Automatic Pollen Detection Based on Feature Fusion and Self-attention Mechanism
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-0115-6_9
Reference17 articles.
1. Marcos, J.Víctor, et al. 2015. Automated pollen identification using microscopic imaging and texture analysis. Micron 68: 36–46.
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3. Imran, A., J. Li, Y. Pei, et al. 2019. Comparative analysis of vessel segmentation techniques in retinal images. IEEE Access 7: 114862–114887.
4. Rodriguez-Damian, M., E. Cernadas, A. Formella, et al. 2006. Automatic detection and classification of grains of pollen based on shape and texture. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 36 (4): 531–542.
5. Chica, M. 2012. Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing. Microscopy Research and Technique.
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