An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity

In the context of “double carbon”, as a traditional high energy consumption industry, the textile industry is facing the severe challenges of energy saving and emission reduction. To improve production efficiency in the textile industry, we propose the use of content-based image retrieval technology to shorten the fabric production cycle. However, fabric retrieval has high requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. This paper presents a novel method for fabric image retrieval. Firstly, we define a fine-grained similarity to measure the similarity between two fabric images. Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH. We employ list-wise learning to perform similarity embedding. The experimental results demonstrate the superiority and efficiency of the proposed hashing model, DVSH

Review of Printed Fabric Pattern Segmentation Analysis and Application

Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes

Otrzymywanie obrazu tkaniny wytworzonej z barwionych przędz przy zastosowaniu metody momentów barwnych i percepcyjnego algorytmu z mieszaniem

Due to the variety of yarn colours and arrangement, it is a challenging problem to retrieve a yarn-dyed fabric image. In this paper, yarn-dyed fabric samples are captured by the DigiEye system first, and then pattern images of the fabric images captured are simulated by pattern design software based on extracted structure parameters of the yarn-dyed fabric. For the simulated pattern image, an effective algorithm is proposed to retrieve these kinds of images by combining the colour moments method and perceptual hash algorithm. Then the pattern images retrieved are mapped back to the yarn-dyed fabric image so as to realise the yarn-dyed fabric image retrieval. In the algorithm proposed, the colour moments method is adopted to extract the colour features, and the perceptual hash algorithm is utilised to calculate the spatial features of the simulated pattern images. Then the two kinds of image features are used to compute the similarity between the input original image and each target image based on the Euclidean distance and Hamming distance. Relevant images can be retrieved in dependence on the similarity value, which is determined by calculating the optimum weighted value of the colour features’ similarity and spatial features’ similarity. In order to measure the retrieval efficiency of the method proposed, the accuracy rate and retrieval rate of image retrieval were computed in experiments using a PATTERN image database with 300 images. The experimental results show that the average accuracy rate of the method proposed is 85.30% and the retrieval rate - 53.51% when the weighted value of the colour feature similarity is fixed at 0.45 and the spatial feature similarity is 0.55. It is shown that the method presented is effective to retrieve pattern images of yarn-dyed fabric.Ze względu na różnorodność kolorów i rozmieszczenia przędz otrzymanie obrazu tkaniny wytworzonej z barwionych przędz jest trudnym zadaniem. W artykule próbki tkanin z barwionych przędz były najpierw analizowane przez system DigiEye, a następnie wykonane zostały symulacje obrazów z zastosowaniem oprogramowania do projektowania wzorów oparte na wyodrębnionych parametrach struktury tkaniny. W przypadku symulacji obrazu wzoru zaproponowano skuteczny algorytm do odzyskiwania tego rodzaju obrazów poprzez połączenie metody momentów koloru i percepcyjnego algorytmu z mieszaniem. W zaproponowanym algorytmie do wyodrębniania cech kolorów zastosowano metodę momentów barwnych, a do obliczenia cech przestrzennych symulowanych obrazów został wykorzystywany percepcyjny algorytm mieszania. Następnie użyto dwóch rodzajów cech obrazu do obliczenia podobieństwa między oryginalnym obrazem wejściowym a każdym obrazem docelowym w oparciu o odległość euklidesową i odległość Hamminga. Odpowiednie obrazy można odzyskać w zależności od wartości podobieństwa, która jest określana przez obliczenie optymalnej ważonej wartości podobieństwa cech koloru i podobieństwa cech przestrzennych. Aby zmierzyć skuteczność proponowanej metody w eksperymentach obliczono wskaźnik dokładności i szybkość pobierania obrazów, wykorzystując bazę danych obrazów PATTERN z 300 obrazami. Wyniki eksperymentalne pokazały, że średni współczynnik dokładności proponowanej metody wynosi 85,30%, a szybkość pobierania 53,51%, wartość podobieństwa cech kolorów wynosiła 0,45, a podobieństwo cech przestrzennych wynosiło 0,55. Wykazano, że prezentowana metoda jest skuteczna w przypadku otrzymywania obrazów wzorów tkanin z przędz barwionych

