Automatic pollen images recognition is crucial for pollinosis symptoms prevention and
treatment. The problem of pollen recognition can be efficiently solved using deep learning …

This paper addresses two problems: the automated pollen species recognition and counting
them on an image obtained with a lighting microscope. Automation of pollen recognition is …

Pollen grains play a critical role in environmental, agricultural, and allergy research despite
their tiny dimensions. The accurate classification of pollen grains remains a significant …

Pollen grains play a critical role in environmental, agricultural, and allergy research despite
their tiny dimensions. The accurate classification of pollen grains remains a significant …

The paper presents the results of a new method for training the NASNet neural network
called Monte Carlo Concrete DropPath for epistemic uncertainty estimation to classify pollen …

The proposed methodology for optimizing the identification of micro-objects is implemented
on the basis of dynamic models, neural networks of various topologies, mechanisms for …

Предмет исследования. Представлены результаты моделирования гомоскедастичной
алеаторной неопределенности для детекции пыльцевых зерен на изображениях …

The paper investigates the homoscedastic aleatoric uncertainty modeling for the detection of
pollen in images. The new uncertainty modeling loss functions are presented, which are …

Предмет исследования. Представлены результаты обучения нейронной сети NASNet
с помощью нового метода отключения путей по расписанию на основе семплирования …

Предмет исследования. Представлены результаты обучения нейронной сети NASNet
с помощью нового метода отключения путей по расписанию на основе семплирования …