Color Inverse Halftoning Method with the Correlation of Multi-Color Components Based on Extreme Learning Machine

Zhang, Erhu; Zhang, Yan; Duan, Jinhui

doi:10.3390/app9050841

Cited by 17 publications

(16 citation statements)

References 23 publications

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“…Plain and twill fabric detection methods can be classified into five aspects: Spectral [9,10], learning [11,12], statistical [13][14][15], model-based [16,17], and structural methods [18,19]. The spectral method based on the Wavelet transform [20] achieved 97.5% detection accuracy with five known defect types and a 93.3% detection accuracy (a slight drop) with three unknown defect types in an evaluation.…”

Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Zhang

et al. 2019

Applied Sciences

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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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Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Zhang

et al. 2019

Applied Sciences

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“…The three features consist of multi-scale completed local binary patterns (MS-CLBP), Bag of visual words (BOVW), and spatial pyramid matching (SPM). Methods based on CNN mainly refer to select the features of a certain layer in the convolutional neural network, and then use the support vector machine (SVM), extreme learning machine (ELM) or logistic regression classifier [12][13][14][15][16][17][18]. The literature [5] has used the 15th layer feature extracted from the pre-trained model vgg-16 based on the ImageNet dataset.…”

Section: Introductionmentioning

confidence: 99%

Classification of Marine Vessels with Multi-Feature Structure Fusion

2019

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The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result.

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“…Surface defect detection and classification are important tasks in many fields, such as the manufacturing industry [1,2], textile industry [3], and printing industry [4,5]. For the printing industry, with the increasing speed of modern printing machines and the increasing requirements of printing enterprises in terms of product quality, traditional detection and classification methods based on human visual inspection are impossible.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

Zhang

et al. 2019

Symmetry

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Deep learning has been successfully applied to classification tasks in many fields due to its good performance in learning discriminative features. However, the application of deep learning to printing defect classification is very rare, and there is almost no research on the classification method for printing defects with imbalanced samples. In this paper, we present a deep convolutional neural network model to extract deep features directly from printed image defects. Furthermore, considering the asymmetry in the number of different types of defect samples-that is, the number of different kinds of defect samples is unbalanced-seven types of over-sampling methods were investigated to determine the best method. To verify the practical applications of the proposed deep model and the effectiveness of the extracted features, a large dataset of printing detect samples was built. All samples were collected from practical printing products in the factory. The dataset includes a coarse-grained dataset with four types of printing samples and a fine-grained dataset with eleven types of printing samples. The experimental results show that the proposed deep model achieves a 96.86% classification accuracy rate on the coarse-grained dataset without adopting over-sampling, which is the highest accuracy compared to the well-known deep models based on transfer learning. Moreover, by adopting the proposed deep model combined with the SVM-SMOTE over-sampling method, the accuracy rate is improved by more than 20% in the fine-grained dataset compared to the method without over-sampling.

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Color Inverse Halftoning Method with the Correlation of Multi-Color Components Based on Extreme Learning Machine

Cited by 17 publications

References 23 publications

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

Classification of Marine Vessels with Multi-Feature Structure Fusion

A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

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