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An image database of printed fabrics with repeating dot patterns was created to alleviate issues associated with management of and searches for numerous dot printed fabrics in the printing industry. The function of the database is to archive and allow retrieval of images. First, we discuss image archiving of repeating pattern-based dot printed fabrics. The color image was scanned by resolution of 200 dpi. The wavelet transformation was used to preprocess the image to obtain a scanned image 1/16 of the size of the original to be the stored image. To acquire images with repeating pattern color and repeating pattern template, the binary image of each pattern was obtained using the Sobel edge detection method and a morphological operation. Then pattern elements identical to the target pattern element were screened out. Afterwards, the centroid positions of these identical pattern elements were used to subdivide the repeating pattern color image and repeating pattern template image using a vertical vector method. Finally, the RGB 512-color histogram was used as the color feature of the dot printed fabrics, and the geometric and moment invariant feature values of the repeating pattern template image were used as the pattern feature of the dot printed fabrics. Our experimental results show that images can be acquired that are suitable for use in a dot printed fabric image database. The color and template images of the repeating patterns, which represent the image content of the printed fabrics, were obtained to create an image database of repeating pattern-based dot printed fabrics. This image database contains data on 300 printed fabrics which can be used for subsequent research on database image retrieval.

This paper proposes an automated analysis system for Tatami embroidery fabric images that automates color analysis, pattern shape analysis and texture analysis. Firstly, the RGB (red, green and blue) image of embroidery fabric is obtained by a color scanner. In color analysis, the wavelet transforms and median filter are used for RGB image preprocessing, and then the Fuzzy C-Means (FCM) clustering method is used for the binary region splitting method, the color features of statistical values of colors and number of colors can be obtained. In the pattern shape analysis, the individual pattern components are segmented by the separated colors, and the shape features of pattern components are obtained by moment invariants. Finally, in the texture analysis of Tatami embroidery fabrics, the gray image of the weave block is segmented from the pattern component region, after the entropy filter, the parallel division lines are segmented by a threshold value, and then the angle and spacing between Tatami embroidery parallel division lines are obtained by Hough transform to represent texture features. The experimental results show that this method can implement Tatami embroidery fabric color, shape and texture analyses automatically.

Image inspection by wavelet packets and a neural network classifier is presented for non-defect and six kinds of defects in knitted fabrics. The types of defect include a hole, set mark (coarse), dropped stitch, oil stain, streak, and tight end. In this study, wavelet packet decomposition of a sample image is carried out based on the best-basis wavelet packet tree with three resolution levels. The lowest-two entropy among all sub-band images and the standard deviation for the original image are selected as feature inputs of the neural network classifier. These textural features are shown in seven groups, which are separately distributed in the feature space. We gathered a total of 112 experimental samples, with 16 samples in each of the seven aforementioned categories. The results demonstrate that with the three features, 56 test samples are correctly inspected. However, the lack of one of the three features yields wrong classification of some samples. Therefore, the three features selected are definitely suitable for recognition of our knitted fabric defects and also are the smallest number of features required to give accurate inspection.

The current analysis of fabric weave diagrams requires using fabric analyzing glass to record the weave number manually. This method damages eyesight and is also very time consuming. In addition, the unweaving mode damages the weave structure of woven fabric. This study uses a computer vision system and digital image processing technology for direct non-destructive analysis of the commonly used 12 fabric textures of woven fabrics without unweaving. Moreover, it proposes an automated woven fabric weave recognition method to enhance the practicability and fault tolerance of the recognition system. Firstly, the woven fabric image was shot by using a front light source and back light source, the noise of the woven fabric image was reduced by using a median filter and the contrast was increased by using histogram equalization. The statistical threshold value was used to segment the warp yarn area and the opening operation of morphology was used to disconnect the connected blocks and erode small noise. Horizontal projection and vertical projection were used to segment the warp yarn and weft yarn. The weave diagram was drawn to improve the computing time of the gray-level co-occurrence matrix. The contrast in the gray-level co-occurrence matrix was selected as the eigenvalue. In terms of woven fabric samples, 12 target samples were obtained, the Euclidean distance classifier was used and the 12 test samples were used for the experiment. The result showed a recognition rate of 100%. The recognition system was adopted by this study to effectively recognize the woven fabric weave.

