Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks

2017 ◽  
Vol 40 (6) ◽  
pp. e12558 ◽  
Author(s):  
Kamran Kheiralipour ◽  
Abbas Pormah
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Shape Features ◽  
Cucumber Fruit ◽  
Artificial Neural

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.

scholarly journals Detection of Cataract Through Feature Extraction by the Novel Angular Binary Pattern (NABP) and Classification by Kernel Based Convolutional Neural Networks

2021 ◽  
Author(s):  
A Sirajudeen ◽  
Anuradha balasubramaniam ◽  
S Karthikeyan
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Texture Features ◽  
Processing Technique ◽  
Image Processing Technique ◽  
The Novel ◽  
Shape Features ◽  
Visual Interpretation ◽  
Color Features

Abstract Cataract is a condition of the opacity in the lenticular regions, which usually results in bad visual interpretation of the viewed object or any entity. Hence the timely detection of cataract is considered to be significant and can even contribute in the prevention from loss of fight that might occur if the cataract is left untreated. In this paper, detection of cataract disease is carried out based on the image processing technique. Color features, texture features and shape features are extracted separately. This study proposed a Novel Angular Binary Pattern (NABP) for the extraction of texture features. And after the extraction of features, the images are subjected to classification through the implementation of the proposed novel Kernel Based Convolutional Neural Networks. Results are obtained separately for all the three types of features. A comparison is carried out for the proposed work with existing works and based on the results obtained it can be seen that the proposed work comes up with the enhanced results than the traditional methods.

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

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.

Classification of ground-truth fire debris samples using artificial neural networks

2021 ◽  
Vol 23 ◽  
pp. 100313
Author(s):  
Nicholas A. Thurn ◽  
Taylor Wood ◽  
Mary R. Williams ◽  
Michael E. Sigman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Truth ◽  
Fire Debris ◽  
Artificial Neural
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