A New Approach to the Determination of Warp-Weft Densities in Textile Fabrics by Using an Image Processing Technique

2014 ◽  
Vol 9 (1) ◽  
pp. 155892501400900 ◽  
Author(s):  
Kazım Yildiz ◽  
Volkan Yusuf Şenyürek ◽  
Zehra Yildiz ◽  
Mustafa Sabri Özen
Keyword(s):  
Processing Technique ◽  
Woven Fabrics ◽  
Woven Fabric ◽  
Image Processing Technique ◽  
Image Transformation ◽  
New Approach ◽  
Textile Fabrics ◽  
Matlab Program ◽  
Experimental Process

This paper presents a new approach for processing images of woven fabrics to determine the warp-weft densities. This approach includes three main steps, namely; image transformation, image enhancement, and analyzing signals of the image. In the experimental process, 19 different woven fabric images were scanned at a high resolution (2400 dpi); then these images were transferred to the MATLAB program. By using the vertical and horizontal frequencies of the textile image, the FFT analyses were carried out. Consequently with 97 % accuracy, the densities were predicted only by using the images instead of counting them by hand.

Determination of Two-Phase Flow Void Fraction of R600a in a Horizontal Smooth Tube

2012 ◽  
Author(s):  
Özden Ağra ◽  
Hakan Demir ◽  
Ş. Özgür Atayılmaz ◽  
Ahmet Yurtseven ◽  
A. Selim Dalkılıç ◽  
...  
Keyword(s):  
Neural Network ◽  
Void Fraction ◽  
High Speed ◽  
Saturation Temperature ◽  
Horizontal Tube ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Two Phase ◽  
Void Fractions

In this paper, the void fraction of alternative refrigerant R600a flowing inside horizontal tube is determined by means of an experimental technique, well known correlations in the literature and a generalized neural network analysis. The horizontal tube is made from smooth glass tubing of 4 mm inner diameter. The test runs are done at average saturated condensing temperatures between 30 and 40 °C while the average qualities and the mass fluxes are between 0.45–0.91 and 68.5–138.1 kg m-2s-1 respectively. The flow regime determination inside the tube is performed by means of sight glasses placed at the inlet and outlet sections of the test section, used for in-tube condensation tests, virtually. An image processing technique, performed by means of a high speed camera, is used to determine the void fractions of stratified and annular condensing flow of R600a experimentally. The void fractions are determined using relevant measured data together with 11 different void fraction models and correlations reported in the open literature analytically. Artificial neural network (ANN) analysis is developed to determine the void fractions numerically. For this aim, mass flow rate, average vapor quality, saturation temperature, liquid and vapor densities, liquid and vapor dynamic viscosities and surface tension are selected as the input parameters, while the void fraction is selected as the output. Three-layer network is used for predicting the void fraction. The number of the neurons in the hidden layer was determined by a trial and error process evaluating the performance of the network and standard sensitivity analysis. The measured void fraction values are found to be in good agreement with those from ANN analysis and correlations in the literature. It is also seen that the trained network are more predictive on the determination of void fraction than most of the investigated correlations.

scholarly journals A NEW APPROACH FOR CLASSIFICATION OF DIFFERENT WOVEN FABRIC PATTERNS AND THREAD DENSITIES WITH CONVOLUTIONAL NEURAL NETWORKS

2021 ◽  
Vol 2021 ◽  
pp. 88-93
Author(s):  
E. Gültekin ◽  
H.İ. Çelik ◽  
H.K. Kaynak
Keyword(s):  
Visual Inspection ◽  
Texture Classification ◽  
Confusion Matrix ◽  
Woven Fabrics ◽  
Woven Fabric ◽  
Visual Evaluation ◽  
Compact Structure ◽  
New Approach ◽  
Small Pore

Fabrics produced from microfilaments are superior to conventional fiber fabrics, due to their properties such as light weight, durability, waterproofness, windproofness, breathability and drapeability. Tightly woven fabrics produced from microfilament yarns have a very compact structure due to small pore dimensions between the fibers inside the yarns and between yarns themselves. It is almost very difficult to distinguish the structures of densely woven fabrics with the visual evaluation. Therefore, it is very important to automatically determine the differences in the texture properties of such fabrics. Thanks to the developments in image acquision technology and image processing methods, the texture classification of fabrics can be estimated more quickly and reliably than visual inspection. In this study, the classification of high-density microfilament woven fabrics according to different texture types and thread density was achieved by using the ResNet-50 algorithm. The obtained results were evaluated in a confusion matrix form. The classification accuracy of the CNN algorithm was measured as 0.95 on average.

scholarly journals A Multipurpose Image Processing Technique for Wireless and Wired Networks

2012 ◽  
pp. 205-209
Author(s):  
Akash Kumar Bhoi ◽  
Baidyanath Panda
Keyword(s):  
Image Processing ◽  
Communication Network ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Single Server ◽  
Receiver Side ◽  
High Quality ◽  
New Approach ◽  
Wired Networks ◽  
Prime Problem

One of the most important and challenging goal of current and future communication network is transmission of high quality images from sender to receiver side quickly with least error where limitation of bandwidth is a prime problem. Here we will discuss a new approach towards compressing and decompressing with perfect accuracy for its suitable transmission and reception. This technology is also helpful in Server and Client models used in industries where a large number of clients work over a single Server. Hence to minimize the load during transmission of a volumetric image/video this process can be implemented.

Particle Shape Analysis Using Digital Image Processing

2019 ◽  
pp. 377-391
Author(s):  
Katia Tannous ◽  
Fillipe de Souza Silva
Keyword(s):  
Image Processing ◽  
Aspect Ratio ◽  
Digital Image Processing ◽  
Shape Analysis ◽  
Processing Technique ◽  
Solid Particles ◽  
Three Dimensions ◽  
Image Processing Technique ◽  
Prolate Spheroids

This chapter will discuss new software, Particles and Geometric Shapes Analyzer (APOGEO), aiming the determination of aspect ratio and sphericity of solid particles by image processing technique without any manual work. This software can quantify the major and minor axes correlating two or three dimensions of particles (e.g., biomass, mineral, pharmaceutical, and food products) to obtain their shape. The particles can be associated with different geometries, such as rectangular parallelepiped, cylinder, oblate and prolate spheroids, and irregular. The results are presented in histograms and tables, but also can be saved in a spreadsheet.

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