色纺纱线中纤维混色比例的图像检测

In order to solve the problems of the empirical spadework and time consuming when confirming the proportions of the colored fibers in the colored spun yarns,with the help of the video microscopy and image processing technology,a novel method based on a clustering algorithm is proposed to classify the color fibers and inspect the proportions of different color fibers. Firstly,the colored spun yarns are untwisted into the fibers,the colored fibers are arranged on the slide under the mild tension, and the corresponding images are captured by the video microscopy. Secondly,the gray projection method is adopted to localize the colored fibers,and the average value of R,G,B of all pixels in the center line of each colored fiber is extracted as a feature vector to characterize the colored fiber respectively. Finally, the feature vectors in RGB model are converted to the L* a* b* color model,a clustering algorithm by searching density peaks is applied into classifying the colored fibers and the proportions of each color fibers are calculated. Experiments results demonstrate that the proposed method can inspect the colors and proportions of colored fibers in the colored spun yarns with a satisfactory accuracy

Automatic Construction of Digital Woven Fabric by Using Sequential Yarn Images

In this article, a computerized method is proposed for simulating digital woven fabric (DWF) based on sequential yarn images captured from a moving yarn. A mathematical model of woven fabric structure is established by assuming that the crimped shape of yarns in weave structure is elastica, and the cross-sections of yarn in sequence image and fabric are circular and ellipse, respectively. The sequential yarn images, which are preprocessed and stitched first by image processing methods, are resized based on the mathematical model. Then a light intensity curve, which consists of radial curve model and axial curve model, is used to simulate the gray texture distribution of interlacing points in radial and axial directions. Finally, a Boole Matrix model is used to control the woven pattern. In the experiment, a slub yarn and a normal yarn samples with same count are applied to simulate gray texture fabrics. Then the gray fabrics are transformed to color fabrics based on three color maps. The fabric simulations are confined to single fabrics of plain, 2/2 matt, and 1/3 twill weaves

Pomiar parametrów geometrycznych przędzy fantazyjnej za pomocą techniki sekwencjonowania obrazów opartej na obrazie komputerowym

This article presents a computer vision method for measuring the geometrical parameters of slub yarn based on yarn sequence images captured from a moving slub yarn. An image segmentation method proposed by our earlier work was applied to segment sequence slub yarn images to obtain overlapping diameter data. Then an image stitching method was proposed to remove the overlapped data based on the normalised cross correlation (NCC) method. In order to detect the geometrical parameters of slub yarn, the frequency histogram, curve fitting , and spectrogram methods were adopted to analyse the sequence diameter data obtained. Four kinds of slub yarn with different geometrical parameters were tested using the method proposed and Uster method. The experimental results show that the detection results for slub amplitude, slub length, slub distance, and slub period obtained using the method proposed were consistent with the set values and Uster results.W artykule przedstawiono komputerową metodę pomiaru parametrów geometrycznych przędzy fantazyjnej na podstawie sekwencjonowania obrazów. Metoda segmentacji obrazu zaproponowana we wcześniejszej pracy została zastosowana do obrazów przędzy fantazyjnej w celu uzyskania danych dotyczących pomiarów średnicy. Następnie, w celu usunięcia nakładających się danych, zaproponowano metodę obróbki obrazu opartą o znormalizowaną metodę korelacji krzyżowej (NCC). W celu wykrycia parametrów geometrycznych przędzy fantazyjnej zastosowano histogram częstotliwości oraz dopasowanie krzywej i metody spektrogramowe do analizy uzyskanych danych. Za pomocą proponowanej metody i metody Uster przeanalizowano cztery rodzaje przędz fantazyjnych o różnych parametrach geometrycznych. Wyniki eksperymentalne wykazały, że wyniki detekcji amplitudy, długości, odległości i okresu wzgrubień uzyskane przy użyciu proponowanej metody były zgodne z wartościami zadanymi i wynikami Uster