Recognition and Classification of Pollen Grains Based on the Use of Statistical, Dynamic Image Characteristics, and Unique Properties of Neural Networks

2021 ◽  
pp. 170-179
Author(s):  
Jumanov Isroil Ibragimovich ◽  
Djumanov Olim Isroilovich ◽  
Safarov Rustam Abdullayevich
Keyword(s):  
Neural Networks ◽  
Pollen Grains ◽  
Dynamic Image ◽  
Image Characteristics ◽  
Statistical Dynamic

scholarly journals Improving the quality of identification and filtering of micro-object images based on neural networks

2021 ◽  
Vol 304 ◽  
pp. 01007
Author(s):  
Isroil Jumanov ◽  
Olim Djumanov ◽  
Rustam Safarov
Keyword(s):  
Neural Networks ◽  
Wavelet Transforms ◽  
Dynamic Models ◽  
A Priori ◽  
Pollen Grains ◽  
Image Contour ◽  
Statistical Dynamic ◽  
Classification Of Images ◽  
Parameter Values

Constructive approaches, principles, and models for optimizing the identification of micro-objects have been developed based on the use of combined statistical, dynamic models and neural networks with mechanisms for filtering noise and foreign particles of images of medical objects and pollen grains. Algorithms for learning neural networks under conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing are investigated. The mechanisms of contour selection, segmentation, obtaining the boundaries of segments with hard and soft thresholds, filtering using the morphological features of the image have been developed [1]. Mechanisms for recognition and classification of images, adaptation of parameter values, tuning of the network structure, approximation and smoothing of random emissions, bursts in the image contour are proposed. A mechanism for suppressing impulse noise and noise is implemented based on various filtering methods, preserving the boundaries of objects and small-sized parts. Mathematical expressions are obtained for estimating the identification errors caused by nonstationarity, inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of micro-objects has been developed. The results were obtained for correct, incorrect recognition, as well as rejected pollen samples, which were synthesized with cubic, biquadratic, interpolation spline-functions and wavelet transforms.

scholarly journals A Comparison Study of Deep Convolutional Neural Networks for the Classification of Brazilian Savannah Pollen Grains: Preliminary Results

2019 ◽  
Author(s):  
Bruno Aristimunha ◽  
Felipe Silveira Brito Borges ◽  
Ariadne Barbosa Gonçalves ◽  
Hemerson Pistori
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pollen Grains ◽  
Pollen Grain ◽  
Comparison Study ◽  
Deep Convolutional Neural Networks ◽  
Classification Result ◽  
Brazilian Savannah ◽  
Accuracy Rates

The classification of pollen grains images are currently done manually and visually, being a weariful task and predisposed to mistakes due to human exhaustion. In this paper, the authors introduce an automatic classification of 55 different pollen grain classes, using convolutional neural networks. Different architectures and hyperparameters were used to improve the classification result. Using the networks VGG16, VGG19, and InceptionV3, were obtained accuracy rates over 93.58%.

scholarly journals Precise automatic classification of 46 different pollen types with convolutional neural networks

2020 ◽  
Author(s):  
Víctor Sevillano ◽  
Katherine Holt ◽  
José L. Aznarte
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Pollen Grains ◽  
Correct Classification ◽  
Visual Classification ◽  
Learning Techniques ◽  
Pollen Types ◽  
Human Operators ◽  
Manual Classification

AbstractIn palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and farmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models. Our proposal manages to properly classify up to 98% of the examples from a dataset with 46 different classes of pollen grains, produced by the Classifynder classification system. This is an unprecedented result which surpasses all previous attempts both in accuracy and number and difficulty of taxa under consideration, which include types previously considered as indistinguishable.

Time Delay Neural Networks for the Classification of Flow Regimes

1997 ◽  
Vol 21 (1-2) ◽  
pp. S367-S371
Author(s):  
Y Yamashita
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Flow Regimes

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Deep Learning through Convolutional Neural Networks for Classification of Image A Novel Approach Using Hyper Filter

2019 ◽  
Vol 7 (6) ◽  
pp. 164-168
Author(s):  
Kshitij Tripathi ◽  
Rajendra G. Vyas ◽  
Anil K. Gupta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Novel Approach

CLASSIFICATION OF SOUNDS OF ASTHMATIC BREATHING USING NEURAL NETWORKS

2018 ◽  
Author(s):  
G. К. Berdibaeva ◽  
◽  
O. N. Bodin ◽  
D. S. Firsov ◽  
◽  
...  
Keyword(s):  
Neural Networks
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