Research graph
References from Automated classification of pollen grains microscopic images using cognitive attention based on human Two Visual Streams Hypothesis. Local targets link to admitted publications; unresolved targets remain external evidence.
Neural networks for increased accuracy of allergenic pollen monitoring
10.1038/s41598-021-90433-x · 2021 · External reference
Modern approaches for leveraging biodiversity collections to understand change in plant-insect interactions
10.3389/fevo.2022.924941 · 2022 · External reference
Efficient pollen grain classification using pre-trained Convolutional Neural Networks: a comprehensive study
10.1186/s40537-023-00815-3 · 2023 · External reference
Advanced CNN architectures for pollen classification: Design and comprehensive evaluation
10.1080/08839514.2022.2157593 · 2023 · External reference
Pattern recognition methodologies for pollen grain image classification: a survey
10.1007/s00138-021-01271-w · 2022 · External reference
How to identify pollen like a palynologist: A prior knowledge-guided deep feature learning for real-world pollen classification
2024 · External reference
10.1109/imcom53663.2022.9721754
10.1109/imcom53663.2022.9721754 · External reference
Analysis of automatic image classification methods for Urticaceae pollen classification
10.1016/j.neucom.2022.11.042 · 2023 · External reference
Comparative analysis of image classification algorithms based on traditional machine learning and deep learning
2021 · External reference
Deep learning models for digital image processing: a review
10.1007/s10462-023-10631-z · 2024 · External reference
A survey of convolutional neural networks: Analysis, applications, and prospects
10.1109/tnnls.2021.3084827 · 2022 · External reference
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells
10.1007/s11042-022-12108-7 · 2022 · External reference
10.1109/csicc55295.2022.9780522
10.1109/csicc55295.2022.9780522 · External reference
Deep Learning Model of Image Classification Using Machine Learning
2022 · External reference
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
10.1186/s40537-021-00444-8 · 2021 · External reference
Understanding of convolutional neural network (CNN): A review
10.31763/ijrcs.v2i4.888 · 2023 · External reference
Artificial visual system for orientation detection based on Hubel–Wiesel model
10.3390/brainsci12040470 · 2022 · External reference
Unresolved reference
External reference
An attentive survey of attention models
10.1145/3465055 · 2021 · External reference
Attention, please! A survey of neural attention models in deep learning
10.1007/s10462-022-10148-x · 2022 · External reference
Proposal for human visual pathway in the extrastriate cortex by fiber tracking method using diffusion-weighted MRI
2020 · External reference
Deep residual learning for image recognition: A survey
10.3390/app12188972 · 2022 · External reference
A comprehensive survey on support vector machine classification: Applications, challenges and trends
2020 · External reference
Feature extraction and machine learning for the Classification of Brazilian Savannah Pollen Grains
10.1371/journal.pone.0157044 · 2016 · External reference
10.1109/cvprw.2016.165
10.1109/cvprw.2016.165 · External reference
10.1109/ist48021.2019.9010563
10.1109/ist48021.2019.9010563 · External reference
10.1109/icip40778.2020.9190776
10.1109/icip40778.2020.9190776 · External reference
Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks
10.1371/journal.pone.0201807 · 2018 · External reference
POLLEN73S: An image dataset for pollen grains classification
2020 · External reference
10.1109/cvprw50498.2020.00498
10.1109/cvprw50498.2020.00498 · External reference
Precise automatic classification of 46 different pollen types with convolutional neural networks
10.1371/journal.pone.0229751 · 2020 · External reference
10.1109/clei53233.2021.9639998
10.1109/clei53233.2021.9639998 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2021 · External reference
Pollen grain classification based on ensemble transfer learning on the Cretan Pollen Dataset
10.3390/plants11070919 · 2022 · External reference
Artificial intelligence-based classification of pollen grains using attention-guided pollen features aggregation network
2023 · External reference
A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems
2020 · External reference
Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation
2023 · External reference
An efficient AdaBoost algorithm with the multiple thresholds classification
10.3390/app12125872 · 2022 · External reference
Random forest algorithm for the classification of spectral data of astronomical objects
10.3390/a16060293 · 2023 · External reference
Random radial basis function kernel-based support vector machine
2021 · External reference
A survey of decision trees: Concepts, algorithms, and applications
10.1109/access.2024.3416838 · 2024 · External reference
A survey of k nearest neighbor algorithms for solving the class imbalanced problem
10.1155/2021/5520990 · 2021 · External reference
Feature aggregation network for small object detection
2024 · External reference
S3D: Squeeze and excitation 3D convolutional neural networks for a fall detection system
10.3390/math10030328 · 2022 · External reference
Segmenting 20 types of pollen grains for the Cretan Pollen Dataset v1 (CPD-1)
10.3390/app11146657 · 2021 · External reference
A survey on deep transfer learning and beyond
10.3390/math10193619 · 2022 · External reference
A Survey on ensemble learning under the era of deep learning
10.1007/s10462-022-10283-5 · 2023 · External reference
Deep machine learning of MobileNet, Efficient, and Inception models
10.3390/a17030096 · 2024 · External reference
Salt and pepper noise removal method based on the edge-adaptive total variation model
10.3389/fams.2022.918357 · 2022 · External reference
A survey on image data augmentation for deep learning
10.1186/s40537-019-0197-0 · 2019 · External reference
10.1109/cvpr.2009.5206848
10.1109/cvpr.2009.5206848 · External reference
A review of convolutional neural networks in computer vision
10.1007/s10462-024-10721-6 · 2024 · External reference
Comparing linear discriminant analysis and supervised learning algorithms for binary classification—A method comparison study
10.1002/bimj.202200098 · 2024 · External reference
10.1007/978-3-030-01234-2_1
10.1007/978-3-030-01234-2_1 · 2018 · External reference
A simple and light-weight attention module for convolutional neural networks
10.1007/s11263-019-01283-0 · 2020 · External reference
Data augmentation in classification and segmentation: A survey and new strategies
10.3390/jimaging9020046 · 2023 · External reference
A comprehensive survey of image augmentation techniques for deep learning
2023 · External reference
A survey of automated data augmentation algorithms for deep learning-based image classification tasks
10.1007/s10115-023-01853-2 · 2023 · External reference
Attention mechanisms in computer vision: A survey
10.1007/s41095-022-0271-y · 2022 · External reference
10.1109/eait.2018.8470438
10.1109/eait.2018.8470438 · External reference
Activation functions in deep learning: A comprehensive survey and benchmark
2022 · External reference
General Performance Score for classification problems
10.1007/s10489-021-03041-7 · 2022 · External reference
10.1109/iccv.2017.74
10.1109/iccv.2017.74 · External reference
10.1109/wacv.2018.00097
10.1109/wacv.2018.00097 · External reference
10.1109/cvprw50498.2020.00020
10.1109/cvprw50498.2020.00020 · External reference
