Image Processing and Neuro-Fuzzy Computing for Cork Quality Classification

2007 ◽  
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

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

2009 ◽  
Vol 21 (6) ◽  
pp. 745-760 ◽  
Author(s):  
Beatriz Paniagua ◽  
Miguel A. Vega-Rodríguez ◽  
Juan A. Gomez-Pulido ◽  
Juan M. Sanchez-Perez
Keyword(s):  
Image Processing ◽  
Neuro Fuzzy ◽  
Cork Stoppers ◽  
Fuzzy Computing

Cork quality classification system using a unified image processing and fuzzy-neural network methodology

1997 ◽  
Vol 8 (4) ◽  
pp. 964-974 ◽  
Author(s):  
Joongho Chang ◽  
Gunhee Han ◽  
J.M. Valverde ◽  
N.C. Griswold ◽  
J.-F. Duque-Carrillo ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Classification System ◽  
Fuzzy Neural Network ◽  
Cork Quality ◽  
Quality Classification ◽  
Fuzzy Neural

scholarly journals Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

2018 ◽  
pp. 57-61
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas
Keyword(s):  
Image Processing ◽  
Fuzzy System ◽  
Absolute Error ◽  
Data Set ◽  
Wheat Grains ◽  
Image Processing Techniques ◽  
Neuro Fuzzy ◽  
Length Width ◽  
Processing Techniques

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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