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Relevant bibliographies by topics / Defect textures

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Academic literature on the topic 'Defect textures'

Author: Grafiati

Published: 1 February 2025

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Journal articles on the topic "Defect textures"

1

Zhou, Lei, Bingya Ma, Yanyan Dong, Zhewen Yin, and Fan Lu. "DCFE-YOLO: A novel fabric defect detection method." PLOS ONE 20, no. 1 (January 14, 2025): e0314525. https://doi.org/10.1371/journal.pone.0314525.

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Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. First, Dynamic Snake Convolution is introduced into the backbone network to enhance sensitivity to elongated and subtle defects, improving the extraction of edge and texture details. Second, a Channel Priority Convolutional Attention mechanism is incorporated after the Spatial Pyramid Pooling layer to enable more precise defect localization by leveraging multi-scale structures and channel priors. Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. Experimental results demonstrate a significant improvement in detection performance. Specifically, mAP@0.5 increased by 2.9%, precision improved by 3.5%, and mAP@0.5:0.95 rose by 2.3%, highlighting the model’s superior capability in detecting complex defects. The project is available at https://github.com/lilian998/fabric.

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Carrilho, Rui, Kailash A. Hambarde, and Hugo Proença. "A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection." Applied Sciences 14, no. 12 (June 19, 2024): 5298. http://dx.doi.org/10.3390/app14125298.

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Detecting anomalies in texture has become a significant concern across various industrial processes. One prevalent application of this is in inspecting patterned textures, especially in the domain of fabric defect detection, which is a commonly encountered scenario. This task entails dealing with a wide array of colours and textile varieties, spanning a broad spectrum of fabrics. Due to the extensive diversity in colours, textures, and defect characteristics, fabric defect detection presents a complex and formidable challenge within the realm of patterned texture inspection. While recent trends have seen a rise in the utilization of deep learning methods for anomaly detection, there still exist notable gaps in this field. In this paper, we introduce a novel dataset comprising a diverse selection of fabrics and defects from a textile company based in Portugal. Our contributions encompass the provision of this unique dataset and the evaluation of state-of-the-art (SOTA) methods’ performance on our dataset.

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Zhang, Yuming, Zhongyuan Gao, Chao Zhi, Mengqi Chen, Youyong Zhou, Shuai Wang, Sida Fu, and Lingjie Yu. "A novel defect generation model based on two-stage GAN." e-Polymers 22, no. 1 (January 1, 2022): 793–802. http://dx.doi.org/10.1515/epoly-2022-0071.

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Abstract The fabric defect models based on deep learning often demand numerous training samples to achieve high accuracy. However, obtaining a complete dataset containing all possible fabric textures and defects is a big challenge due to the sophisticated and various fabric textures and defect forms. This study created a two-stage deep pix2pixGAN network called Dual Deep pix2pixGAN Network (DPGAN) to address the above problem. The defect generation model was trained based on the DPGAN network to automatically “transfer” defects from defected fabric images to clean, defect-free fabric images, thus strengthening the training data. To evaluate the effectiveness of the defect generation model, extensive comparative experiments were conducted to assess the performance of the fabric defect detection before and after data enhancement. The results indicate that the detection accuracy was improved regarding the belt_yarn, hole, and stain defect.

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Shi, Hui, Gangyan Li, and Hanwei Bao. "Lightweight Reconstruction Network for Surface Defect Detection Based on Texture Complexity Analysis." Electronics 12, no. 17 (August 27, 2023): 3617. http://dx.doi.org/10.3390/electronics12173617.

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Deep learning networks have shown excellent performance in surface defect recognition and classification of certain industrial products. However, most industrial product defect samples are scarce and have a wide variety of defect types, making methods that require a large number of defect samples for training unsuitable. In this paper, a lightweight surface defect detection network (LRN-L) based on texture complexity analysis is proposed. Only a large number of defect-free samples, which can be easily obtained, are needed to detect defects. LRN-L includes two stages: texture reconstruction stage and defect localization stage. In the texture reconstruction phase, a lightweight reconstruction network (LRN) based on convolutional autoencoder is designed, which can reconstruct defect-free texture images; a loss function combining structural loss and L1 loss is proposed to improve the detection effect; we built a calculation model for image complexity, calculated the texture complexity for texture samples, and divided textures into three levels based on complexity. In the defect localization stage, the residual between the reconstructed image and the original image is taken as the possible region of the defect, and the defect localization is realized via a segmentation algorithm. In this paper, the network structure, loss function, texture complexity and other factors of LRN-L are analyzed in detail and compared with other similar algorithms on multiple texture datasets. The results show that LRN-L has strong robustness, accuracy and generalization ability, and is more suitable for industrial online detection.

