Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190962
Yan Zhu, Yi Niu, Fu Li, Chunbo Zou, Guangming Shi
The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps. Since the finite features harvested from one single aesthetic style image are inadequate to represent the rich textures of the content natural image, existing techniques treat the full-channel style feature patches as simple signal tensors and create new style feature patches via signal-level fusion. In this paper, we propose a channel-grouping based patch swap technique to group the style feature maps into surface and texture channels, and the new features are created by the combination of these two groups, which can be regarded as a semantic-level fusion of the raw style features. Experimental results demonstrate that the proposed method outperforms the existing techniques in providing more style-consistent textures while keeping the content fidelity.
{"title":"Channel-Grouping Based Patch Swap For Arbitrary Style Transfer","authors":"Yan Zhu, Yi Niu, Fu Li, Chunbo Zou, Guangming Shi","doi":"10.1109/ICIP40778.2020.9190962","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190962","url":null,"abstract":"The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps. Since the finite features harvested from one single aesthetic style image are inadequate to represent the rich textures of the content natural image, existing techniques treat the full-channel style feature patches as simple signal tensors and create new style feature patches via signal-level fusion. In this paper, we propose a channel-grouping based patch swap technique to group the style feature maps into surface and texture channels, and the new features are created by the combination of these two groups, which can be regarded as a semantic-level fusion of the raw style features. Experimental results demonstrate that the proposed method outperforms the existing techniques in providing more style-consistent textures while keeping the content fidelity.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123860490","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190953
G. Franchi, Emanuel Aldea, Séverine Dubuisson, I. Bloch
Tracking an entire high-density crowd composed of more than five hundred individuals is a difficult task that has not yet been accomplished. In this article, we propose to track pedestrians using a model composed of a Particle Filter (PF) and three Deep Convolutional Neural Networks (DCNN). The first network is a detector that learns to localize the persons. The second one is a pretrained network that estimates the optical flow, and the last one corrects the flow. Our contribution resides in the way we train this last network by PF supervision, and in Markov Random Field linking the different tracks.
{"title":"Tracking Hundreds of People in Densely Crowded Scenes With Particle Filtering Supervising Deep Convolutional Neural Networks","authors":"G. Franchi, Emanuel Aldea, Séverine Dubuisson, I. Bloch","doi":"10.1109/ICIP40778.2020.9190953","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190953","url":null,"abstract":"Tracking an entire high-density crowd composed of more than five hundred individuals is a difficult task that has not yet been accomplished. In this article, we propose to track pedestrians using a model composed of a Particle Filter (PF) and three Deep Convolutional Neural Networks (DCNN). The first network is a detector that learns to localize the persons. The second one is a pretrained network that estimates the optical flow, and the last one corrects the flow. Our contribution resides in the way we train this last network by PF supervision, and in Markov Random Field linking the different tracks.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123972319","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191310
N. Mulliqi, Sule YAYILGAN YILDIRIM, A. Mohammed, L. Ahmedi, Hao Wang, Ogerta Elezaj, Ø. Hovde
Accurate polyp detection during the colonoscopy procedure impacts colorectal cancer prevention and early detection. In this paper, we investigate the influence of skip connections as the main component of encoder-decoder based convolutional neural network (CNN) architectures for colorectal polyp detection. We conduct experiments on long and short skip connections and further extend the existing architecture by introducing dense lateral skip connections. The proposed segmentation architecture utilizes short skip connections in the contracting path, moreover it utilizes dense long and lateral skip connections in between the contracting and expanding path. Results obtained from the MICCAI 2015 Challenge dataset show progressive improvement of the segmentation result with expanded utilization of skip connections. The proposed colorectal polyp segmentation architecture achieves performance comparable to the state-of-the-art under significantly reduced number of model parameters.
