scholarly journals Automatic Classification of Woven Fabric Structure by Using Learning Vector Quantization

2011 ◽  
Vol 15 ◽  
pp. 5005-5009 ◽  
Author(s):  
Junfeng Jing ◽  
Jing Wang ◽  
Pengfei Li ◽  
Yang Li
Keyword(s):  
Vector Quantization ◽  
Automatic Classification ◽  
Learning Vector Quantization ◽  
Woven Fabric ◽  
Fabric Structure

Automatic classification of woven fabric structure based on texture feature and PNN

2014 ◽  
Vol 15 (5) ◽  
pp. 1092-1098 ◽  
Author(s):  
Junfeng Jing ◽  
Mengmeng Xu ◽  
Pengfei Li ◽  
Qi Li ◽  
Suimei Liu
Keyword(s):  
Texture Feature ◽  
Automatic Classification ◽  
Woven Fabric ◽  
Fabric Structure

Classification of Textual Documents Using Learning Vector Quantization

2006 ◽  
Vol 6 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Muhammad Fahad Umer ◽  
M. Sikander Hayat Khiyal
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization

scholarly journals Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm

2019 ◽  
Vol 16 (18) ◽  
pp. 3294 ◽  
Author(s):  
Piotr Boniecki ◽  
Małgorzata Idzior-Haufa ◽  
Agnieszka Pilarska ◽  
Krzysztof Pilarski ◽  
Alicja Kolasa-Wiecek
Keyword(s):  
Vector Quantization ◽  
Digital Images ◽  
Neural Model ◽  
Learning Vector Quantization ◽  
Neural Models ◽  
Topological Map ◽  
Compost Maturity ◽  
Feature Map ◽  
Graphic Information

Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classifying the degree of compost maturation based on easily accessible graphic information encoded in the digital images. The research resulted in the development of original software for quick and easy assessment of compost maturity. The generated SOFM neural model was the kernel of the constructed IT system.

Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization

1998 ◽  
Vol 6 (1) ◽  
pp. 65-74 ◽  
Author(s):  
L. Pesu ◽  
P. Helistö ◽  
E. Ademovič ◽  
J.-C. Pesquet ◽  
A. Saarinen ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Wavelet Packet ◽  
Learning Vector Quantization ◽  
Respiratory Sounds ◽  
Wavelet Packet Decomposition

Neural Classifiers for Automated Visual Inspection

1994 ◽  
Vol 208 (2) ◽  
pp. 83-89 ◽  
Author(s):  
D T Pham ◽  
E J Bayro-Corrochano
Keyword(s):  
Vector Quantization ◽  
Visual Inspection ◽  
Vision System ◽  
Back Propagation ◽  
Learning Vector Quantization ◽  
Multi Layer Perceptron ◽  
Automated Visual Inspection ◽  
Valve Stem

This paper discusses the application of a back-propagation multi-layer perceptron and a learning vector quantization network to the classification of defects in valve stem seals for car engines. Both networks were trained with vectors containing descriptive attributes of known flaws. These attribute vectors (‘signatures’) were extracted from images of the seals captured by an industrial vision system. The paper describes the hardware and techniques used and the results obtained.

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