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Li, Feng, Lina Yuan, Kun Zhang, and Wenqing Li. "A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix." Textile Research Journal 90, no. 7-8 (October 1, 2019): 776–96. http://dx.doi.org/10.1177/0040517519879904.

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A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gray-level co-occurrence matrix, an unpatterned fabric-defect detection scheme is proposed, including texture-feature extraction and detection stages. In the first stage, the multidirectional texture-feature matrix of a nondefective fabric image is extracted, and then the detection threshold is determined based on the similarity between the feature matrices. In the second stage, the defect is detected with the detection threshold. The proposed method is adapted to various grayscale textile images with different characteristics and is robust to a wide variety of image-processing operations. In addition, it is invariant to grayscale changes, performs well when representing textures and detecting defects and has lower computational complexity than other methods.

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Mo, Dongmei, and Wai Keung Wong. "Fabric Defect Classification based on Deep Hashing Learning." AATCC Journal of Research 8, no. 1_suppl (September 2021): 191–201. http://dx.doi.org/10.14504/ajr.8.s1.23.

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Classifying categories of fabric defects can greatly help to identify the source of causing fabric defects in the textile manufacturing process. Most existing artificial intelligence based methods focus on identifying and locating defective regions and do not analyze the categories of the defects. On the other hand, as current fabric defect detection methods depend on handcrafted features, they can only handle fabric with specific patterns or textures. In this paper, we propose a novel model which can learn high-level representation from the automatic observations of the input images that can recognize the categories of the defects for various fabric patterns and textures, instead of only locating defects on specific patterns. Experimental results show that the proposed method is superior to the state-of-the-art deep hash methods in terms of fabric defect classification.

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Li, Jianqi, Binfang Cao, Fangyan Nie, and Minhan Zhu. "Feature Extraction of Foam Nickel Surface Based on Multi-Scale Texture Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (March 20, 2019): 175–82. http://dx.doi.org/10.20965/jaciii.2019.p0175.

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In the foam nickel process, texture is the indicator of foam nickel performance. In order to recognize foam nickel surface defects accurately and provide guidance for production operations, this paper proposes a method for extracting foam nickel image textures based on multi-scale texture analysis. First, nonsubsampled contourlet (NSCT) is used to carry out foam nickel image multi-scale decomposition, and the low-frequency and high-frequency components following decomposition are used to characterize different defect details. Then, the Haralick eigenvalue, which measures the foam nickel image texture information at each sub-band, is calculated. The KPCA and optimal DAG-SVM are adopted in order to reduce the parameter dimension and clarify defects. Tests are carried out on the foam nickel surface image samples, including crack, scratch, pollution, leakage, and indentation tests. The results indicate that the method proposed in this paper can extract different pieces of detailed texture information and can achieve a defect-identifying accuracy of up to 88.9%.

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Liu, Yang, and Weiqi Yuan. "A Distributed System-Based Multiplex Networks to Extract Texture Feature." International Journal of Distributed Systems and Technologies 13, no. 3 (July 1, 2022): 1–11. http://dx.doi.org/10.4018/ijdst.307991.

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Defect detection is an indispensable part of quality detection in manufacturing. It is a challenging task to recognize defects on the surface of castings with random textures. This paper proposes a texture extraction method based on multiplex networks for defect segmentation in a random background. The proposed method redefines the image information in the form of multiplex network topologies according to the different properties of casting surface texture. Finally, the proposed method segments different texture regions by extracting the similarity of texture primitives in the multiplex networks. The study conducted experiments in a distributed system environment, and the results show that the proposed method is effective in actual industrial data sets. As an interdisciplinary application of network science and machine vision, the proposed method provides a valuable application mode for the development of complex networks in new fields and provides a new research idea for the texture analysis of castings.

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Zhang, Huanhuan, Jinxiu Ma, Junfeng Jing, and Pengfei Li. "Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means." Applied Sciences 9, no. 17 (August 26, 2019): 3506. http://dx.doi.org/10.3390/app9173506.

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In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.

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Song, K. Y., J. Kittler, and M. Petrou. "Defect detection in random colour textures." Image and Vision Computing 14, no. 9 (October 1996): 667–83. http://dx.doi.org/10.1016/0262-8856(96)84491-x.

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Dissertations / Theses on the topic "Defect textures"

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Xie, Xianghua. "Defect detection in random colour textures." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425096.

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Afghah, Seyedeh Sajedeh. "MODELING SKYRMIONS, DEFECT TEXTURES, AND ELECTRICAL SWITCHINGIN LIQUID CRYSTALS." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532952208004472.

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Mahyaoui, Camille. "Exploitation des textures de phases cristal-liquides pour diverses applications optiques." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP118.