{"title":"The Importance Of Skip Connections In Encoder-Decoder Architectures For Colorectal Polyp Detection","authors":"N. Mulliqi, Sule YAYILGAN YILDIRIM, A. Mohammed, L. Ahmedi, Hao Wang, Ogerta Elezaj, Ø. Hovde","doi":"10.1109/ICIP40778.2020.9191310","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191310","url":null,"abstract":"Accurate polyp detection during the colonoscopy procedure impacts colorectal cancer prevention and early detection. In this paper, we investigate the influence of skip connections as the main component of encoder-decoder based convolutional neural network (CNN) architectures for colorectal polyp detection. We conduct experiments on long and short skip connections and further extend the existing architecture by introducing dense lateral skip connections. The proposed segmentation architecture utilizes short skip connections in the contracting path, moreover it utilizes dense long and lateral skip connections in between the contracting and expanding path. Results obtained from the MICCAI 2015 Challenge dataset show progressive improvement of the segmentation result with expanded utilization of skip connections. The proposed colorectal polyp segmentation architecture achieves performance comparable to the state-of-the-art under significantly reduced number of model parameters.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121190251","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190970
Mário Saldanha, G. Sanchez, C. Marcon, L. Agostini
Versatile Video Coding (VVC) is the next-generation of video coding standards, which was developed to double the coding efficiency over its predecessor High-Efficiency Video Coding (HEVC). Several new coding tools have been investigated and adopted in the VVC Test Model (VTM), whose current version can improve the intra coding efficiency by 24% at the cost of a much higher coding complexity than the HEVC Test Model (HM). Thus, this paper provides a detailed VVC intra coding complexity analysis, which can support upcoming works for finding the most timeconsuming tool that could be simplified to achieve a real-time encoder design.
{"title":"Complexity Analysis Of VVC Intra Coding","authors":"Mário Saldanha, G. Sanchez, C. Marcon, L. Agostini","doi":"10.1109/ICIP40778.2020.9190970","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190970","url":null,"abstract":"Versatile Video Coding (VVC) is the next-generation of video coding standards, which was developed to double the coding efficiency over its predecessor High-Efficiency Video Coding (HEVC). Several new coding tools have been investigated and adopted in the VVC Test Model (VTM), whose current version can improve the intra coding efficiency by 24% at the cost of a much higher coding complexity than the HEVC Test Model (HM). Thus, this paper provides a detailed VVC intra coding complexity analysis, which can support upcoming works for finding the most timeconsuming tool that could be simplified to achieve a real-time encoder design.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121353387","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191149
Sara Akodad, L. Bombrun, Y. Berthoumieu, C. Germain
The launch of the last generation of Earth observation satellites has yield to a great improvement in the capabilities of acquiring Earth surface images, providing series of multitemporal images. To process these time series images, many machine learning algorithms have been proposed in the literature such as warping based methods and recurrent neural networks (LSTM,…). Recently, based on an ensemble learning approach, the time series cluster kernel (TCK) has been proposed and has shown competitive results compared to the state-of-the-art. Unfortunately, it does not model the spectral/spatial dependencies. To overcome this problem, this paper aims at extending the TCK approach by modeling the time series of second-order statistical features (SO-TCK). Experimental results are conducted on different benchmark datasets, and land cover classification with remote sensing satellite time series over the Reunion Island.
{"title":"Cluster Kernel For Learning Similarities Between Symmetric Positive Definite Matrix Time Series","authors":"Sara Akodad, L. Bombrun, Y. Berthoumieu, C. Germain","doi":"10.1109/ICIP40778.2020.9191149","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191149","url":null,"abstract":"The launch of the last generation of Earth observation satellites has yield to a great improvement in the capabilities of acquiring Earth surface images, providing series of multitemporal images. To process these time series images, many machine learning algorithms have been proposed in the literature such as warping based methods and recurrent neural networks (LSTM,…). Recently, based on an ensemble learning approach, the time series cluster kernel (TCK) has been proposed and has shown competitive results compared to the state-of-the-art. Unfortunately, it does not model the spectral/spatial dependencies. To overcome this problem, this paper aims at extending the TCK approach by modeling the time series of second-order statistical features (SO-TCK). Experimental results are conducted on different benchmark datasets, and land cover classification with remote sensing satellite time series over the Reunion Island.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116282275","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190811
S. Roy, T. Bouwmans
In this article, a novel pixel based object detection framework is proposed that leverages dual type pixel-level information to construct the background model. The first type of information is initially used intensity histograms over a training set of a few initial video frames. Finally, it is formed by gathering all the minimum and maximum values of contiguous non-zero frequencies of the temporal intensity histogram. The second type of information constitutes a set having only the discrete pixel values. Subsequently, a pixel-level periodic updating scheme is used to make the model robust and flexible enough to recognize and detect foregrounds in various critical background environments. This dual format model produces effective results over many state-of-the-art methods in a large variety of challenging real-life video sequences.
