Kosei Kurisu, N. Suematsu, Kazunori Iwata, A. Hayashi
Finite mixture modeling has been widely used for image segmentation. However, since it takes no account of the spatial correlation among pixels in its standard form, its segmentation accuracy can be heavily deteriorated by noise in images. To improve segmentation accuracy in noisy images, the spatially variant finite mixture model has been proposed, in which a Markov Random Filed (MRF) is used as the prior for the mixing proportions and its parameters are estimated using the Expectation-Maximization (EM) algorithm based on the maximum a posteriori (MAP) criterion. In this paper, we propose a spatially correlated mixture model in which the mixing proportions are governed by a set of underlying functions whose common prior distribution is a Gaussian process. The spatial correlation can be expressed with a Gaussian process easily and flexibly. Given an image, the underlying functions are estimated by using a quasi EM algorithm and used to segment the image. The effectiveness of the proposed technique is demonstrated by an experiment with synthetic images.
{"title":"Image Segmentation Using a Spatially Correlated Mixture Model with Gaussian Process Priors","authors":"Kosei Kurisu, N. Suematsu, Kazunori Iwata, A. Hayashi","doi":"10.1109/ACPR.2013.21","DOIUrl":"https://doi.org/10.1109/ACPR.2013.21","url":null,"abstract":"Finite mixture modeling has been widely used for image segmentation. However, since it takes no account of the spatial correlation among pixels in its standard form, its segmentation accuracy can be heavily deteriorated by noise in images. To improve segmentation accuracy in noisy images, the spatially variant finite mixture model has been proposed, in which a Markov Random Filed (MRF) is used as the prior for the mixing proportions and its parameters are estimated using the Expectation-Maximization (EM) algorithm based on the maximum a posteriori (MAP) criterion. In this paper, we propose a spatially correlated mixture model in which the mixing proportions are governed by a set of underlying functions whose common prior distribution is a Gaussian process. The spatial correlation can be expressed with a Gaussian process easily and flexibly. Given an image, the underlying functions are estimated by using a quasi EM algorithm and used to segment the image. The effectiveness of the proposed technique is demonstrated by an experiment with synthetic images.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134000341","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}
A new finger-vein image matching method based on structure feature is proposed in this paper. To describe the finger-vein structures conveniently, the vein skeletons are firstly extracted and used as the primitive information. Based on the skeletons, a curve tracing scheme depended on junction points is proposed for curve segment extraction. Next, the curve segments are encoded piecewise using a modified included angle chain, and the structure feature code of a vein network are generated sequentially. Finally, a dynamic scheme is adopted for structure feature matching. Experimental results show that the proposed method perform well in improving finger-vein matching accuracy.
{"title":"Structure Feature Extraction for Finger-Vein Recognition","authors":"Di Cao, Jinfeng Yang, Yihua Shi, Chenghua Xu","doi":"10.1109/ACPR.2013.113","DOIUrl":"https://doi.org/10.1109/ACPR.2013.113","url":null,"abstract":"A new finger-vein image matching method based on structure feature is proposed in this paper. To describe the finger-vein structures conveniently, the vein skeletons are firstly extracted and used as the primitive information. Based on the skeletons, a curve tracing scheme depended on junction points is proposed for curve segment extraction. Next, the curve segments are encoded piecewise using a modified included angle chain, and the structure feature code of a vein network are generated sequentially. Finally, a dynamic scheme is adopted for structure feature matching. Experimental results show that the proposed method perform well in improving finger-vein matching accuracy.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131014866","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}
Colour Filter Array (CFA) demosaicking is a process to interpolate missing colour values in order to produce a full colour image when a single image sensor is used. For smooth regions, a higher order of interpolation will usually achieve higher accuracy. However when there is a colour edge, a lower order of interpolation is desirable as it will avoid interpolation across an edge without blurring it. In this paper, a bilateral filter, which has been known to preserve sharp edges, is used to adaptively modify the weights for interpolation. When there is a colour edge, the weights will bias towards a lower order of interpolation using closer pixel values only. Otherwise, the weights will bias towards a higher interpolation for smooth regions. In order to avoid interpolation across a possible edge adjacent to the missing pixel location, four estimates using the adaptive bilateral filter are first determined for each cardinal direction. A classifier comprising a weighted median filter together with a bilateral filter is then used to produce an output of the missing colour pixel value from the four estimates. It has been shown that our proposed method has improved performance in preserving sharp colour edges with minimal colour artifacts, and it outperforms other existing demosaicking methods for most images.
