Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations

2017 ◽  
Vol 40 (6) ◽  
pp. e12575 ◽  
Author(s):  
Ebenezer O. Olaniyi ◽  
Adefemi A. Adekunle ◽  
Temitope Odekuoye ◽  
Adnan Khashman
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Automatic System ◽  
Texture Feature ◽  
Texture Feature Extraction

scholarly journals Neural Network-based System for Automatic Passport Stamp Classification

2020 ◽  
Vol 49 (4) ◽  
pp. 583-607
Author(s):  
Wala Zaaboub ◽  
Lotfi Tlig ◽  
Mounir Sayadi ◽  
Basel Solaiman
Keyword(s):  
Neural Network ◽  
Automatic System ◽  
Texture Feature ◽  
Illegal Immigrants ◽  
International Tourism ◽  
Multi Layer Perceptron ◽  
Texture Feature Extraction ◽  
Mlp Neural Network ◽  
Artificial Network ◽  
Systematic Control

The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

scholarly journals Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

2020 ◽  
Vol 16 (3) ◽  
pp. 280-294 ◽  
Author(s):  
Toto Haryanto ◽  
Adib Pratama ◽  
Heru Suhartanto ◽  
Aniati Murni ◽  
Kusmardi Kusmardi ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Neural Network ◽  
Texture Feature ◽  
Gpu Acceleration ◽  
Texture Feature Extraction ◽  
Histopathology Images

Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function

2020 ◽  
Vol 79 (37-38) ◽  
pp. 27867-27890 ◽  
Author(s):  
Bishal Bhandari ◽  
Abeer Alsadoon ◽  
P. W. C. Prasad ◽  
Salma Abdullah ◽  
Sami Haddad
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Oral Cancer ◽  
Loss Function ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Deep Learning Neural Network

scholarly journals IDENTIFICATION OF NITROGEN STATUS IN Brassica juncea L.USING COLOR MOMENT GLCM AND BACKPROPAGATION NEURAL NETWORK

2020 ◽  
Author(s):  
Satya Kumara
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Brassica Juncea ◽  
Back Propagation ◽  
Texture Feature ◽  
Back Propagation Neural Network ◽  
Texture Feature Extraction ◽  
Nitrogen Status ◽  
Color Moment

Vegetables cultivation using hydroponic is becoming popular now days because of its irrigation and fertilizer efficiency. One type of vegetable which can be cultivated using hydroponic is green mustard (Brassica juncea L.) tosakan variety. This vegetable is harvested in the vegetative phase, approximately aged of 30 days after planting. In addition, during the vegetative phase, this plant requires more nitrogen for growth of vegetative organs. The lack of nitrogen will lead to slow growth and the leaves turn yellow. In this study, non-destructive technology was developed to identify nitrogen status through the image of green mustard leaf by using digital image processing and artificial neural network. The image processing method used was the color moment for color feature extraction, gray level co-occurrence matrix (GLCM) for texture feature extraction and back propagation neural network to identify nitrogen status from the image of leaf. The input image data resulted from acquisition process was RGB color image which was converted to HSV. Prior to the color and texture feature extraction and texture, acquisition image was segmented and cropped to get the leaf image only. Next Step was to conduct training using back propagation neural network with two hidden layer combinations, 20,000 iteration epoch. Accuracy of the test results using those methods was 97.82%. The result indicates those three methods is reliable to identify nitrogen status in the leaf of green mustard.

Texture feature extraction method for scale and rotation invariant image retrieval

2012 ◽  
Vol 48 (11) ◽  
pp. 626 ◽  
Author(s):  
M.H. Rahman ◽  
M.R. Pickering ◽  
M.R. Frater ◽  
D. Kerr
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Extraction Method ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Rotation Invariant ◽  
Feature Extraction Method
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