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.

中文翻译：

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基于卷积神经网络的自动3D花粉识别

自动花粉识别的重要性已经在从古气候研究到一些日常实践（如花粉过敏预测）的多个领域进行了研究。本文试图提出一种基于卷积神经网络的自动3D花粉图像识别方法。为了达到这个目的，应该考虑高特征维度和复杂的姿势变换。因此，这项工作的重点是一个三部分的新方法：构建空间局部关键点以获得花粉图像的局部稳定点，使用局部稳定点作为输入计算定向局部二值模式，使用卷积神经网络识别花粉粒作为分类器。实验在两个标准花粉图像数据集上进行：共聚焦-E 数据集和 Pollenmonitor 数据集。