Improving the industrial classification of cork stoppers by using image processing and Neuro-Fuzzy computing

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

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Image Processing and Neuro-Fuzzy Computing for Cork Quality Classification

2007 5th IEEE International Conference on Industrial Informatics ◽

10.1109/indin.2007.4384851 ◽

2007 ◽

Cited By ~ 1

Author(s):

Beatriz Paniagua-Paniagua ◽

Miguel A. Vega-Rodriguez ◽

Juan M. Sanchez-Perez ◽

Juan A. Gomez-Pulido

Keyword(s):

Image Processing ◽

Cork Quality ◽

Quality Classification ◽

Neuro Fuzzy ◽

Fuzzy Computing

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Review Neuro-fuzzy computing for image processing and pattern recognition

International Journal of Systems Science ◽

10.1080/00207729608929325 ◽

1996 ◽

Vol 27 (12) ◽

pp. 1179-1193 ◽

Cited By ~ 19

Author(s):

SANKAR K. PAL ◽

ASHISH GHOSH

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Neuro Fuzzy ◽

Fuzzy Computing

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High-precision Classification of Small Insects with Similar Color and Shape Using Image Processing

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.136.1120 ◽

2016 ◽

Vol 136 (8) ◽

pp. 1120-1127 ◽

Cited By ~ 1

Author(s):

Naoya Ikemoto ◽

Kenji Terada ◽

Yuta Takashina ◽

Akio Nakano

Keyword(s):

Image Processing ◽

High Precision

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An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0107 ◽

2017 ◽

Vol 7 (7) ◽

pp. 408

Author(s):

Yashpal Jitarwal ◽

Tabrej Ahamad Khan ◽

Pawan Mangal

Keyword(s):

Image Processing ◽

Texture Features ◽

Automatic Classification ◽

Processing Technique ◽

Image Processing Technique ◽

Advance Technique ◽

Time Required ◽

Fruit Sorting ◽

Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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