Construction of an image recognition process on the basis of optical Fourier diffractometry: application to degraded bone structure classification

1994 ◽  
Vol 33 (4) ◽  
pp. 1106
Author(s):  
Jacek Galas
Keyword(s):  
Image Recognition ◽  
Bone Structure ◽  
Recognition Process

scholarly journals EKSTRAKSI FITUR AKSARA BALI MENGGUNAKAN METODE ZONING

2015 ◽  
Vol 14 (2) ◽  
pp. 44
Author(s):  
I Wayan Agus Surya Darma ◽  
I K. G. Darma Putra ◽  
Made Sudarma
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Character Recognition ◽  
Recognition System ◽  
Important Process ◽  
Recognition Process ◽  
Multiple Regions ◽  
Test Result

Feature extraction is an important process in character recognition system. The purpose of this process is to obtain special feature from a character image. This paper is focuses on how to obtain special feature from a handwritten Balinese character image using zoning. This algorithm dividing Balinese character image into multiple regions, then a special feature on each region resulting the data extracted feature. The test result in this paper generates a various  semantic and direction feature data. This is because this paper using handwritten Balinese character. Furthermore, the features that produced in this paper can be used on Balinese character image recognition process

Fluency Board Streamlines Recognition Process

ASHA Leader ◽  
2010 ◽  
Vol 15 (6) ◽  
pp. 22-23
Author(s):  
James McClure ◽  
Chamonix Olsen
Keyword(s):  
Recognition Process

Bone Structure Explains Racial Variance in Fractures

2006 ◽  
Vol 5 (12) ◽  
pp. 30
Author(s):  
KERRI WACHTER
Keyword(s):  
Bone Structure

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.

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