper attempts to present an automatic 3D pollen image recognition method based on convolutional neural network. To achieve this purpose, high feature dimensions and complex posture transformation should be taken into account. Therefore, this work focuses on a three-part novel approach: constructing spatial local key points to obtain local stable points of pollen images, computing orientational local binary pattern using local stable points as the inputs, and identifying the pollen grains using convolutional neural network as the classifier. Experiments are performed on two standard pollen image datasets: Confocal-E dataset and Pollenmonitor dataset. It is concluded that the proposed approach can effectively extract the features of pollen images and is robust to posture transformation, slight occlusion, and pollution.
ISSN:1058-9244
1875-919X
DOI:10.1155/2021/5577307