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.

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.

Patient visit forecasting in an emergency department using a deep neural network approach

Kybernetes ◽  
2019 ◽  
Vol 49 (9) ◽  
pp. 2335-2348 ◽  
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Masood Fathi ◽  
Flavio S. Fogliatto
Keyword(s):  
Neural Network ◽  
Emergency Department ◽  
Linear Regression ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Network Approach ◽  
Content Type

Purpose This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days. Design/methodology/approach In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor. Findings Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression. Research limitations/implications The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications. Originality/value To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

Facial Expression Recognition Based on Gabor Texture Features and Centre Binary Pattern

2015 ◽  
Vol 742 ◽  
pp. 257-260 ◽  
Author(s):  
Li Sai Li ◽  
Zi Lu Ying ◽  
Bin Bin Huang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Wavelet Filters ◽  
Adaboost Algorithm ◽  
Gabor Feature

This paper was proposed a new algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Centre Binary Pattern (CBP). Firstly, gabor texture feature were extracted from every expression image. Five scales and eight orientations of gabor wavelet filters were used to extract gabor texture features. Then the CBP features were extracted from gabor feature images and adaboost algorithm was used to select final features from CBP feature images. Finally, we obtain expression recognition results on the final expression features by Sparse Representation-based Classification (SRC) method. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm had a much higher recognition rate than the traditional algorithms.

scholarly journals Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network

2019 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Candra Dewi ◽  
Suci Sundari ◽  
Mardji Mardji
Keyword(s):  
Neural Network ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Color Features ◽  
Accuracy Result ◽  
Occurrence Matrix

Patchouli (Pogostemon Cablin Bent) has higher PA (Patchouli Alcohol) and oil production if grown in soil containing 75% organic matter. One way that can be used to detect the content of organic matter is to use soil images. The problem in the use of soil images is the color of the soil that is almost similar, namely the gradation between dark brown to black. Therefore, color features are not enough to be used as input in the recognition process. For this purposes, texture features are added in this study in addition to color features. The color features are extracted using color moment and the texture features are extracted using Gray Level Co-occurrence Matrix (GLCM). These feature was then chosen to get the best combination as input in the identification process using the Backpropagation Neural Network (BPNN). The system identifies the quantity of soil organic matter into five classes, namely very low, low, medium, high, and very high. The highest accuracy result obtained was 73% and MSE value 0.5122 by using five GLCM features (Angular Second Moment, contrast, correlation, Inverse Difference Moment, and entropy). This result was obtained by using the BPNN parameter, namely learning rate values 0.5, maximum iteration values of 1000, number training data 210, and total test data 12.

scholarly journals Content Based Image Retrieval using Multi-level 3D Color Texture and Low Level Color Features with Neural Network Based Classification System

2021 ◽  
Vol 15 ◽  
pp. 265-270
Author(s):  
Priyesh Tiwari ◽  
Shivendra Nath Sharan ◽  
Kulwant Singh ◽  
Suraj Kamya
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Multi Level ◽  
Different Color ◽  
Color Texture

Content based image retrieval (CBIR), is an application of real-world computer vision domain where from a query image, similar images are searched from the database. The research presented in this paper aims to find out best features and classification model for optimum results for CBIR system.Five different set of feature combinations in two different color domains (i.e., RGB & HSV) are compared and evaluated using Neural Network Classifier, where best results obtained are 88.2% in terms of classifier accuracy. Color moments feature used comprises of: Mean, Standard Deviation,Kurtosis and Skewness. Histogram features is calculated via 10 probability bins. Wang-1k dataset is used to evaluate the CBIR system performance for image retrieval.Research concludes that integrated multi-level 3D color-texture feature yields most accurate results and also performs better in comparison to individually computed color and texture features.

An identification method of cashmere and wool by the two features fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yaolin Zhu ◽  
Jiayi Huang ◽  
Tong Wu ◽  
Xueqin Ren
Keyword(s):  
Textile Industry ◽  
Texture Feature ◽  
Texture Features ◽  
Identification Accuracy ◽  
Impact Factors ◽  
Content Type ◽  
Identification Method ◽  
Feature Parameters ◽  
The Impact ◽  
Optimal Feature

PurposeThe purpose of this paper is to select the optimal feature parameters to further improve the identification accuracy of cashmere and wool.Design/methodology/approachTo increase the accuracy, the authors put forward a method selecting optimal parameters based on the fusion of morphological feature and texture feature. The first step is to acquire the fiber diameter measured by the central axis algorithm. The second step is to acquire the optimal texture feature parameters. This step is mainly achieved by using the variance of secondary statistics of these two texture features to get four statistics and then finding the impact factors of gray level co-occurrence matrix relying on the relationship between the secondary statistic values and the pixel pitch. Finally, the five-dimensional feature vectors extracted from the sample image are fed into the fisher classifier.FindingsThe improvement of identification accuracy can be achieved by determining the optimal feature parameters and fusing two texture features. The average identification accuracy is 96.713% in this paper, which is very helpful to improve the efficiency of detector in the textile industry.Originality/valueIn this paper, a novel identification method which extracts the optimal feature parameter is proposed.

