scholarly journals Predicting Weather Conditions from Images

2020 ◽  
Vol 6 (11) ◽  
pp. 174-178
Author(s):  
Shonima Minhas and Shreya Kapoor
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Literature Survey ◽  
Activity Detection ◽  
Adaptive Weights ◽  
Video Recognition

Convolutional neural networks (CNNs) are widely acknowledged in the fields of image and video recognition, face recognition, image analysis, image classification and activity detection. CNNs take images as their input; assign adaptive weights and biases to numerous features of the image; and then assign the various categories to them. The intent of this paper is to establish a model to classify outdoor images to different weather classes. Literature survey about the field related to weather prediction has shown that the best results are obtained while using the CNN models. This paper proposes a method of implementation of convolutional neural networks to classify separate weather conditions into four classes, namely cloudy, rainy, shine and sunrise. In this paper, four CNN models with different number of model layers are implemented and their results are examined.

scholarly journals Automated plankton image analysis using convolutional neural networks

2018 ◽  
Vol 16 (12) ◽  
pp. 814-827 ◽  
Author(s):  
Jessica Y. Luo ◽  
Jean-Olivier Irisson ◽  
Benjamin Graham ◽  
Cedric Guigand ◽  
Amin Sarafraz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Networks

Dairy Cow Tiny Face Recognition Based on Convolutional Neural Networks

2019 ◽  
pp. 216-222
Author(s):  
Zehao Yang ◽  
Hao Xiong ◽  
Xiaolang Chen ◽  
Hanxing Liu ◽  
Yingjie Kuang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Dairy Cow

scholarly journals Image Analysis using Deep Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1385-1388
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Techniques ◽  
Massive Information ◽  
Comparative Examination ◽  
Hypothetical Structure ◽  
The Way

In late years, critical learning methodologies especially Convolutional Neural Networks have been utilized in different solicitations. CNN's have appeared to be a key capacity to ordinarily expel broad volumes of data from massive information. The uses of CNNs have inside and out ended up being useful especially in orchestrating ordinary pictures. Regardless, there have been essential obstacles in executing the CNNs in a restorative zone as a result of the nonattendance of genuine getting ready data. Consequently, general imaging benchmarks, for instance, Image Net have been conspicuously used in the restorative not too zone notwithstanding the way that they are perfect when appeared differently about the CNNs. In this paper, a comparative examination of LeNet, AlexNet, and GoogLeNet has been done. Starting there, the paper has proposed an improved hypothetical structure for requesting helpful life structures pictures using CNNs. In perspective on the proposed structure of the framework, the CNNs building are required to beat the previous three plans in requesting remedial pictures.

scholarly journals Illumination-robust face recognition based on deep convolutional neural networks architectures

2020 ◽  
Vol 18 (2) ◽  
pp. 1015
Author(s):  
Ridha Ilyas Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Feature Learning ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Biometric Technology ◽  
Equalization Algorithm ◽  
Robust Face Recognition

<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

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