Ahmad AL Smadi, Shuyuan Yang, Atif Mehmood, A. Abugabah, Min Wang, M. Bashir
Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. Image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high-frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust spatial data misfortune that happens when images are combined. Experimental results demonstrated that the proposed method provides consistent results concise with reported by the previ-ous research in terms of subjective and objective assessments on remote sensing data.
{"title":"Smart pansharpening approach using kernel-based image filtering","authors":"Ahmad AL Smadi, Shuyuan Yang, Atif Mehmood, A. Abugabah, Min Wang, M. Bashir","doi":"10.1049/IPR2.12251","DOIUrl":"https://doi.org/10.1049/IPR2.12251","url":null,"abstract":"Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. Image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high-frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust spatial data misfortune that happens when images are combined. Experimental results demonstrated that the proposed method provides consistent results concise with reported by the previ-ous research in terms of subjective and objective assessments on remote sensing data.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87726508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unsupervised clustering is a kind of popular solution for unsupervised person re-identification (re-ID). However, due to the influence of cross-view differences, the results of clustering labels are not accurate. To solve this problem, an unsupervised re ID method based on cross-view distributed alignment (CV-DA) to reduce the influence of unsupervised cross-view is proposed. Specifically, based on a popular unsupervised clustering method, density clustering DBSCAN is used to obtain pseudo labels. By calculating the similarity scores of images in the target domain and the source domain, the similarity distribution of different camera views is obtained and is aligned with the distribution with the consistency constraint of pseudo labels. The cross-view distribution alignment constraint is used to guide the clustering process to obtain a more reliable pseudo label. The comprehensive comparative experiments are done in two public datasets, i.e. Market-1501 and DukeMTMC-reID. The comparative results show that the proposed method outper-forms several state-of-the-art approaches with mAP reaching 52.6% and rank1 71.1%. In order to prove the effectiveness of the proposed CV-DA, the proposed constraint is added into two advanced re-ID methods. The experimental results demonstrate that the mAP and rank increase by ∽ 0.5–2% after using the cross-view distribution alignment constraint comparing with that of the associated original methods without using CV-DA.
{"title":"An unsupervised person re-identification approach based on cross-view distribution alignment","authors":"Xibin Jia, Xing Wang, Qinggai Mi","doi":"10.1049/IPR2.12256","DOIUrl":"https://doi.org/10.1049/IPR2.12256","url":null,"abstract":"Unsupervised clustering is a kind of popular solution for unsupervised person re-identification (re-ID). However, due to the influence of cross-view differences, the results of clustering labels are not accurate. To solve this problem, an unsupervised re ID method based on cross-view distributed alignment (CV-DA) to reduce the influence of unsupervised cross-view is proposed. Specifically, based on a popular unsupervised clustering method, density clustering DBSCAN is used to obtain pseudo labels. By calculating the similarity scores of images in the target domain and the source domain, the similarity distribution of different camera views is obtained and is aligned with the distribution with the consistency constraint of pseudo labels. The cross-view distribution alignment constraint is used to guide the clustering process to obtain a more reliable pseudo label. The comprehensive comparative experiments are done in two public datasets, i.e. Market-1501 and DukeMTMC-reID. The comparative results show that the proposed method outper-forms several state-of-the-art approaches with mAP reaching 52.6% and rank1 71.1%. In order to prove the effectiveness of the proposed CV-DA, the proposed constraint is added into two advanced re-ID methods. The experimental results demonstrate that the mAP and rank increase by ∽ 0.5–2% after using the cross-view distribution alignment constraint comparing with that of the associated original methods without using CV-DA.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83129320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaogang Du, Dongxin Gu, Tao Lei, Song Wang, Xuejun Zhang, H. Meng
National Natural Science Foundation of China. Grant Numbers: 61762058, 61861024, 61871259; Natural Science Foundation of Gansu Province of China. Grant Number: 20JR5RA404; Natural Science Basic Research Program of Shaanxi. Grant Number: 2021JC-47.
