Research of Fabric Weave Pattern’s Recognition Using Texture Orientation Features

2011 ◽  
Vol 328-330 ◽  
pp. 1763-1767
Author(s):  
Jian Qiang Shen ◽  
Xuan Zou
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Radon Transform ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Line Detection ◽  
Original Image ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for measuring fabric texture orientations and recognizing weave patterns. Wavelet transform is suited for fabric image decomposition and Radon Transform is fit for line detection in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet transform, these orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify double layer and some derivative twill weaves such as angular twill and pointed twill.

Fabric Weave Pattern’s Recognition Based on Texture Orientation Features

2013 ◽  
Vol 423-426 ◽  
pp. 2404-2408 ◽  
Author(s):  
Jian Qiang Shen ◽  
Ge Ge Li ◽  
Xuan Zou ◽  
Yan Li
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Wavelet Packet Transform ◽  
Original Image ◽  
Energy Distributions ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for representing fabric texture orientations and recognizing weave patterns. Wavelet packet transform is suited for fabric image decomposition in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet packet transform, and the regular orientations can be represented as the energy distributions of these images because the average energies of different fabric texture directions are changeable in a certain way. These energy orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify some complex weaves.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

scholarly journals Pesquisa sobre empresas multinacionais brasileiras: análises descritivas e preditivas

Internext ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. 64
Author(s):  
Mario Henrique Ogasavara ◽  
Gilmar Masiero ◽  
Marcio De Oliveira Mota ◽  
Lucas Souza
Keyword(s):  
Neural Network ◽  
Citation Analysis ◽  
Multinational Enterprises ◽  
Descriptive Analysis ◽  
Predictive Analysis ◽  
The Novel ◽  
Neural Network Approach ◽  
Novel Approach ◽  
The Neural Network ◽  
Eclectic Paradigm

<p><em>T</em>his study attempts to review recent research on the internationalization of Brazilian multinational enterprises (I-BMNEs) based on an analysis of the 174 published articles that have appeared in international and Brazilian academic journals, books, and conference proceedings. The descriptive analysis seeks to undertake a citation analysis as well as to provide a typology of the leading researchers and school affiliations, the predominating theoretical and methodological approaches. This paper also proposes a predictive analysis based on a novel approach of neural network in order to classify features of a manuscript and predict the fit of its publication. We find that the research on I-BMNEs is driven by a small number of leading institutions and researchers which utilize case studies as their research method and have the Uppsala and Eclectic Paradigm models as theoretical framework. The citation analysis shows that authors of foreigner origin are cited from journal publications or translated books. The novel technique and design of the neural network approach was modeled to fit for bibliometric studies and the outcomes of the predictive analysis were able to classify correctly 56.25% of the manuscripts. We conclude by providing a set of recommendations to advance the research on I-BMNEs.</p>

scholarly journals Using the Mask-RCNN Convolutional Neural Network to Automate the Construction of Two-Dimensional Solid Vertebral Models

2020 ◽  
Vol 20 (4) ◽  
pp. 502-516
Author(s):  
Alexander S. Beskrovny ◽  
◽  
Leonid V. Bessonov ◽  
Dmitriy V. Ivanov ◽  
Irina V. Kirillova ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Aided Design ◽  
Automatic Recognition ◽  
Medical Decision ◽  
Two Dimensional ◽  
Medical Decision Support ◽  
The Neural Network ◽  
Solid Models ◽  
Urgent Task ◽  
Medical Data Processing

Biomechanical modeling requires the construction of an accurate solid model of the object under study based on the data of a particular patient. This problem can be solved manually using modern software packages for medical data processing or using computer-aided design systems. This approach is used by many researchers and allows you to create accurate solid models, but is time consuming. In this regard, the automation of the construction of solid models suitable for performing biomechanical calculations is an urgent task and can be carried out using neural network technologies. This study presents the implementation of one of the methods for processing computed tomography data in order to create two-dimensional accurate solid models of vertebral bodies in a sagittal projection. An artificial neural network Mask-RCNN was used for automatic recognition of vertebrae. The assessment of the quality of the automatic recognition performed by the neural network was carried out on the basis of comparison with the S¨ orensen measure with manual segmentation performed by practitioners. Application of the method makes it possible to significantly speed up the process of modeling bone structures of the spine in 2D mode. The implemented technique was used in the development of a solid-state model module, which is included in the SmartPlan Ortho 2D medical decision support system developed at Saratov State University within the framework of the Advanced Research Foundation project.

scholarly journals PENERAPAN TRANSFORMASI DATA DISCRETE WAVELET TRANSFORM PADA NEURAL NETWORK UNTUK PREDIKSI HARGA SAHAM

2019 ◽  
Vol 2 (1) ◽  
pp. 17-22
Author(s):  
Indah Suryani
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Stock Prices ◽  
Data Transformation ◽  
Discrete Wavelet ◽  
Data Set ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method

Research on stock prices is still interesting for researchers. As in this study, ANTM's stock price closing data is used as a data set that is processed to be then predicted in the future. The Neural Network method is a method that is very widely used by researchers because of its various advantages. While the Discrete Wavelet Transform method is used to transform data to improve data quality so that it is expected to improve Neural Network performance. Based on experiments conducted by the Neural Network method with the Binary Sigmoid activation function which also carried out data transformation with Discrete Wavelet Transform, it has produced a smaller RMSE than prediction experiments without using data transformation with Discrete Wavelet Transform.   Keywords: Prediction, Stock Prices, Neural Network, Discrete Wavelet Transform

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