Neural Classifiers for Automated Visual Inspection

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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A Study on the Application of Learning Vector Quantization Neural Network in Pattern Classification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.525.657 ◽

2014 ◽

Vol 525 ◽

pp. 657-660 ◽

Cited By ~ 1

Author(s):

Shuo Ding ◽

Xiao Heng Chang ◽

Qing Hui Wu

Keyword(s):

Neural Network ◽

Pattern Classification ◽

Vector Quantization ◽

Bp Neural Network ◽

Back Propagation ◽

Learning Vector Quantization ◽

Classification Method ◽

Two Dimensional ◽

The One

Standard back propagation (BP) neural network has disadvantages such as slow convergence speed, local minimum and difficulty in definition of network structure. In this paper, an learning vector quantization (LVQ) neural network classifier is established, then it is applied in pattern classification of two-dimensional vectors on a plane. To test its classification ability, the classification results of LVQ neural network and BP neural network are compared with each other. The simulation result shows that compared with classification method based on BP neural network, the one based on LVQ neural network has a shorter learning time. Besides, its requirements for learning samples and the number of competing layers are also lower. Therefore it is an effective classification method which is powerful in classification of two-dimensional vectors on a plane.

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Classification of MRI Brain Tumor and Mammogram Images using Adaboost and Learning Vector Quantization Neural Network

2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW) ◽

10.1109/iccdw45521.2020.9318645 ◽

2020 ◽

Author(s):

Ravindra Sugdeo Sonavane ◽

Adhyayan Sugdeo Sonavane

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Vector Quantization ◽

Learning Vector Quantization ◽

Mri Brain ◽

Mammogram Images

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Image Classification of Ribbed Smoked Sheet using Learning Vector Quantization

Journal of Physics Conference Series ◽

10.1088/1742-6596/801/1/012050 ◽

2017 ◽

Vol 801 ◽

pp. 012050 ◽

Cited By ~ 3

Author(s):

R F Rahmat ◽

A F Pulungan ◽

S Faza ◽

R Budiarto

Keyword(s):

Image Classification ◽

Vector Quantization ◽

Learning Vector Quantization

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Automatic Classification of Woven Fabric Structure by Using Learning Vector Quantization

Procedia Engineering ◽

10.1016/j.proeng.2011.08.930 ◽

2011 ◽

Vol 15 ◽

pp. 5005-5009 ◽

Cited By ~ 5

Author(s):

Junfeng Jing ◽

Jing Wang ◽

Pengfei Li ◽

Yang Li

Keyword(s):

Vector Quantization ◽

Automatic Classification ◽

Learning Vector Quantization ◽

Woven Fabric ◽

Fabric Structure

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Classification of Human Body Smell by Learning Vector Quantization

Distributed Computing and Artificial Intelligence, 15th International Conference - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-94649-8_11 ◽

2018 ◽

pp. 86-93

Author(s):

Sigeru Omatu

Keyword(s):

Vector Quantization ◽

Human Body ◽

Learning Vector Quantization

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Classification of Landsat 8 Imagery Using Kohonen’s Self Organizing Maps and Learning Vector Quantization

Lecture Notes in Electrical Engineering - Advances in Communication, Signal Processing, VLSI, and Embedded Systems ◽

10.1007/978-981-15-0626-0_35 ◽

2019 ◽

pp. 445-462

Author(s):

B. R. Shivakumar ◽

S. V. Rajashekararadhya

Keyword(s):

Vector Quantization ◽

Learning Vector Quantization ◽

Landsat 8 ◽

Self Organizing Maps ◽

Self Organizing

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Classification of Textual Documents Using Learning Vector Quantization

Information Technology Journal ◽

10.3923/itj.2007.154.159 ◽

2006 ◽

Vol 6 (1) ◽

pp. 154-159 ◽

Cited By ~ 21

Author(s):

Muhammad Fahad Umer ◽

M. Sikander Hayat Khiyal

Keyword(s):

Vector Quantization ◽

Learning Vector Quantization

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16183294 ◽

2019 ◽

Vol 16 (18) ◽

pp. 3294 ◽

Cited By ~ 1

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.

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