{"title":"Adaptive CFA Demosaicking Using Bilateral Filters for Colour Edge Preservation","authors":"J. S. J. Li, S. Randhawa","doi":"10.1109/ACPR.2013.75","DOIUrl":"https://doi.org/10.1109/ACPR.2013.75","url":null,"abstract":"Colour Filter Array (CFA) demosaicking is a process to interpolate missing colour values in order to produce a full colour image when a single image sensor is used. For smooth regions, a higher order of interpolation will usually achieve higher accuracy. However when there is a colour edge, a lower order of interpolation is desirable as it will avoid interpolation across an edge without blurring it. In this paper, a bilateral filter, which has been known to preserve sharp edges, is used to adaptively modify the weights for interpolation. When there is a colour edge, the weights will bias towards a lower order of interpolation using closer pixel values only. Otherwise, the weights will bias towards a higher interpolation for smooth regions. In order to avoid interpolation across a possible edge adjacent to the missing pixel location, four estimates using the adaptive bilateral filter are first determined for each cardinal direction. A classifier comprising a weighted median filter together with a bilateral filter is then used to produce an output of the missing colour pixel value from the four estimates. It has been shown that our proposed method has improved performance in preserving sharp colour edges with minimal colour artifacts, and it outperforms other existing demosaicking methods for most images.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131043700","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}
This paper proposes a novel method to extract melanin and hemoglobin concentrations of human skin, using bilateral decomposition with the knowledge of a multiple layered skin model and absorbance characteristics of major chromophores. Different from state-of-art approaches, the proposed method enables to address highlight and strong shading usually existing in skin color images captured under uncontrolled environment. The derived melanin and hemoglobin indices, directly related to the pathological tissue conditions, tend to be less influenced by external imaging factors and are effective for describing pigmentation distributions. Experiments demonstrate the value of the proposed method for computer-aided diagnosis of different skin diseases. The diagnostic accuracy of melanoma increases by 9-15% for conventional RGB lesion images, compared to techniques using other color descriptors. The discrimination of inflammatory acne and hyper pigmentation reveals acne stage, which would be useful for acne severity evaluation. It is expected that this new method will prove useful for other skin disease analysis.
{"title":"Melanin and Hemoglobin Identification for Skin Disease Analysis","authors":"Zhao Liu, J. Zerubia","doi":"10.1109/ACPR.2013.9","DOIUrl":"https://doi.org/10.1109/ACPR.2013.9","url":null,"abstract":"This paper proposes a novel method to extract melanin and hemoglobin concentrations of human skin, using bilateral decomposition with the knowledge of a multiple layered skin model and absorbance characteristics of major chromophores. Different from state-of-art approaches, the proposed method enables to address highlight and strong shading usually existing in skin color images captured under uncontrolled environment. The derived melanin and hemoglobin indices, directly related to the pathological tissue conditions, tend to be less influenced by external imaging factors and are effective for describing pigmentation distributions. Experiments demonstrate the value of the proposed method for computer-aided diagnosis of different skin diseases. The diagnostic accuracy of melanoma increases by 9-15% for conventional RGB lesion images, compared to techniques using other color descriptors. The discrimination of inflammatory acne and hyper pigmentation reveals acne stage, which would be useful for acne severity evaluation. It is expected that this new method will prove useful for other skin disease analysis.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128187082","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}
This paper investigates the problem of cross-modal retrieval, where users can search results across various modalities by submitting any modality of query. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. To address this problem, we propose a joint graph regularized multi-modal subspace learning (JGRMSL) algorithm, which integrates inter-modality similarities and intra-modality similarities into a joint graph regularization to better explore the cross-modal correlation and the local manifold structure in each modality of data. To obtain good class separation, the idea of Linear Discriminant Analysis (LDA) is incorporated into the proposed method by maximizing the between-class covariance of all projected data and minimizing the within-class covariance of all projected data. Experimental results on two public cross-modal datasets demonstrate the effectiveness of our algorithm.