This study designed and optimized the system parameters for a photovoltaic thermal system (PV/thermal system) combined with reflectors. Moreover, it discussed the gain of electrical efficiency and thermal efficiency on the system after adding two reflectors on each of the south and north sides, and adjusting the water circulation system. As the rising angle and position of the sun varies each season, in order to make this study more rigorous, experiments were conducted in four seasons of a year. The Taguchi orthogonal array was used for experimental planning, and the optimal parameters were analyzed for electrical efficiency and thermal efficiency. The analysis of variance was conducted to examine the influential parameters, and principal component analysis was used to calculate the principal component point of each experiment. The results were employed to construct a response surface methodology model. Finally, the steepest descent method was applied to obtain the optimal parameters. The reflector theory was applied to calculate the gain of solar radiation amount after installing the reflector. Moreover, the gain was inputted into the simulation software TRNSYS to simulate the electrical power output and the water temperature in the water storage tank. The confirmatory experiments of the four seasons found that the electrical energy after installing the reflector increased by 0.117---0.183 kWh, and the thermal energy increased by 1.7---2.6 $$^{\circ }\hbox {C}$$źC. The experiment confirmed that the prediction error was $$

Embroidery fabric is different from other planar fabrics such as printed fabrics and twill fabrics. Because embroidery fabrics have inherent solid texture patterns, furry edges, voids and thickness shadows, it is very difficult to filter and simulate texture patterns and this is the bottleneck for embroidery automation. Therefore, this paper proposes the texture fitting method. The texture fitting method is a kind of nonfiltered digital image processing method. For embroidery fabrics full of multiple single-connected, single-color and single-texture closed regions, the texture fitting method can complete color and region separation, and texture simulation fast. Then the results can be output to monitors or plotters to investigate the simulation effect and it can be compared to real fabrics, or this technology can be used as a generalized filter for embroidery fabrics. This paper first addresses a combination of mean, morphological and central weighted median filters to remove light variation on embroidery surface, periodic darkness on the greige, and noised texture structures, so as to separate colors by weighted fuzzy C-means method and reshape one-dimensional image pixels to finish region separation. The second part of this paper utilizes the texture fitting method to identify stitch colors and simulate texture patterns over the whole image. By exporting the result to visual devices, we can prove the integral correctness and efficiency of the texture simulation.

Currently, there is very little literature on automatic image recognition and classification of embroidery fabrics. In today’s embroidery industry, front-end pattern-making still relies greatly on labor, using pattern-making software to carefully depict patterns and images in different colors and regions. Hence, an image analysis system that can recognize colors, regions and patterns automatically is a critical technique of improving the competitiveness of the embroidery industry. In this paper, the mean filtering method, central-weighted median filtering method and morphological operation were employed to filter out the light variation on the embroidery fabric surface structure, and a genetic algorithm was applied to distinguish images of repeat pattern embroidery from that of nonrepeat pattern embroidery. If it is a repeat pattern, then a much smaller sized subimage would be searched in the original image for the same color components and spatial structure, which could lower the computing load of the entire image greatly and is expected to achieve the processing speed required in an online real-time system. As for nonrepeat pattern embroidery images, discrete wavelet transform was applied to acquire low-frequency subimages, which can retain important image features while improving the computing efficiency. Be it a repeat or nonrepeat pattern, after obtaining subimages, specific criteria were used to determine the exact number of clusters, and the weighted fuzzy C-means method was employed to run color separation and region separation. The experiment proved that, in regard to the color embroidery images of repeat and nonrepeat patterns, the method proposed in this paper succeeded in color and region separations with good result.