{"title":"Dual Information-Based Background Model For Moving Object Detection","authors":"S. Roy, T. Bouwmans","doi":"10.1109/ICIP40778.2020.9190811","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190811","url":null,"abstract":"In this article, a novel pixel based object detection framework is proposed that leverages dual type pixel-level information to construct the background model. The first type of information is initially used intensity histograms over a training set of a few initial video frames. Finally, it is formed by gathering all the minimum and maximum values of contiguous non-zero frequencies of the temporal intensity histogram. The second type of information constitutes a set having only the discrete pixel values. Subsequently, a pixel-level periodic updating scheme is used to make the model robust and flexible enough to recognize and detect foregrounds in various critical background environments. This dual format model produces effective results over many state-of-the-art methods in a large variety of challenging real-life video sequences.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116341043","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191249
Satvik Chemudupati, P. Pokala, C. Seelamantula
We present a new sparsity based technique for interferometric phase estimation. We consider complex extensions of non-convex regularizers such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation penalty (SCAD) for sparse recovery. We solve the problem of interferometric phase estimation based on complex-domain dictionary learning. We develop an algorithm, namely, improved sparse interferometric phase estimation (iSpInPhase) based on alternating direction method of multipliers (ADMM) and Wirtinger calculus for solving the optimization problem. Wiritinger calculus is employed because the cost functions are nonholomorphic. We evaluate the performance of iSpInPhase on synthetic data, namely, truncated Gaussian elevation and also on mountain terrain data, namely, Long’s peak, for different noise levels. Performance comparisons show that iSpInPhase outperforms the state-of-the-art techniques in terms of standard performance assessment measures.
{"title":"Non-Convex Optimization For Sparse Interferometric Phase Estimation","authors":"Satvik Chemudupati, P. Pokala, C. Seelamantula","doi":"10.1109/ICIP40778.2020.9191249","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191249","url":null,"abstract":"We present a new sparsity based technique for interferometric phase estimation. We consider complex extensions of non-convex regularizers such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation penalty (SCAD) for sparse recovery. We solve the problem of interferometric phase estimation based on complex-domain dictionary learning. We develop an algorithm, namely, improved sparse interferometric phase estimation (iSpInPhase) based on alternating direction method of multipliers (ADMM) and Wirtinger calculus for solving the optimization problem. Wiritinger calculus is employed because the cost functions are nonholomorphic. We evaluate the performance of iSpInPhase on synthetic data, namely, truncated Gaussian elevation and also on mountain terrain data, namely, Long’s peak, for different noise levels. Performance comparisons show that iSpInPhase outperforms the state-of-the-art techniques in terms of standard performance assessment measures.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121439753","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9191103
Santiago De-Luxán-Hernández, Gayathri Venugopal, Valeri George, H. Schwarz, D. Marpe, T. Wiegand
Lossy compression is the main target of the upcoming video coding standard Versatile Video Coding (VVC). However, lossless coding is supported in VVC by utilizing a certain encoder configuration. Particularly, the Transform Skip Mode (TSM) is always selected at the block level to bypass the transform stage (together with a QP that results in the same output as input at the quantization stage). Consequently, the Intra Subpartition (ISP) coding mode cannot be used for lossless coding, considering that its combination with TSM is not supported in VVC because it does not provide a significant coding benefit for the lossy common test conditions. For this reason, it is proposed to enable such a combination for the benefit of lossless coding. Besides, the encoder search has been optimized to improve the trade-off between compression benefit and encoder run-time. Experimental results show a 0.71% coding gain with a corresponding encoder run-time of 111%.
