scholarly journals Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network

2019 ◽  
Author(s):  
J.J. Winston ◽  
D.J. Hemanth ◽  
A. Angelopoulou ◽  
E. Kapetanios
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Iris Image ◽  
Self Organizing

scholarly journals Novel Optimization Based Hybrid Self-Organizing Map Classifiers for Iris Image Recognition

2020 ◽  
Vol 13 (1) ◽  
pp. 1048
Author(s):  
J. Jenkin Winston ◽  
Gul Fatma Turker ◽  
Utku Kose ◽  
D. Jude Hemanth
Keyword(s):  
Image Recognition ◽  
Self Organizing Map ◽  
Iris Image ◽  
Self Organizing

NEURAL NETWORK FOR IMAGE RECOGNITION OF MAGNETOOPTIC MATERIALS DOMAIN STRUCTURES

2016 ◽  
Vol 59 (3) ◽  
pp. 117-121
Author(s):  
A. V. Bragin ◽  
◽  
R. R. Navletov ◽  
D. V. Pyanzin ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Domain Structures

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

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