Nondestructive Evaluation System for White Ginseng Quality Using Image Processing Technique

2006 ◽  
Vol 321-323 ◽  
pp. 1225-1228
Author(s):  
Seong Min Kim ◽  
Chul Soo Kim ◽  
Chong Ho Lee ◽  
Myung Ho Kim ◽  
Seung Jae Park
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Evaluation System ◽  
Image Acquisition ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Gray Level ◽  
White Ginseng ◽  
Artificial Neural

A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26%, which is comparable to the 30% in case of manual grading. The grading rate was one sample per a second.

Detection of Eye Diseases using Image Processing and Artificial Neural Networks

2020 ◽  
pp. 15-20
Author(s):  
K. Sujatha ◽  
V. Srividhya ◽  
V. Karthikeyan ◽  
L. Madheshwaran ◽  
N. P. G. Bhavani
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Eye Diseases ◽  
Artificial Neural

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

2020 ◽  
pp. 095440622094226 ◽  
Author(s):  
Amanda Campos Souza ◽  
Gulliver Catão Silva ◽  
Lecino Caldeira ◽  
Fernando Marques de Almeida Nogueira ◽  
Moisés Luiz Lagares Junior ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Intelligence ◽  
Low Carbon Steel ◽  
Support Vector ◽  
Gray Level ◽  
Low Carbon ◽  
Identification Rate ◽  
Artificial Neural ◽  
Occurrence Matrix

This work focuses on the identification of five of the most common ferritic morphologies present in welded fusion zones of low carbon steel through images acquired by photomicrographies. With this regards, we discuss the importance of the gray-level co-occurrence matrix to extract the features to be used as the input of the computational intelligence techniques. We use artificial neural networks and support vector machines to identify the proportions of each morphology and present the error identification rate for each technique. The results show that the use of gray-level co-occurrence extraction allows a less intense computational model with statistical validity and the support vector machine as a computational intelligence technique allows smaller variability when compared to the artificial neural networks.

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