Identification of wool and mohair fibres with texture feature extraction and deep learning

Yildiz, Kazım

doi:10.1049/iet-ipr.2019.0907

Cited by 25 publications

(17 citation statements)

References 37 publications

(42 reference statements)

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“…Obtain high-level semantic information that can reflect the image category, thereby avoiding the tedious preprocessing and complex feature extraction of the original image. Yildiz [29] used deep learning methods to learn the texture features of video images and classify them. Rajagopal et al [30] used a convolutional neural network training method to improve the accuracy of image recognition.…”

Section: Related Workmentioning

confidence: 99%

Virtual Reality Video Image Classification Based on Texture Features

Qin

2021

Complexity

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As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. However, the convolutional neural network method still has disadvantages such as complex network model, too long training time and excessive consumption of computing resources, slow convergence speed, network overfitting, and classification accuracy that needs to be improved. Therefore, this article proposes a dense convolutional neural network classification algorithm based on texture features for images in virtual reality videos. First, the texture feature of the image is introduced as a priori information to reflect the spatial relationship between pixels and the unique characteristics of different types of ground features. Second, the grey level cooccurrence matrix (GLCM) is used to extract the grey level correlation features of the image in space. Then, Gauss Markov Random Field (GMRF) is used to establish the statistical correlation characteristics between neighbouring pixels, and the extracted GLCM-GMRF texture feature and image intensity vector are combined. Finally, based on DenseNet, an improved shallow layer dense convolutional neural network (L-DenseNet) is proposed, which can compress network parameters and improve the feature extraction ability of the network. The experimental results show that compared with the current classification method, this method can effectively suppress the influence of coherent speckle noise and obtain better classification results.

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Section: Related Workmentioning

confidence: 99%

Virtual Reality Video Image Classification Based on Texture Features

Qin

2021

Complexity

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“…A novel local binary pattern-based feature extraction method was proposed by Yildiz et al for fine fibers detection, which achieved the objective, easy, rapid, time, and cost-effective results [19]. Liu et al proposed a structured multi-feature extraction method for spatiotemporal activity recognition, which is an inspiration for defect detection [20].…”

Section: A Feature Extractionmentioning

confidence: 99%

“…Motivated by the recent contribution evaluation methods of surface defect detection, in this paper we used the following evaluation metrics, as shown in (20): 1) False positive rate (FPR), namely, the proportion of pixels falsely detected as defects; 2) False negative rate (FNR), namely, the proportion of pixels falsely detected as non-defects; 3) Mean absolute error (MAE), namely, the difference between the detection result (DR) and ground truth (GT…”

Section: Evaluation Metricsmentioning

confidence: 99%

Automatic and Efficient Metallic Surface Defect Detection Based on Key Pixel Point Locations

Gao

Sun

et al. 2021

IEEE Sensors J.

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Surface defect detection aims to accurately recognize and distinguish types of defects and plays a key role in many applications. However, most of the recent studies focus on specific scenario detection and do not fairly consider the balance between the speed and accuracy. In the paper, we propose a key pixel points location-oriented method to identify multiscale defects, with several important properties: 1) A real-time template matching-based model is designed to speed up the process by introducing the Gaussian operator; 2) An improved Hough-based model is used to achieve a higher detection precision by deep mining both incremental properties and parallel properties; 3) An adaptive filtering-based image preprocessing method is proposed to eliminate the interference of multiple types of clutters and noises. In the experiments, a mean average rate of 96% was achieved to detect and classify four types of common defects and the average time was reduced to 0.149s. Furthermore, we fully evaluate the proposed method on two public datasets collected in real production lines and compare the results with other state-of-the-art methods. The results show that the proposed method achieved better balanced performance in many real application scenarios.

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“…In recent years, methods based on deep learning have made remarkable achievements in image classification. 23,24 Deep learning uses a learning network of perceptrons with multiple hidden layers. It can automatically learn feedback and optimize the appropriate image features, and then combine and abstract from the low-level features to build a high-level representation.…”

Section: Related Workmentioning

confidence: 99%

Element extraction and convolutional neural network-based classification for blue calico

Jia

Liu

2020

Textile Research Journal

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Blue calico is a highly valued folk handicraft that forms part of China’s national intangible cultural heritage. Thus, blue calico is a worthy target for reconstruction using modern image processing technology. Extracting the visual components or elements of a blue calico pattern is one way to capture the underlying design and enable innovation in traditional patterns using modern techniques. This paper presents a method of element extraction and classification based on a smart convolutional neural network (CNN), with an improved CifarNet structure, which we call CalicoNet. Initially, the algorithm for element extraction is implemented to generate element samples of blue calico. This process includes gray scaling, binarization, and contour extraction. We construct a data set of elements with 12 types. Then, four critical hyper-parameters, the batch-size, dropout ratio, learning rate, and pooling strategy, are optimized by a comparative analysis. A combination classifier strategy is subsequently added to the fully connected layers to strengthen the feature expression in the corresponding classes. Finally, the superiority of the proposed CalicoNet is verified through a comparison with other sophisticated CNNs. Experimental results demonstrate that CalicoNet achieves a validation accuracy of 99.2% for the training set, a total time of 1.13 hours for the whole data set, and a test mean accuracy precision of 98.66%. The robust performance of the proposed method across the element data set indicates that CalicoNet is a promising approach for element extraction and classification.

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Identification of wool and mohair fibres with texture feature extraction and deep learning

Cited by 25 publications

References 37 publications

Virtual Reality Video Image Classification Based on Texture Features

Virtual Reality Video Image Classification Based on Texture Features

Automatic and Efficient Metallic Surface Defect Detection Based on Key Pixel Point Locations

Element extraction and convolutional neural network-based classification for blue calico

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