{"title":"Multi-modal Subspace Learning with Joint Graph Regularization for Cross-Modal Retrieval","authors":"K. Wang, Wei Wang, R. He, Liang Wang, T. Tan","doi":"10.1109/ACPR.2013.44","DOIUrl":"https://doi.org/10.1109/ACPR.2013.44","url":null,"abstract":"This paper investigates the problem of cross-modal retrieval, where users can search results across various modalities by submitting any modality of query. Since the query and its retrieved results can be of different modalities, how to measure the content similarity between different modalities of data remains a challenge. To address this problem, we propose a joint graph regularized multi-modal subspace learning (JGRMSL) algorithm, which integrates inter-modality similarities and intra-modality similarities into a joint graph regularization to better explore the cross-modal correlation and the local manifold structure in each modality of data. To obtain good class separation, the idea of Linear Discriminant Analysis (LDA) is incorporated into the proposed method by maximizing the between-class covariance of all projected data and minimizing the within-class covariance of all projected data. Experimental results on two public cross-modal datasets demonstrate the effectiveness of our algorithm.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133930053","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}
Image segmentation is a fundamental task of image processing that consists in partitioning the image by grouping pixels into homogeneous regions. We propose a novel segmentation algorithm that consists in combining many runs of a simple and fast randomized segmentation algorithm. Our algorithm also yields a soft-edge closed contour detector. We describe the theoretical probabilistic framework and report on our implementation that experimentally corroborates that performance increases with the number of runs.
{"title":"Consensus Region Merging for Image Segmentation","authors":"F. Nielsen, R. Nock","doi":"10.1109/ACPR.2013.142","DOIUrl":"https://doi.org/10.1109/ACPR.2013.142","url":null,"abstract":"Image segmentation is a fundamental task of image processing that consists in partitioning the image by grouping pixels into homogeneous regions. We propose a novel segmentation algorithm that consists in combining many runs of a simple and fast randomized segmentation algorithm. Our algorithm also yields a soft-edge closed contour detector. We describe the theoretical probabilistic framework and report on our implementation that experimentally corroborates that performance increases with the number of runs.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115378011","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}
In this paper an ObjectCode method is presented for fast template matching. Firstly, Local Binary Patterns are adopted to get the patterns for the template and the search image, respectively. Then, a selection strategy is proposed to choose a small portion of pixels (on average 1.87%) from the template, whose patterns are concatenated to form an ObjectCode representing the characteristics of the interested target region. For the candidates in the search image, we get the candidate codes using the selected pixels from the template accordingly. Finally, the similarities between the ObjectCode and the candidate codes are calculated efficiently by a new distance measure based on Hamming distance. Extensive experiments demonstrated that our method is 13.7 times faster than FFT-based template matching and 1.1 times faster than Two-stage Partial Correlation Elimination (TPCE) with similar performances, thus is a fast alternative for current template matching methods.
{"title":"A Fast Alternative for Template Matching: An ObjectCode Method","authors":"Yiping Shen, Shuxiao Li, Chenxu Wang, Hongxing Chang","doi":"10.1109/ACPR.2013.80","DOIUrl":"https://doi.org/10.1109/ACPR.2013.80","url":null,"abstract":"In this paper an ObjectCode method is presented for fast template matching. Firstly, Local Binary Patterns are adopted to get the patterns for the template and the search image, respectively. Then, a selection strategy is proposed to choose a small portion of pixels (on average 1.87%) from the template, whose patterns are concatenated to form an ObjectCode representing the characteristics of the interested target region. For the candidates in the search image, we get the candidate codes using the selected pixels from the template accordingly. Finally, the similarities between the ObjectCode and the candidate codes are calculated efficiently by a new distance measure based on Hamming distance. Extensive experiments demonstrated that our method is 13.7 times faster than FFT-based template matching and 1.1 times faster than Two-stage Partial Correlation Elimination (TPCE) with similar performances, thus is a fast alternative for current template matching methods.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"85 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115734955","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}
Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. This categorisation is typically performed by manual evaluation which is time consuming and subjective. In this paper, we present a method for automatic classification of HEp-2 cells using local binary pattern (LBP) based texture descriptors and ensemble classification. In our approach, we utilise multi-dimensional LBP (MD-LBP) histograms, which record multi-scale texture information while maintaining the relationships between the scales. Our dedicated ensemble classification approach is based on a set of heterogeneous base classifiers obtained through application of different feature selection algorithms, a diversity based pruning stage and a neural network classifier fuser. We test our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to outperform all algorithms that were entered in the competition as well as to exceed the performance of a human expert.