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Les cristaux liquides sont utilisés pour leurs propriétés électro-optiques depuis les années 1970, que ce soit dans les écrans LCDs ou pour des applications moins répandues telles que le vitrage actif. L'industrie verrière s'intéresse particulièrement à cette dernière. La technologie actuellement commercialisée exploite une matrice polymère dans laquelle sont dispersées des gouttelettes de cristal liquide en phase nématique. Sous l'effet d'une tension électrique, le vitrage passe d'un état diffusant à transparent. Cependant, la transparence de ce second état n'est pas optimale, ce qui a motivé la recherche d'autres solutions techniques. Il a été montré récemment que la phase smectique A pouvait également être utilisée pour concevoir un prototype de vitrage actif : un type de défauts topologiques (coniques focales) est généré en phase smectique A et polymérisé pour être maintenu en phase nématique. Grâce à cette étape, l'échantillon passe réversiblement d'un état diffusant à un état transparent lorsqu'une tension est appliquée. Ce système appartient à la famille des PSLCs (Polymer Stabilised Liquid Crystals). Dans cette thèse, nous avons optimisé les paramètres de polymérisation (concentration en monomère, photoinitiateur, puissance UV) pour maximiser le contraste entre l'état transparent et l'état diffusant. La relation entre les propriétés électro-optiques et la microstructure des échantillons a aussi été étudiée. Le principe a ensuite été étendu à une autre phase cristal liquide, très proche formellement de la phase smectique A : la phase nématique twist-bend (NTB). Cette phase présente une grande diversité de défauts topologiques, ce qui nous a permis de non seulement montrer qu'il était possible d'utiliser la phase NTB pour le vitrage actif mais également de développer un réseau de diffraction commandable électriquement. Pour cette dernière application, la « rope-like texture » de la phase NTB a été polymérisée afin d'être maintenue en phase nématique, laquelle est connue pour s'aligner réversiblement sur le champ électrique. Nous sommes ensuite revenus à la phase smectique A qui, bien que déjà très étudiée, présente encore des propriétés peu exploitées. Nous avons cherché à exploiter le réseau quasi-hexagonal de coniques focales obtenu par simple dépôt par spin-coating. Nous avons montré que ce réseau peut être utilisé pour confiner des nanoparticules (3 nm - 10 nm). La méthode fonctionne pour plusieurs types de nanoparticules (or, quantum dots). Une étude approfondie de l'état d'agrégation des nanoparticules et de leur localisation dans la matrice cristal liquide a été conduite. Deux populations de nanoparticules ont été mises en évidence : des agrégats de dimension micrométrique flottant en surface du cristal liquide et localisés sur les défauts et des nanoparticules adsorbées sur le substrat. Une évolution de la répartition des particules adsorbées a été observée sur des temps longs : un réseau nid d'abeilles apparaît en quelques mois. Enfin, les propriétés optiques des deux types de défauts observés en phase smectique A en conditions d'ancrage hybride ont été étudiées : les coniques focales (qui apparaissent dans les échantillons d'épaisseur supérieure à 1,5 µm) et les « défauts linéaires » (observés dans les échantillons d'épaisseur inférieure à 1,5 µm). Les coniques focales diffusent la lumière et confèrent à l'échantillon un aspect occultant tandis que les défauts linéaires diffractent la lumière, rendant l'échantillon coloré. Nous avons montré d'une part que les propriétés diffusantes des coniques focales sont d'autant plus importantes que la couche de cristal liquide est épaisse et d'autre part que les défauts linéaires se comportent bien comme un réseau de diffraction. La structure de ces deux types de défauts a été étudiée par microscopie optique. Un modèle à partir de cyclides de Dupin a été proposé pour les coniques focales mais la structure des défauts linéaires n'est pas encore complètement élucidée
Liquid crystals have been used for their electro-optical properties since the 1970s, both in LCD screens and for less widespread applications such as smart glass. The glass industry is particularly interested in the latter. The technology currently on the market uses a polymer matrix in which droplets of liquid crystal in the nematic phase are dispersed. Under the effect of an electrical voltage, the glass switches from a scattering to a transparent state. However, the transparency of this second state is not optimal, which has prompted the search for other technical solutions. It has recently been shown that the smectic A phase can also be used to design a smart glass prototype: a type of topological defect (focal conic domains) is generated in the smectic A phase and polymerised to be maintained in the nematic phase. Thanks to this step, the sample reversibly changes from a scattering state to a transparent state when a voltage is applied. This system belongs to the family of PSLCs (Polymer Stabilised Liquid Crystals). In this thesis, we optimised the polymerisation parameters (monomer concentration, photoinitiator, UV light intensity) to maximise the contrast between the transparent and scattering states. The relationship between the electro-optical properties and the microstructure of the samples was also studied. The principle was then extended to another liquid crystal phase that is formally very close to the smectic A phase: the twist-bend nematic (NTB) phase. This phase exhibits a wide variety of topological defects, enabling us not only to show that the NTB phase can also be used for smart glass applications, but also to develop an electrically tunable diffraction grating. For the latter application, the ‘rope-like texture' of the NTB phase was polymerised to be maintained in the nematic phase, which is known to align reversibly along the electric field. We then revisited the smectic A phase, which has already been extensively studied, but whose properties have not yet been fully investigated. In particular, we sought to make use of the quasi-hexagonal lattice of focal conic domains that is obtained by simple spin-coating deposition. We have shown that this lattice can be used to confine nanoparticles (3 nm - 10 nm). The method works for several types of nanoparticles (gold, quantum dots). An in-depth study of the aggregation state of nanoparticles and their location in the liquid crystal matrix was carried out. Two populations of nanoparticles were identified: micrometre-sized aggregates floating on the surface of the liquid crystal and localised on the defects, and nanoparticles adsorbed on the substrate. An evolution in the distribution of adsorbed particles was observed over long periods: a honeycomb lattice appeared in a few months. Finally, the optical properties of the two types of defects observed in the smectic A phase under hybrid anchoring conditions were studied: focal conic domains (which appear in samples thicker than 1.5 µm) and ‘linear defects' (observed in samples thinner than 1.5 µm). The focal conic domains scatter light and give the sample a hazy appearance, while the linear defects diffract visible light, giving the sample a structural colour. We have shown that the scattering properties of the focal conics are enhanced the thicker the liquid crystal layer, and that the linear defects behave as a diffraction grating. The structure of these two types of defects was studied using optical microscopy. A model based on Dupin's cyclides was proposed for focal conic domains. The structure of linear defects has not yet been fully elucidated