{"title":"HNSF Log-Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features","authors":"Xiaogang Du, Dongxin Gu, Tao Lei, Song Wang, Xuejun Zhang, H. Meng","doi":"10.1049/IPR2.12254","DOIUrl":"https://doi.org/10.1049/IPR2.12254","url":null,"abstract":"National Natural Science Foundation of China. Grant Numbers: 61762058, 61861024, 61871259; \u0000Natural Science Foundation of Gansu Province of China. Grant Number: 20JR5RA404; \u0000Natural Science Basic Research Program of Shaanxi. Grant Number: 2021JC-47.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81650306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenjun Tang, M. Pang, Chunqiang Yu, Guijin Fan, Xianquan Zhang
Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block-based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS-2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in embedding capacity.
{"title":"Reversible data hiding for encrypted image based on adaptive prediction error coding","authors":"Zhenjun Tang, M. Pang, Chunqiang Yu, Guijin Fan, Xianquan Zhang","doi":"10.1049/IPR2.12252","DOIUrl":"https://doi.org/10.1049/IPR2.12252","url":null,"abstract":"Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block-based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS-2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in embedding capacity.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88688516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The volume of lower extremity muscles is affected by some diseases. Quantification of thigh muscles in medical images can lead to an easier investigation of these diseases. Most of the previous works in thigh muscle segmentation are based on models and atlases that require manually segmented datasets in 3D. As manual segmentation of these muscles is a time-consuming task, in this work, only one initial slice is segmented by a new hybrid FRFCM-based multi-atlas method and other slices are segmented based on this slice. In the proposed method, after noise reduction, the muscle region is extracted from other tissues by the FRFCM method. Then, an initial slice of each dataset is segmented by a multi-atlas method. The segmented muscles in the initial slice are used to segment muscles in the other slices of each dataset. The proposed method was evaluated with 20 CT datasets. The average DSC, Precision, and Sensitivity of the method for individual muscle segmentation were 91 . 20 ± 2 . 37, 91 . 95 ± 3 . 54, and 90 . 71 ± 3 . 89, respectively. The quantitative and intuitive results of the proposed method show the better results of this method in comparison to other state-of-the-art thigh muscle segmentation techniques.
{"title":"Thigh muscle segmentation using a hybrid FRFCM-based multi-atlas method and morphology-based interpolation algorithm","authors":"Malihe Molaie, R. Zoroofi","doi":"10.1049/IPR2.12245","DOIUrl":"https://doi.org/10.1049/IPR2.12245","url":null,"abstract":"The volume of lower extremity muscles is affected by some diseases. Quantification of thigh muscles in medical images can lead to an easier investigation of these diseases. Most of the previous works in thigh muscle segmentation are based on models and atlases that require manually segmented datasets in 3D. As manual segmentation of these muscles is a time-consuming task, in this work, only one initial slice is segmented by a new hybrid FRFCM-based multi-atlas method and other slices are segmented based on this slice. In the proposed method, after noise reduction, the muscle region is extracted from other tissues by the FRFCM method. Then, an initial slice of each dataset is segmented by a multi-atlas method. The segmented muscles in the initial slice are used to segment muscles in the other slices of each dataset. The proposed method was evaluated with 20 CT datasets. The average DSC, Precision, and Sensitivity of the method for individual muscle segmentation were 91 . 20 ± 2 . 37, 91 . 95 ± 3 . 54, and 90 . 71 ± 3 . 89, respectively. The quantitative and intuitive results of the proposed method show the better results of this method in comparison to other state-of-the-art thigh muscle segmentation techniques.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82608376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoguang Li, Ning Dong, Jianglu Huang, L. Zhuo, Jiafeng Li
Face image captured via surveillance videos in an open environment is usually of low quality, which seriously affects the visual quality and recognition accuracy. Most image super-resolution methods adopt paired high-quality and its interpolated low-resolution version to train the super-resolution network. It is difficult to achieve contented visual quality and restoring discriminative features in real scenarios. A discriminative self-attention cycle generative adversarial network is proposed for real-world face image super-resolution. Based on the cycle GAN framework, unpaired samples are adopted to train a degradation network and a reconstruction network simultaneously. A self-attention mechanism is employed to capture the contextual information for details restoring. A Siamese face recognition network is introduced to provide a constraint on identify consistency. In addition, an asymmetric perceptual loss is introduced to handle the imbalance between the degradation model and the reconstruction model. Experimental results show that the observation model achieved more realistic low-quality face images, and the super-resolved face images have shown better subjective quality and higher face recognition performance.
