Automatic recognition of woven fabric pattern based on image processing and BP neural network

Simulation is normally used in order to analyze systems that are hard or impossible to describe using systems of explicit equations. In particular, this applies to highly complex dynamic systems such as most textile machines and processes.By carrying out a simulation experiment, knowledge about the real system can be gathered comparatively easily in contrast to trials on the machine or during the actual process. This includes practical experiments as well as a simulation using a computer program. Another field of application of simulations are systems that do not yet exist. This comprises the testing of new plant design concepts or for example the development of new spacecraft and aircraft or automobiles using carbon textile reinforced composites. 14.1Simulation with and without ComputerIn general, simulation methods can be divided into those that use a computer and those that do not, as shown in Fig. 14.1. Those simulations without computer can be separated into destructive and nondestructive methods. Simulations that use a computer can either be based on technical models (for example, finite element method for structural composites, computational fluid dynamics for polymer flows), on examples taken from nature (for example, artificial neural networks, evolutionary algorithms for machine setting optimization), and those based on analytical equations (for example, warp tension during weaving). Simulation Coauthor: Y.-S. Gloy 14.3 Modeling 14.3.1Types of ModelingIn order to save time and hence money, most simulation models use a simplified description of the real process or machine. Figure 14.2 shows typical models. Modeling Gray box Parts of system known ComponentsBlack box Behavior known Inner structure known White box Inner structure know Abstraction Reduction Figure 14.2 Principles of modeling White-Box ModelThis kind of model is suitable if the inner structure of the system is known. This structure is then deliberately abstracted, modified, and reduced to the most important influencing parameters. A typical example is the modeling of a weaving machine. Black-Box ModelIf the inner structure of a system is unknown but its behavior or its interaction with other systems can be observed and modeled, this is called a black-box model. A typical example is the use of neural networks to simulate textile processes. Gray-Box ModelIn many cases, only parts of a system are fully known and only a few, but not all, interactions between its components are established. The respective model is then called a gray-box model. In order to save costs, this approach is widely used. A fuzzy model can be regarded as a gray-box model in some cases. Model SimplificationIn order to simplify a model compared to reality, the following possibilities are often used:Components that are not of crucial significance are not taken into account. A trial based on factorial design can be helpful to determine the important influencing parameters.Unimportant details are neglected.The system is split into its components and these are analyzed separatel...

show abstract

“…This was achieved with only 10 rules. This idea is related to a concept derived 10 years earlier and published in Osthus (1997). …”

Section: Warp Tension In Weavingmentioning

confidence: 98%

Simulation

Gries¹,

Veit²,

Wulfhorst³

et al. 2014

Textile Technology

View full text Add to dashboard Cite

show abstract

“…The second projection, called the reverse projection, uses the mask M 2 from the bottom right point to the top left point of image to obtain the minimum value coordinate p min (x min , y min ) in the projection. The masks M 1 and M 2 are defined in Equations (10) and (11), respectively. Figure 8 presents the results with both forward and backward projections.…”

Section: Eyeglass Extractionmentioning

confidence: 99%

“…One mature application of AOI is the inspection of the printed circuit board [1][2][3][4]. Recently, the use of AOI-based defect inspection systems has been extended to several industries, such as steel [5][6][7][8][9][10], textile and garment [11][12][13], solar cells [14][15][16][17][18], plastic [19,20], and liquid crystal display (LCD) [21][22][23][24] [ [21][22][23][24] industries. Recently, glass is being used increasingly in various applications, and thus, the glass industry is growing rapidly.…”

Section: Introductionmentioning

confidence: 99%

Automatic Defect Inspection for Coated Eyeglass Based on Symmetrized Energy Analysis of Color Channels

Wang

et al. 2019

Symmetry

View full text Add to dashboard Cite

Nowadays, eyeglassesare used for vision correction as well as in the fashion industry. Eyeglasses have become more expensive and the eyewear industry has grown rapidly, thereby requiring the development of advanced coating technologies. However, defect detection by visual inspection in the manufacturing process of eyeglass coatings is difficult. To solve this problem, we propose the coated eyeglass defect detection system framework based on machine vision for real-time inspection. First, we locate and extract regions of interest (ROI) ofthe coated eyeglass by adopting cross-projection based on symmetrizedenergy analysis. Next, we propose an efficient method based on the symmetrized energy analysis of color channelsto enhance defectsin each color channel of the ROI of the coated eyeglass. Then, we adoptsymmetrized cross-projection energy analysis for locating defective areas inside the ROI of the coated eyeglass. Finally, we compare the defect detection resultsfor the coated eyeglass with the standard manufacturingquality. An experiment is conducted using real data collected froma Taiwanese eyeglass factory to validate the performance of the proposed framework. This framework achieves a 100% defect detection rate, demonstrating that it is valid and useful for inspecting coated eyeglasses in industries.

show abstract

“…Haralicck et al [8] proposed a method of gray-level cooccurrence matrix, which is used to calculate the texture characteristics. R.Pan [9] analyzed weft and warp floats to determine the fabric weave patterns. Y. Ben Salem [10] developed a supervised recognition method using support vector machine to classify fabric weave patterns and used the SVM to classify woven fabric structure.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Woven Fabric Pattern Based on TILT

Xiao

Guo

Geng

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, an effective method based on Transform Invariant Low-rank Textures (TILT) and HOG is proposed to identify woven fabric pattern. Firstly, the method based on TILT is used to solve the deflection phenomenon in the process of woven fabric image acquisition. Secondly, the yarn floats in the fabric image is localized by the yarns segmentation method based on the 2D spatial-domain gray projection, which is used to segment the weft and warp yarns. Thirdly, HOG is applied to extract distinctive invariant features in the process of feature extraction. According to the HOG feature, the texture features of the woven fabric are acquired. Finally, the yarn floats are classified by Fuzzy C-Means (FCM) clustering to recognize the weft and warp cross. Experimental results demonstrate that the proposed method can achieve the recognition of the three woven fabrics, plain, twill, and satin, and obtain accurate classification results.

show abstract

Automatic recognition of woven fabric pattern based on image processing and BP neural network

Cited by 48 publications

References 14 publications

Simulation

Simulation

Automatic Defect Inspection for Coated Eyeglass Based on Symmetrized Energy Analysis of Color Channels

Automatic Recognition of Woven Fabric Pattern Based on TILT

Contact Info

Product

Resources

About