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Song, Keng Yew. "Surface defect detection on textured background." Thesis, University of Surrey, 1993. http://epubs.surrey.ac.uk/844113/.

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This thesis addresses the problem of defect detection on complex textural surfaces. In general, whether the texture to be inspected is regular or random, in image terms it is characterized by local variations in pixel grey level values. These normal variations render the problem of texture defect detection extremely difficult as defects are often manifested by grey level changes and their detection requires more than mere pixel comparisons. In the thesis, classical techniques on texture representation are studied and various existing texture defect detection algorithms are reviewed. Three novel algorithms have been developed to tackle the problem of defect detection on random or regular textures. The first two are devoted to the problem of crack detection and the third algorithm is devoted to the problem of detecting regional defects. For texture crack detection, a cojoint spatial and spatial frequency representation, that is, wigner distribution is proposed to model the inspected texture surface. A detailed analysis of the wigner distribution, its properties and the effect of windowing on its crack detection performance are carried out. Two postprocessing methods, ie, probabilistic relaxation labelling and linear filtering are incorporated into the crack detection algorithm to refine the results. The potential of the Wigner model has also been explored by modifying the crack detection algorithm so as to detect other types of defects. For real world applications, an efficient crack detection algorithm based on a new distribution is proposed. The algorithm is shown to produce comparable results but in much shorter time. For regional defect detection, a hybrid chromato-structural approach to colour texture representation is proposed where combined colour texture information is extracted from various chromatic classes associated with the inspected surface. In the approach, a unified defect detection framework which combines a new colour clustering scheme, morphological smoothing and blob analysis are used to capture the relevant combined colour texture information. With this framework, good defect detection results are obtained and presented in this thesis.

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Pathak, Ajay Kumar. "Automated defect detection in textured materials." Thesis, Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23295168.

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Ngan, Yuk-tung Henry. "Motif-based method for patterned texture defect detection." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/b40203608.

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Ngan, Yuk-tung Henry, and 顏旭東. "Motif-based method for patterned texture defect detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203608.

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Ngan, Yuk-tung Henry, and 顏旭東. "Patterned Jacquard fabric defect detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30070880.

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Piepmeier, Jenelle Armstrong. "Textural analysis for defect detection in automated inspection systems." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/19108.

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Barrett, Heather A. "A COMPARATIVE TRANSMISSION ELECTRON MICROSCOPY INVESTIGATION OF DEFECTS AND TEXTURES IN CRYPTOMELANE." Miami University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=miami1375787636.

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Books on the topic "Defect textures"

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National Seminar on the Application of Textures in Materials Research (1st 1997 Hyderabad, India). Textures in materials research: Proceedings of the First National Seminar on the Application of Textures in Materials Research (NASAT-97) held in [sic] Dec. 4-5, 1997 at the Defence Metallurgical Research Laboratory, Hyderabad (India). Enfield, N.H: Science Publishers, 1999.