{"title":"A discriminative self-attention cycle GAN for face super-resolution and recognition","authors":"Xiaoguang Li, Ning Dong, Jianglu Huang, L. Zhuo, Jiafeng Li","doi":"10.1049/IPR2.12250","DOIUrl":"https://doi.org/10.1049/IPR2.12250","url":null,"abstract":"Face image captured via surveillance videos in an open environment is usually of low quality, which seriously affects the visual quality and recognition accuracy. Most image super-resolution methods adopt paired high-quality and its interpolated low-resolution version to train the super-resolution network. It is difficult to achieve contented visual quality and restoring discriminative features in real scenarios. A discriminative self-attention cycle generative adversarial network is proposed for real-world face image super-resolution. Based on the cycle GAN framework, unpaired samples are adopted to train a degradation network and a reconstruction network simultaneously. A self-attention mechanism is employed to capture the contextual information for details restoring. A Siamese face recognition network is introduced to provide a constraint on identify consistency. In addition, an asymmetric perceptual loss is introduced to handle the imbalance between the degradation model and the reconstruction model. Experimental results show that the observation model achieved more realistic low-quality face images, and the super-resolved face images have shown better subjective quality and higher face recognition performance.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84700063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end-to-end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U-Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1-score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1-score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state-of-the-art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.
{"title":"Blood vessel and background separation for retinal image quality assessment","authors":"Yipeng Liu, Yajun Lv, Zhanqing Li, Jing Li, Yan Liu, Peng Chen, Ronghua Liang","doi":"10.1049/IPR2.12244","DOIUrl":"https://doi.org/10.1049/IPR2.12244","url":null,"abstract":"Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end-to-end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U-Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1-score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1-score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state-of-the-art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82094382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many methods of hiding information in an image are existing now. The least significant bit is the famous method used in steganographic algorithms. Medical image steganography is a technique used to make the transmission of these images secure so that the decision of the Specialist physician based on these images is not affected. In this paper, medical image steganographic algorithm using modified least significant bit and chaotic map is proposed. The main problem is that the selection of embedding pixels within the host image is not protected enough in most existing methods. So, the author used two-dimensional piecewise smooth chaotic map to select the positions of these pixels randomly. On the other hand, all bits in the secret medical image are transmitted without losing any bit. To do that, the secret medical image is encrypted using one-dimensional piecewise chaotic map (Tent map). Then, the steganographic algorithm is used to hide the bits of the encrypted secret medical image. The bits of each embedded pixel are shuffled before the embedding pro-cess randomly. After that, the stego image is created. The host image and stego image are analysed with the peak signal-to-noise ratio, the mean square error, histogram test, image quality measure and relative entropy test. The stego image displays acceptable result when comparing with the host image. Also, the chi-square attack test is performed and the stego image can resist it. The proposed algorithm can assist the sending of medical images via communication media.
{"title":"Medical image steganographic algorithm via modified LSB method and chaotic map","authors":"A. Karawia","doi":"10.1049/IPR2.12246","DOIUrl":"https://doi.org/10.1049/IPR2.12246","url":null,"abstract":"Many methods of hiding information in an image are existing now. The least significant bit is the famous method used in steganographic algorithms. Medical image steganography is a technique used to make the transmission of these images secure so that the decision of the Specialist physician based on these images is not affected. In this paper, medical image steganographic algorithm using modified least significant bit and chaotic map is proposed. The main problem is that the selection of embedding pixels within the host image is not protected enough in most existing methods. So, the author used two-dimensional piecewise smooth chaotic map to select the positions of these pixels randomly. On the other hand, all bits in the secret medical image are transmitted without losing any bit. To do that, the secret medical image is encrypted using one-dimensional piecewise chaotic map (Tent map). Then, the steganographic algorithm is used to hide the bits of the encrypted secret medical image. The bits of each embedded pixel are shuffled before the embedding pro-cess randomly. After that, the stego image is created. The host image and stego image are analysed with the peak signal-to-noise ratio, the mean square error, histogram test, image quality measure and relative entropy test. The stego image displays acceptable result when comparing with the host image. Also, the chi-square attack test is performed and the stego image can resist it. The proposed algorithm can assist the sending of medical images via communication media.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81008802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Underwater image processing has always been a very challenging problem. Under the influence of environmental factors, underwater images are prone to some problems, such as colour cast, low visibility, and few edge details. Here, an image enhancement algorithm is proposed to improve image degradation mainly caused by the absorption of light. First, colour compensation and white balance algorithm are used to restore the natural appearance of the image. Then the improved dark channel prior (DCP) is used to improve the visibility and avoid blocking artifacts which appear in traditional DCP. Unsharp masking (USM) is applied to enhance the texture features of the DCP image. Finally, wavelet fusion is used to fuse the DCP image and DCP + USM image. The fusion algorithm not only further improves the visibility and texture features, but also reduces the noise of DCP + USM. Compared with other methods, quantitative analysis results show that the enhanced images have higher visibility, more details and edge information.
