COMPRESSION OF DESCRIPTIONS IN THE STRUCTURAL IMAGE RECOGNITION

2011 ◽  
Vol 70 (15) ◽  
pp. 1363-1371
Author(s):  
V. A. Gorokhovatsky
Keyword(s):  
Image Recognition ◽  
Structural Image

scholarly journals Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13417-13428
Author(s):  
Yousef Ibrahim Daradkeh ◽  
Iryna Tvoroshenko ◽  
Volodymyr Gorokhovatskyi ◽  
Liza Abdul Latiff ◽  
Norulhusna Ahmad
Keyword(s):  
Fuzzy Logic ◽  
Image Recognition ◽  
Structural Image

APPLICATION OF GRANULATION OF FEATURE DESCRIPTIONS IN STRUCTURAL IMAGE RECOGNITION

2015 ◽  
Vol 74 (6) ◽  
pp. 503-514
Author(s):  
V. A. Gorokhovatsky ◽  
O.A. Kobylin ◽  
Yu.A. Kulikov
Keyword(s):  
Image Recognition ◽  
Structural Image

Atomic structure imaging of the Si/SiO2 interface with high-resolution electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 390-391
Author(s):  
J.L. Batstone ◽  
J.M. Gibson ◽  
Alice.E. White ◽  
K.T. Short
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Medium Voltage ◽  
Voltage Electron ◽  
Resolution Electron ◽  
Structural Image ◽  
Electron Microscopes ◽  
Point To Point

High resolution electron microscopy (HREM) is a powerful tool for the determination of interface atomic structure. With the previous generation of HREM's of point-to-point resolution (rpp) >2.5Å, imaging of semiconductors in only <110> directions was possible. Useful imaging of other important zone axes became available with the advent of high voltage, high resolution microscopes with rpp <1.8Å, leading to a study of the NiSi2 interface. More recently, it was shown that images in <100>, <111> and <112> directions are easily obtainable from Si in the new medium voltage electron microscopes. We report here the examination of the important Si/Si02 interface with the use of a JEOL 4000EX HREM with rpp <1.8Å, in a <100> orientation. This represents a true structural image of this interface.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

AN ALGORITHM FOR IMAGE RECOGNITION AND ANALYSIS USING NEURAL INFORMATION TECHNOLOGIES

2012 ◽  
Vol 71 (17) ◽  
pp. 1565-1574 ◽  
Author(s):  
O. M. Gafurov ◽  
V. I. Syryamkin ◽  
A. O. Gafurov ◽  
S. S. Stolyarova
Keyword(s):  
Image Recognition ◽  
Information Technologies ◽  
Neural Information

New Techniques for Face Image Recognition Identification and Labeling

2007 ◽  
Vol 1 (4) ◽  
pp. 62-69
Author(s):  
Milhled Alfaouri ◽  
◽  
Nada N. Al-Ramahi ◽  
Keyword(s):  
Image Recognition ◽  
Face Image ◽  
New Techniques

Performance Evaluation Of Face Image Recognition Based Voila-Joins With SVM

2019 ◽  
pp. 161
Author(s):  
Jamal Mustafa Al-Tuwaijari ◽  
Suhad Ibrahim Mohammed
Keyword(s):  
Performance Evaluation ◽  
Image Recognition ◽  
Face Image
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