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Shields, David Dwayne. Recent attempts to defend the Byzantine text of the Greek New Testament. Ann Arbor, MI: University Microfilms International, 2000.

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Shields, David Dwayne. Recent attempts to defend the Byzantine text of the Greek New Testament [microform]. Ann Arbor, MI: University Microfilms International, 1986.

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Purushothama, B., and H. V. Sreenivasamurthy. Texturising: Defects, Causes, Effects, Remedies and Prevention Through Quality Management. Woodhead Publishing India PVT. LTD, 2018.

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Purushothama, B., and H. V. Sreenivasamurthy. Texturising: Defects, Causes, Effects, Remedies and Prevention Through Quality Management. Woodhead Publishing India PVT. LTD, 2018.

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Taylor-Jones, Kate. Rhythm, Texture, Moods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190254971.003.0013.

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This chapter directly engages with Ozu as the site of a postcolonial aesthetic and places him alongside French director Claire Denis, who has expressed how Ozu inspires her own work. The hegemonic experiences of the colonizing nation are at the heart of many of Claire Denis’s films as she struggles to articulate the French postcolonial moment. While the environments that Ozu was working inside of are not part of the prototypical representational dynamics of the colonial and postcolonial moment, nevertheless can an interrogative and postcolonial aesthetic possibly be found in his work in the year following Japan’s defeat? This chapter examines how both directors deconstruct and evaluate the multiplicities and tensions that exist in the colonial and postcolonial bodies via the rhythm, textures, and moods inherent in their respective filmic oeuvres. This will involve seeing the postcolonial aesthetic as situated as an affective part of the film language.

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Wickham, Chris. Sleepwalking into a New World. Princeton University Press, 2018. http://dx.doi.org/10.23943/princeton/9780691181141.001.0001.

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Amid the disintegration of the Kingdom of Italy in the eleventh and twelfth centuries, a new form of collective government—the commune—arose in the cities of northern and central Italy. This book takes a bold new look at how these autonomous city-states came about, and fundamentally alters our understanding of one of the most important political and cultural innovations of the medieval world. The book provides richly textured portraits of three cities—Milan, Pisa, and Rome—and sets them against a vibrant backcloth of other towns. It argues that, in all but a few cases, the élite of these cities and towns developed one of the first nonmonarchical forms of government in medieval Europe, unaware that they were creating something altogether new. The book makes clear that the Italian city commune was by no means a democracy in the modern sense, but that it was so novel that outsiders did not know what to make of it. It describes how, as the old order unraveled, the communes emerged, governed by consular elites “chosen by the people,” and subject to neither emperor nor king. They regularly fought each other, yet they grew organized and confident enough to ally together to defeat Frederick Barbarossa, the German emperor, at the Battle of Legnano in 1176. This book reveals how the development of the autonomous city-state took place, which would in the end make possible the robust civic culture of the Renaissance.

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Book chapters on the topic "Defect textures"

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Xie, Xianghua, and Majid Mirmehdi. "Texture Exemplars for Defect Detection on Random Textures." In Pattern Recognition and Image Analysis, 404–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_46.

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Goodby, J. W. "Introduction to Defect Textures in Liquid Crystals." In Handbook of Visual Display Technology, 1897–924. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14346-0_82.

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Goodby, J. W. "Introduction to Defect Textures in Liquid Crystals." In Handbook of Visual Display Technology, 1–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-35947-7_82-2.

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Goodby, J. W. "Introduction to Defect Textures in Liquid Crystals." In Handbook of Visual Display Technology, 1289–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-79567-4_82.

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López, Fernando, José Manuel Prats, Alberto Ferrer, and José Miguel Valiente. "Defect Detection in Random Colour Textures Using the MIA T2 Defect Maps." In Lecture Notes in Computer Science, 752–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11867661_68.

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Siegmund, Dirk, Ashok Prajapati, Florian Kirchbuchner, and Arjan Kuijper. "An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures." In Progress in Artificial Intelligence and Pattern Recognition, 77–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01132-1_9.

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Schael, Marc. "Texture Defect Detection Using Invariant Textural Features." In Lecture Notes in Computer Science, 17–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45404-7_3.

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Chetverikov, Dmitry, and Krisztián Gede. "Textures and structural defects." In Computer Analysis of Images and Patterns, 167–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63460-6_114.

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Singh, Shri. "Defects and Textures in Liquid Crystals." In Handbook of Liquid Crystals—Volume II, 285–389. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52621-3_6.

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Lv, Ying, Xiaodong Yue, Qiang Chen, and Meiqian Wang. "Fabric Defect Detection with Cartoon–Texture Decomposition." In Artificial Intelligence on Fashion and Textiles, 277–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_33.

