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.

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

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

SPATIAL RELATIONSHIP BETWEEN CITY CENTER AND ECONOMIC ACTIVITY CENTER ON URBAN GROWTH IN TSUNAMI-PRONE CITY: THE CASE OF BANDA ACEH, INDONESIA

2015 ◽  
Vol 75 (1) ◽  
Author(s):  
Ashfa Achmad ◽  
Sirojuzilam Hasyim ◽  
Badaruddin Rangkuti ◽  
Dwira N. Aulia
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Urban Growth ◽  
Economic Activity ◽  
Spatial Relationship ◽  
Multi Layer Perceptron ◽  
City Center ◽  
Mlp Neural Network ◽  
Activity Center ◽  
Banda Aceh

The main purpose of this study is to examine the impacts of the distance to city center (CIC) and distance to economic activity center (EAC) on urban growth. Land use/cover (LUC) map of 2005 and 2009 are used to analyze the variables. The variables were tested using Multi-Layer Perceptron (MLP) Neural Network in IDRISI®Selva. The result of MLP process shows that the distance to CIC and the distance to EAC contributed to the urban growth in Banda Aceh between 2005 to 2009. The distance to CIC more influential than the distance to EAC on urban growth.

scholarly journals Monthly rainfall forecast study in southeastern Brazil using multi-layer perceptron (MLP) neural networks

2020 ◽  
Vol 42 ◽  
pp. e4
Author(s):  
Cleber Souza Corrêa ◽  
Diogo Machado Custodio ◽  
Haroldo De Campos Velho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Prediction Error ◽  
Monthly Rainfall ◽  
Southeastern Brazil ◽  
Multi Layer Perceptron ◽  
Mlp Neural Network ◽  
Rainfall Forecast ◽  
Mlp Neural Networks

This work uses the MLP neural network technique to make monthly rainfall forecast estimates for Guarulhos airport in southeastern Brazil using a time series of approximately 70 years. Neural network structures with two or more hidden layers showed a better result, minimizing the prediction error.

scholarly journals Study on Waste Type Identification Method Based on Bird Flock Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shifeng Li ◽  
Liyu Chen
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Texture Feature ◽  
Local Optimum ◽  
Extraction Techniques ◽  
Backpropagation Neural Network ◽  
Texture Feature Extraction ◽  
Adjustment Method ◽  
Identification Method ◽  
Hidden Layer

According to the waste type identification requirement in waste classification, a waste type identification method based on a bird flock neural network (BFNN) was proposed. The problem of obtaining the feature dataset of waste images was considered, and color histogram and texture feature extraction techniques were used. The local optimum problem of a typical backpropagation neural network (BPNN) was considered, and a bird flock optimization (BFO) algorithm was proposed. The accuracy problem of the typical BPNN was considered, and a new online weight adjustment method of neurons was proposed. The number of hidden layer neurons (nodes) of the typical BPNN was considered, and an online adjustment method was proposed. The experimental results show that the recyclables (paper, plastic, glass, and cloth) and nonrecyclables can effectively be identified by the waste type identification method based on the BFNN, and the recognition accuracy is 81% which meets actual needs.

Objective evaluation of optical illusion skirt based on image texture features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenda Wei ◽  
Chengxia Liu ◽  
Jianing Wang
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Visual Illusion ◽  
Subjective Evaluation ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Optical Illusion ◽  
Content Type ◽  
Mlp Neural Network

PurposeNowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.Design/methodology/approachFirstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.FindingsResults show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.Originality/valueCompared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.

AN INTELLIGENT HYBRID APPROACH FOR CONTENT-BASED IMAGE RETRIEVAL

2002 ◽  
Vol 02 (02) ◽  
pp. 173-184 ◽  
Author(s):  
S. KULKARNI ◽  
B. VERMA
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Hybrid Approach ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Multi Layer Perceptron ◽  
Texture Feature Extraction ◽  
Benchmark Database ◽  
Hidden Layer ◽  
Auto Associative Neural Network

The paper presents an intelligent hybrid approach for content-based image retrieval based on texture feature. The proposed approach employs an Auto–Associative Neural Network (AANN) for feature extraction and a Multi–Layer Perceptron (MLP) with a single hidden layer for the classification. Two intelligent approaches such as AANN–MLP and statistical–MLP were investigated. The performance of the proposed approaches was evaluated on a large benchmark database of texture patterns. The results are very promising compared to other existing traditional and intelligent techniques. Some of the experimental results conducted during the investigation, comparative analysis of the results and suggestions to select the appropriate techniques for texture feature extraction and classification are presented in this paper.

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.

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