{"title":"Underwater image enhancement based on colour correction and fusion","authors":"Daqi Zhu, Zhiqiang Liu, Youmin Zhang","doi":"10.1049/IPR2.12247","DOIUrl":"https://doi.org/10.1049/IPR2.12247","url":null,"abstract":"Underwater image processing has always been a very challenging problem. Under the influence of environmental factors, underwater images are prone to some problems, such as colour cast, low visibility, and few edge details. Here, an image enhancement algorithm is proposed to improve image degradation mainly caused by the absorption of light. First, colour compensation and white balance algorithm are used to restore the natural appearance of the image. Then the improved dark channel prior (DCP) is used to improve the visibility and avoid blocking artifacts which appear in traditional DCP. Unsharp masking (USM) is applied to enhance the texture features of the DCP image. Finally, wavelet fusion is used to fuse the DCP image and DCP + USM image. The fusion algorithm not only further improves the visibility and texture features, but also reduces the noise of DCP + USM. Compared with other methods, quantitative analysis results show that the enhanced images have higher visibility, more details and edge information.","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91549089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Road Tunnels are an important part of the current road transportation infrastructure. As the main form of tunnel lining diseases, cracks are easy to interact with other areas, which seriously affects the safe operation of the tunnel. Due to the similarity of brightness and linearity between surface cracks and lining cracks, the existing crack detection algorithms can not extract cracks accurately and quickly. An algorithm of lining seam crack elimination with rectangular mark is proposed here. First, the line segments in the image are detected by the Line Segment Detector algorithm based on the coarse percolation detection of the crack. Second, the distribution directions are calculated, and cracks from the lining seams are distinguished by the adaptive threshold judgment method. Third, by using the distribution characteristics of pixels, the line segments are extended to form rectangular marks perpendicular to the direction of lining seams. Finally, the marking information is used to remove the lining joints and obtain the real surface cracks of tunnel lining. Experimental results show that the algorithm can quickly and effectively remove any shape distribution of lining seam. The algorithm fills in the of concrete tunnel lining surface crack
{"title":"A method of lining seam elimination with angle adaptation and rectangular mark for road tunnel concrete lining images","authors":"Zhong-si Qu, Y. Zhong, Ling Liu","doi":"10.1049/IPR2.12177","DOIUrl":"https://doi.org/10.1049/IPR2.12177","url":null,"abstract":"Road Tunnels are an important part of the current road transportation infrastructure. As the main form of tunnel lining diseases, cracks are easy to interact with other areas, which seriously affects the safe operation of the tunnel. Due to the similarity of brightness and linearity between surface cracks and lining cracks, the existing crack detection algorithms can not extract cracks accurately and quickly. An algorithm of lining seam crack elimination with rectangular mark is proposed here. First, the line segments in the image are detected by the Line Segment Detector algorithm based on the coarse percolation detection of the crack. Second, the distribution directions are calculated, and cracks from the lining seams are distinguished by the adaptive threshold judgment method. Third, by using the distribution characteristics of pixels, the line segments are extended to form rectangular marks perpendicular to the direction of lining seams. Finally, the marking information is used to remove the lining joints and obtain the real surface cracks of tunnel lining. Experimental results show that the algorithm can quickly and effectively remove any shape distribution of lining seam. The algorithm fills in the of concrete tunnel lining surface crack","PeriodicalId":13486,"journal":{"name":"IET Image Process.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72587899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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