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Conference papers on the topic "Defect textures"

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Akter, Tanjila, Abu Salaho As Samman, Anamika Hossain Lily, Md Sadekur Rahman, Nuzhat Noor Islam Prova, and Md Imran Kabir Joy. "Deep Learning Approaches for Multi Class Leather Texture Defect Classifcation." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725952.

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Wan, Jingyan, Changsheng Zhou, and Xu Zhao. "Self-Supervised Fast Texture Defect Detection Based on Salient Object Detection." In 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), 558–63. IEEE, 2024. http://dx.doi.org/10.1109/rcar61438.2024.10671027.

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Wang, Haiwen, Charles C. Wojcik, and Shanhui Fan. "Topological defects and textures of photonic spin." In CLEO: Fundamental Science. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_fs.2023.fth3c.6.

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We introduce a type of point defects in electromagnetic waves: topological spin defect, and provide examples of them. Such defect points have nontrivial topological spin textures surrounding them therefore have quantized topological charges.

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Mirmahdavi, S. A., A. Ahmadyfard, A. A. Shahraki, and P. Khojasteh. "A Novel Modeling of Random Textures Using Fourier Transform for Defect Detection." In 2013 UKSim 15th International Conference on Computer Modelling and Simulation (UKSim 2013). IEEE, 2013. http://dx.doi.org/10.1109/uksim.2013.95.

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Ralló, Miquel, María S. Millán, and Jaume Escofet. "Unsupervised local defect segmentation in textures using Gabor filters: application to industrial inspection." In SPIE Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2009. http://dx.doi.org/10.1117/12.826157.

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Skripkina, D. V., and A. V. Levitin. "Comparative Analysis of One-class and Two-class Support Vector Machines for Detecting Textural Anomalies in Leather Images." In 33rd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/graphicon-2023-503-508.

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The article presents a comparative analysis of the one-class (one-class SVM) and two-class (twoclass SVM) support vector machine for automating the detection of defects in skin images caused by its linear deformation. The task of detecting textural anomalies of the skin is very relevant, and is used in all industries related to the processing of leather into leather products. Real skin images were used in the research. Abnormal textures were obtained as a result of linear computer deformation (stretching and compression) of the original samples. Local binary patterns (LBP) are used as texture features. The evaluation of the quality of work is carried out using the proportion of correct answers of the algorithm. The influence of the deformation depth of anomalous samples on the quality of one-class and two-class methods in the absence of interference and in the presence of salt-pepper interference is analyzed.

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Tang, S. H., and S. Wu. "Deformation-Induced Microstructure Effects on Ultrasonic Waves: Simple Shear Versus Pure Shear." In ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/pvp2006-icpvt-11-93131.

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Although similar in total strain in simple shear and pure shear, they are very diverse in deformation modes, and the effect of these deformation modes on ultrasonic waves is very different. This paper aims to investigate deformation-induced microstructures and their effects on ultrasonic waves under simple and pure shear states. Texture evolutions, plastic spins, point defects induced by cross slips and fractal dimensions are analyzed via finite element polycrystal model. The investigation indicates that the texture evolutions are the same and transverse wave velocities depend on textures mainly; however, the point defects induced by cross slips show a striking difference between simple shear and pure shear, this implies that longitudinal wave velocities are sensitively influenced by point defects during plastic deformation.

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Tout, karim, and patrick Bouteille. "Defect detection on inductive thermography images using convolutional neural networks." In 16th Quantitative InfraRed Thermography conference. QIRT Council, 2022. http://dx.doi.org/10.21611/qirt.2022.3037.

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Inductive infrared thermography has been proven as an interesting solution for the inspection of surface defects. However, inductive thermography images can be noisy or present large variations in contrast and texture. Combine that with the large variability in surface defects shapes, sizes and types, the defect detection task becomes very complex. Defect detection methods based on convolutional neural network (CNN) proved their efficiency for complex detection tasks. This paper discusses two main approaches of defect detection with CNN : classification and object detection. Detection results are presented along with the advantages and weaknesses of each approach for real-time defect detection

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Schwarzer, Heiko, and Stephan Teiwes. "Detection and classification of structural defects on textured surfaces." In Optics in Computing. Washington, D.C.: Optica Publishing Group, 1997. http://dx.doi.org/10.1364/oc.1997.jwa.5.

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Automated product inspection is a highly important issue in industrial manufacturing. A typical problem is the examination of structured surfaces of fabricated items in order to identify defects. Usually, one has to deal with ”endless” materials transported at high speeds, such as breadths of cloth, carpet, plastic foil, or the like. These materials possess a surface texture that is characterized by a certain global regularity but also by slight local variations of indeterministic nature. Fabrication errors can be identified as structures that violate the global regularity. An example for a common cloth texture carrying a typical fabrication error is depicted in the leftmost image of fig. 2. The examination procedure can be divided into the detection and the classification of defects. Whereas the primary goal of the detection is to localize all irregularities, the latter part refers to the distinction of different defect types.

