This study presents an image analysis framework coupled with machine learning algorithms for the classification of microscopy pollen grain images.
Pollen classification is based upon several characteristics of the pollen grains, such as symmetry, polarity, shape, size, structure, sculpture and apertures.
Apr 6, 2021 · Use geometric and textural features to describe pollen classes. A self-created data set with 6 classes and 584 images in total (90 to 100 images per class) was ...
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This study presents an image analysis framework coupled with machine learning algorithms for the classification of microscopy pollen grain images and ...
This study presents an image analysis framework coupled with machine learning algorithms for the classification of microscopy pollen grain images.
An extensive study on pollen grain identification is presented in this work. A combination of geometrical and texture characteristics is proposed as pollen ...
Pollen Classification Based on Geometrical, Descriptors and Colour Features Using Decorrelation Stretching Method · Abstract · Chapter PDF · Keywords · References.
Pollen grains, pollen classification, colour features, geometrical features ... The geometrical features have been used in almost all works related to grain.
In this study, an attention-guided pollen feature aggregation network (APFA-Net) based on deep feature aggregation and channel-wise attention is proposed.
Jun 28, 2024 · The authors suggested a fusion of geometric and textural characteristics in [22] as distinctive intrinsic attributes for a pollen dataset ...