This paper presents an innovative Printed Fabric Computerized Automatic Color Separation System. First, a scanner is used to obtain the digitized color image of printed fabric in RGB (red, green, blue) mode, and the image is then converted to the digitized color image in HSI (hue, saturation, intensity) mode. Next, the genetic algorithms are applied to search the smaller sub-image having the same color distribution as the original fabric for proceeding with the subsequent color separation algorithm. For the color separation algorithm, the FCM-based (Fuzzy C-Means) Region Splitting Method is used to carry out the color segmentation of the printed fabric image. The experimental result shows that the color separation method proposed by this study can quickly and correctly achieve the automatic color separation of the printed fabric.

Traditionally, fabric texture identification is based on visual inspection. Recent studies have proposed automatic recognition, which utilizes computer vision to recognize the texture of different fabrics. In the recognition process, the fabric weave patterns are identified by the warp and weft floats. However, due to the optical environments and the appearance differences of fabrics and yarns, the stability and fault-tolerance of the computer vision method are yet to be improved. By using the fabric weave patterns image identification system, this study analyzed the fabric image to find out the warp and weft by the pixel gray-level cumulative values. It then cut out the image of the warp and weft floats to obtain the texture feature values, and used the Fuzzy C-Means (FCM) algorithm to identify the warp and weft floats. The identification results can derive the black-white digital image and the digital matrix of the fabric weave patterns. Finally, weaves classification is conducted based on the successfully trained two-stage Back-Propagation Neural Network. This two-stage neural network can be used to construct the computer vision system to recognize fabric texture, and to increase the system reliability and accuracy. This study used the first-order and second-order co-occurrence matrix, and confirmed that fabric patterns can be identified and classified accurately with this method.

This article proposes an innovative color printed fabric computer color distinguishing system whose main functions are to precisely distinguish the printed fabric pattern colors and match colors to improve the current time-consuming color distinguishing conducted by manpower. The RGB color mode is an industrial color standard, by which the change and overlapping of color channels of red, green, and blue represent types of colors. RGB stands for red, green, and blue, respectively. It is one of the most widely used color system and covers almost all of the colors sensible to human vision. Hence, this paper adopts the RGB color mode to present color printed fabric images. First, to reduce the color distinguishing computation, a genetic algorithm was applied in search of small images of the same color in the original color printed fabric. Then, color distinguishing computation was conducted by a probabilistic neural network (PNN), which has the advantage of a very fast learning speed. Finally, PANTONE® standa...

A novel approach of repeat pattern segmentation is proposed for printed fabrics. Printed fabrics are captured by a color scanner and converted into full color digital files. To classify the color segmentation and pattern elements a fuzzy C-means (FCM) clustering algorithm and a specific cluster-validity criterion are used to obtain the pattern image of printed fabric. Then, the repeat pattern segmentation of the pattern image is established by Hough transform. The experimental results have shown that this systematic method is very suitable for the analysis of the repeat pattern of printed fabrics.

In this paper, a novel approach to color and pattern analysis is proposed for printed fabric. An unsupervised analysis method is developed using a fuzzy C-means (FCM) clustering algorithm and a specific cluster-validity criterion (SC criterion). First, the printed fabric is captured by a color scanner and converted into full color digital files, then the mean filter is used to smooth the color of the image. The search for good cluster numbers is made by the SC criterion, and the corresponding color clusters are obtained based on the FCM clustering algorithm. Finally, the color and pattern features of the printed fabric are calculated. The experimental results show that this approach is very suitable for analyzing the colors and patterns of printed fabrics.

A new robust recognition algorithm is proposed for fabric weave pattern recognition. The gray-level images of solid woven fabrics are captured by a color scanner and converted into digital files, then enhanced images are obtained by a gray-level morpho logical operation. Based on the interstices of yarns, warp and weft crossed areas are located, and four texture features of these areas are obtained by first-order and second- order statistics. Unsupervised decision rules for recognizing warp and weft floats are developed using a fuzzy c-means clustering method. The experimental materials include plain, twill, and satin woven fabrics. Experimental results demonstrate that three basic weave patterns can be clearly identified.