{"title":"A Fast Lossless Implementation Of The Intra Subpartition Mode For VVC","authors":"Santiago De-Luxán-Hernández, Gayathri Venugopal, Valeri George, H. Schwarz, D. Marpe, T. Wiegand","doi":"10.1109/ICIP40778.2020.9191103","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9191103","url":null,"abstract":"Lossy compression is the main target of the upcoming video coding standard Versatile Video Coding (VVC). However, lossless coding is supported in VVC by utilizing a certain encoder configuration. Particularly, the Transform Skip Mode (TSM) is always selected at the block level to bypass the transform stage (together with a QP that results in the same output as input at the quantization stage). Consequently, the Intra Subpartition (ISP) coding mode cannot be used for lossless coding, considering that its combination with TSM is not supported in VVC because it does not provide a significant coding benefit for the lossy common test conditions. For this reason, it is proposed to enable such a combination for the benefit of lossless coding. Besides, the encoder search has been optimized to improve the trade-off between compression benefit and encoder run-time. Experimental results show a 0.71% coding gain with a corresponding encoder run-time of 111%.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124300419","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190989
Vassilios Vonikakis, Stefan Winkler
We propose a frontalization technique for 2D facial landmarks, designed to aid in the analysis of facial expressions. It employs a new normalization strategy aiming to minimize identity variations, by displacing groups of facial landmarks to standardized locations. The technique operates directly on 2D landmark coordinates, does not require additional feature extraction and as such is computationally light. It achieves considerable improvement over a reference approach, justifying its use as an efficient preprocessing step for facial expression analysis based on geometric features.
{"title":"Identity-Invariant Facial Landmark Frontalization For Facial Expression Analysis","authors":"Vassilios Vonikakis, Stefan Winkler","doi":"10.1109/ICIP40778.2020.9190989","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190989","url":null,"abstract":"We propose a frontalization technique for 2D facial landmarks, designed to aid in the analysis of facial expressions. It employs a new normalization strategy aiming to minimize identity variations, by displacing groups of facial landmarks to standardized locations. The technique operates directly on 2D landmark coordinates, does not require additional feature extraction and as such is computationally light. It achieves considerable improvement over a reference approach, justifying its use as an efficient preprocessing step for facial expression analysis based on geometric features.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123992278","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}
Pub Date : 2020-10-01DOI: 10.1109/ICIP40778.2020.9190976
Jun Ma, Shilin Wang, Aixin Zhang, Alan Wee-Chung Liew
Recent research shows that the lip feature can achieve reliable authentication performance with a good liveness detection ability. However, with the development of sophisticated human face generation methods by the deepfake technology, the talking videos can be forged with high quality and the static lip information is not reliable in such case. Meeting with such challenge, in this paper, we propose a new deep neural network structure to extract robust lip features against human and Computer-Generated (CG) imposters. Two novel network units, i.e. the feature-level Difference block (Diffblock) and the pixel-level Dynamic Response block (DRblock), are proposed to reduce the influence of the static lip information and to represent the dynamic talking habit information. Experiments on the GRID dataset have demonstrated that the proposed network can extract discriminative and robust lip features and outperform two state-of-the-art visual speaker authentication approaches in both human imposter and CG imposter scenarios.
{"title":"Feature Extraction For Visual Speaker Authentication Against Computer-Generated Video Attacks","authors":"Jun Ma, Shilin Wang, Aixin Zhang, Alan Wee-Chung Liew","doi":"10.1109/ICIP40778.2020.9190976","DOIUrl":"https://doi.org/10.1109/ICIP40778.2020.9190976","url":null,"abstract":"Recent research shows that the lip feature can achieve reliable authentication performance with a good liveness detection ability. However, with the development of sophisticated human face generation methods by the deepfake technology, the talking videos can be forged with high quality and the static lip information is not reliable in such case. Meeting with such challenge, in this paper, we propose a new deep neural network structure to extract robust lip features against human and Computer-Generated (CG) imposters. Two novel network units, i.e. the feature-level Difference block (Diffblock) and the pixel-level Dynamic Response block (DRblock), are proposed to reduce the influence of the static lip information and to represent the dynamic talking habit information. Experiments on the GRID dataset have demonstrated that the proposed network can extract discriminative and robust lip features and outperform two state-of-the-art visual speaker authentication approaches in both human imposter and CG imposter scenarios.","PeriodicalId":405734,"journal":{"name":"2020 IEEE International Conference on Image Processing (ICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249282","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：