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Zhou, Hao, Yixin Chen, David Troendle, and Byunghyun Jang. "One-Class Model for Fabric Defect Detection." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112314.

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An automated and accurate fabric defect inspection system is in high demand as a replacement for slow, inconsistent, error-prone, and expensive human operators in the textile industry. Previous efforts focused on certain types of fabrics or defects, which is not an ideal solution. In this paper, we propose a novel one-class model that is capable of detecting various defects on different fabric types. Our model takes advantage of a well designed Gabor filter bank to analyze fabric texture. We then leverage an advanced deep learning algorithm, autoencoder, to learn general feature representations from the outputs of the Gabor filter bank. Lastly, we develop a nearest neighbor density estimator to locate potential defects and draw them on the fabric images. We demonstrate the effectiveness and robustness of the proposed model by testing it on various types of fabrics such as plain, patterned, and rotated fabrics. Our model also achieves a true positive rate (a.k.a recall) value of 0.895 with no false alarms on our dataset based upon the Standard Fabric Defect Glossary.

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Reports on the topic "Defect textures"

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Baete, Christophe. PR-405-153600-R01 Validation of the AC Corrosion Criteria Based on Real-World Pipeline Measurements. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2019. http://dx.doi.org/10.55274/r0011592.

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This report refers to the activities performed in the frame of PRCI project on the refinement of the AC corrosion criteria by evaluating real-world pipeline AC corrosion cases and applying an improved AC corrosion prediction model. The diagrams of AC corrosion likelihood assessment in the standard ISO18086:2015 was used as a starting point. Correlations between field data (especially those provided through dig reports from AC corrosion anomalies) and the simulation results for different CP polarization levels, AC induced voltage, soil conditions (texture, soil resistivity and moisture) and coating defect properties (size and thickness) were investigated. The final goal is achieving a further refinement of the proposed AC corrosion criteria in previous project (EC-6-2) based on long-term AC corrosion behavior. This document has a related webinar.

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Shomer, Ilan, Ruth E. Stark, Victor Gaba, and James D. Batteas. Understanding the hardening syndrome of potato (Solanum tuberosum L.) tuber tissue to eliminate textural defects in fresh and fresh-peeled/cut products. United States Department of Agriculture, November 2002. http://dx.doi.org/10.32747/2002.7587238.bard.

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The project sought to understand factors and mechanisms involved in the hardening of potato tubers. This syndrome inhibits heat softening due to intercellular adhesion (ICA) strengthening, compromising the marketing of industrially processed potatoes, particularly fresh peeled-cut or frozen tubers. However, ICA strengthening occurs under conditions which are inconsistent with the current ideas that relate it to Ca-pectate following pectin methyl esterase (PME) activity or to formation of rhamnogalacturonan (RG)-II-borate. First, it was necessary to induce strengthening of the middle lamellar complex (MLX) and the ICA as a stress response in some plant parenchyma. As normally this syndrome does not occur uniformly enough to study it, we devised an efficient model in which ICA-strengthening is induced consistently under simulated stress by short-chain, linear, mono-carboxylic acid molecules (OAM), at 65 oC [appendix 1 (Shomer&Kaaber, 2006)]. This rapid strengthening was insufficient for allowing the involved agents assembly to be identifiable; but it enabled us to develop an efficient in vitro system on potato tuber parenchyma slices at 25 ºC for 7 days, whereas unified stress was reliably simulated by OAMs in all the tissue cells. Such consistent ICA-strengthening in vitro was found to be induced according to the unique physicochemical features of each OAM as related to its lipophilicity (Ko/w), pKa, protonated proportion, and carbon chain length by the following parameters: OAM dissociation constant (Kdiss), adsorption affinity constant (KA), number of adsorbed OAMs required for ICA response (cooperativity factor) and the water-induced ICA (ICAwater). Notably, ICA-strengthening is accompanied by cell sap leakage, reflecting cell membrane rupture. In vitro, stress simulation by OAMs at pH<pKa facilitated the consistent assembly of ICAstrengthening agents, which we were able to characterize for the first time at the molecular level within purified insoluble cell wall of ICA-strengthened tissue. (a) With solid-state NMR, we established the chemical structure and covalent binding to cell walls of suberin-like agents associated exclusively with ICA strengthening [appendix 3 (Yu et al., 2006)]; (b) Using proteomics, 8 isoforms of cell wall-bound patatin (a soluble vacuolar 42-kDa protein) were identified exclusively in ICA-strengthened tissue; (c) With light/electron microscopy, ultrastructural characterization, histochemistry and immunolabeling, we co-localized patatin and pectin in the primary cell wall and prominently in the MLX; (d) determination of cell wall composition (pectin, neutral sugars, Ca-pectate) yielded similar results in both controls and ICA-strengthened tissue, implicating factors other than PME activity, Ca2+ or borate ions; (e) X-ray powder diffraction experiments revealed that the cellulose crystallinity in the cell wall is masked by pectin and neutral sugars (mainly galactan), whereas heat or enzymatic pectin degradation exposed the crystalline cellulose structure. Thus, we found that exclusively in ICA-strengthened tissue, heat-resistant pectin is evident in the presence of patatin and suberinlike agents, where the cellulose crystallinity was more hidden than in fresh control tissue. Conclusions: Stress response ICA-strengthening is simulated consistently by OAMs at pH< pKa, although PME and formation of Ca-pectate and RG-II-borate are inhibited. By contrast, at pH>pKa and particularly at pH 7, ICA-strengthening is mostly inhibited, although PME activity and formation of Ca-pectate or RG-II-borate are known to be facilitated. We found that upon stress, vacuolar patatin is released with cell sap leakage, allowing the patatin to associate with the pectin in both the primary cell wall and the MLX. The stress response also includes formation of covalently bound suberin-like polyesters within the insoluble cell wall. The experiments validated the hypotheses, thus led to a novel picture of the structural and molecular alterations responsible for the textural behavior of potato tuber. These findings represent a breakthrough towards understanding of the hardening syndrome, laying the groundwork for potato-handling strategies that assure textural quality of industrially processed particularly in fresh peeled cut tubers, ready-to-prepare and frozen preserved products.