Financial integration via panel cointegration approaches in ASEAN+5

2016 ◽  
Vol 43 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Najla Shafighi ◽  
Abu Hassan Shaari ◽  
Behrooz Gharleghi ◽  
Tamat Sarmidi ◽  
Khairuddin Omar
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Interest Rate ◽  
Financial Integration ◽  
Panel Cointegration ◽  
Real Output ◽  
Content Type ◽  
Mlp Neural Network ◽  
Long Run ◽  
Short Run

Purpose – The purpose of this paper is to identify whether any financial integration exists among ASEAN+5 members and some East Asian countries, including China, Japan, Korea, Hong Kong, and Taiwan, through interest rate, exchange rate, level of prices, and real output. Design/methodology/approach – Therefore, the authors intend to identify any long-term relationship among these variables utilizing the data in the most efficient manner via panel cointegration and panel unit root tests. The study likewise uses a panel-based vector error correction (panel-vec) model for comparison and also short-run relationship analysis. The long-run relationship is estimated using dynamic ordinary least square technique and a panel multi-layer perceptron (MLP) neural network. Findings – For the ten countries under consideration, the empirical result supports the long-run equilibrium relationship among real output, exchange rate, interest rate, and level of prices, and that the cointegration relationship implies unidirectional causality from exchange rate to real output. This result is favorable to a model that contains real output as a dependent variable and exchange rate, interest rate, and level of prices as explanatory variables. Panel-vec results indicate no evidence of short-run causality from exchange rate to real output. Furthermore, the comparison result of long-run equation estimation shows the superiority of neural networks over econometric models. Originality/value – This paper adds to the literature by examining the financial cointegration using a panel model that contains real exchange rate, interest rate, real output, and inflation rate in ASEAN+5. Additionally this paper applied the MLP neural network to yield a robust estimation of the long-run equation obtained among the variables.

scholarly journals Texture Feature Extraction for Batik Images Using GLCM and GLRLM with Neural Network Classification

2019 ◽  
pp. 06-15
Author(s):  
K. Chandraprabha ◽  
S. Akila
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Probabilistic Neural Network ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Long Run ◽  
Short Run ◽  
Artificial Neural

Batik has a vast variety of motifs and colors. Aside from its popularity as being part of Indonesian culture, it has become the source of Indonesia’s income. Batik was more promising in the past years for the business opportunities. Batik has economic and high export value as the commodity. Batik has become the main part of national culture; however there is a lack of understanding for many people, as they are still unaware about batik motifs and patterns. Therefore, it is needed for building a model to identify batik motifs. This study aims to combine the features of texture and the feature of shapes’ ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using gray level co-occurrence matrices (GLCM) which include Contrast, Correlation, Homogeneity and Energy. And include the Gray level Run length matrices (GLRLM) which includes Gray Level Non-Uniformity (GLN), Long Run Emphasis (LRE), Short Run Emphasis (SRE), Run Percentage (RP). At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, and the combination of GLCM and GLRLM. From the three features used in the classification of batik image with artificial neural networks it includes Probabilistic Neural network, it was obtained that GLCM feature has the lowest accuracy rate of 78% and the combination of GLCM and GLRLM features produces a greater value of accuracy by 84%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the probabilistic neural network with the combination of GLCM and GLRLM features in batik image.

Applying multivariate linear regression and multi-layer perceptron artificial neural network to design an energy consumption baseline in a low density polyethylene plant

2019 ◽  
Vol 13 (4) ◽  
pp. 1133-1148
Author(s):  
Behnam Hamedi ◽  
Alireza Mokhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Linear Regression ◽  
Energy Management ◽  
Energy Performance ◽  
Multivariate Linear Regression ◽  
Multi Layer Perceptron ◽  
Content Type ◽  
Artificial Neural

Purpose The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical. Design/methodology/approach The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE. Findings In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model. Originality/value It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

Research on underwear pressure prediction based on improved GA-BP algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Subjective Evaluation ◽  
Static State ◽  
Content Type ◽  
The Neural Network ◽  
Ga Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction

PurposeFor comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model.Design/methodology/approachThe objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified.FindingsThe results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure.Originality/valuePSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear.

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