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Swan, Megan, and Christopher Calvo. Site characterization and change over time in semi-arid grassland and shrublands at three parks?Chaco Culture National Historic Park, Petrified Forest National Park, and Wupatki National Monument: Upland vegetation and soils monitoring 2007?2021. National Park Service, 2024. http://dx.doi.org/10.36967/2301582.

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This report presents results of upland vegetation and soil monitoring of semi-arid grasslands at three Parks by the Southern Colorado Plateau Inventory and Monitoring Network (SCPN) from 2007?2021. The purpose is to compare and contrast five grassland ecological sites and examine how they have changed during the first 15 years of monitoring. Crews collected data on composition and abundance of vegetation, both at the species level and by lifeform (e.g., perennial grass, shrub, forb) and soil aggregate stability and soil texture at 150 plots within five target grassland/shrubland communities delineated using NRCS ecological site (ecosite) classification (30 plots per ecosite). Soils in plots at Petrified Forest NP and Chaco Culture NHP were deeper than those at Wupatki NM. Undifferentiated soil crust comprised the largest component of the soil surface, except at Wupatki where surface gravel dominated. Cover of biological soil crust (cyanobacteria, lichen, and moss) was low. Soil aggregate stability was moderate. From 2007?2021, SCPN crews identified 283 unique plant species. Overall live foliar cover ranged from 12-24%. Four of five ecological sites were dominated by C4 grass species (>70% of total live foliar cover). Shrubs co-dominated at one site (WUPA L) and forbs were an overall small component of total vegetation cover but contributed most of the diversity in these sites. Less than 4% of species detected were nonnative. Russian thistle (Salsola tragus) was the most frequently sampled nonnative, occurring in > 50% of plots at Wupatki in the volcanic upland ecological site. Cheatgrass (Bromus tectorum) was the second most common invasive species but occurred in < 10% of the plots at all ecological sites. Vegetation cover was modeled using Bayesian hierarchical models and included seasonal climatic water deficits, year effects and topographic variables as covariates. Models revealed significant negative time trends (i.e., changes over time that were not explained by changes in seasonal deficit covariates included) in some modeled responses, particularly in the cover of perennial grass at all five ecological sites. Time trends in shrub and forb responses were mixed. Species richness showed variable effects by ecosite, decreasing at CHCU S, and increasing at PEFO S and WUPA V. Modeled responses were influenced by climate covariates, but direction of these effects varied. The most consistent effects were that greater July water stress and higher accumulated growing degree days (i.e., warmer spring temperatures) increased cover of perennial grasses and shrubs during the same year. However, greater water stress in the spring had a negative effect on many responses as expected. Decreasing cover of perennial grass and increasing cover of shrubs and weedy forbs has been predicted for southwestern grasslands in response to increasing aridification due to anthropogenic climate change. Perennial grass trends reported here correspond with these predictions with mixed results on shrub and forb community trends. Continued drought conditions will likely exacerbate negative changes in these systems.

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