{"title":"HEp-2 Cell Classification Using Multi-dimensional Local Binary Patterns and Ensemble Classification","authors":"G. Schaefer, N. Doshi, B. Krawczyk","doi":"10.1109/ACPR.2013.175","DOIUrl":"https://doi.org/10.1109/ACPR.2013.175","url":null,"abstract":"Indirect immunofluorescence imaging is a fundamental technique for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications on different autoimmune diseases. This categorisation is typically performed by manual evaluation which is time consuming and subjective. In this paper, we present a method for automatic classification of HEp-2 cells using local binary pattern (LBP) based texture descriptors and ensemble classification. In our approach, we utilise multi-dimensional LBP (MD-LBP) histograms, which record multi-scale texture information while maintaining the relationships between the scales. Our dedicated ensemble classification approach is based on a set of heterogeneous base classifiers obtained through application of different feature selection algorithms, a diversity based pruning stage and a neural network classifier fuser. We test our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to outperform all algorithms that were entered in the competition as well as to exceed the performance of a human expert.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125335923","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}
When we act in a group with family members, friends, colleagues, each group member often play the respective role to achieve a goal that all group members have in common. This paper focuses on leadership among various kinds of roles observed in a social group and proposes a method to estimate a leader based on an interaction analysis. In order to estimate a leader in a group, we extract pointing actions of each person and measure how other people change their actions triggered by the pointing actions, i.e. how much influence the pointing actions have. When we can see the tendency that one specific person makes pointing actions and the actions have a high influence on another member, it is very likely that the person is a leader in a group. The proposed method is based on this intuition and measures the influence of pointing actions using their motion trajectories. We demonstrate that the proposed method has a potential for estimating the leadership through a comparison between the computed influence measures and subjective evaluations using some actual videos taken in a science museum.
{"title":"Group Leadership Estimation Based on Influence of Pointing Actions","authors":"H. Habe, K. Kajiwara, Ikuhisa Mitsugami, Y. Yagi","doi":"10.1109/ACPR.2013.181","DOIUrl":"https://doi.org/10.1109/ACPR.2013.181","url":null,"abstract":"When we act in a group with family members, friends, colleagues, each group member often play the respective role to achieve a goal that all group members have in common. This paper focuses on leadership among various kinds of roles observed in a social group and proposes a method to estimate a leader based on an interaction analysis. In order to estimate a leader in a group, we extract pointing actions of each person and measure how other people change their actions triggered by the pointing actions, i.e. how much influence the pointing actions have. When we can see the tendency that one specific person makes pointing actions and the actions have a high influence on another member, it is very likely that the person is a leader in a group. The proposed method is based on this intuition and measures the influence of pointing actions using their motion trajectories. We demonstrate that the proposed method has a potential for estimating the leadership through a comparison between the computed influence measures and subjective evaluations using some actual videos taken in a science museum.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122024960","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}
Natural image matting is a useful and challenging task when processing image or editing video. It aims at solving the problem of accurately extracting the foreground object of arbitrary shape from an image by use of user-provided extra information, such as trimap. In this paper, we present a new sampling criterion based on random search for image matting. This improved random search algorithm can effectively avoid leaving good samples out and can also deal well with the relation between nearby samples and distant samples. In addition, an effective cost function is adopted to evaluate the candidate samples. The experimental results show that our method can produce high-quality mattes.
{"title":"Improving Sampling Criterion for Alpha Matting","authors":"Jun Cheng, Z. Miao","doi":"10.1109/ACPR.2013.145","DOIUrl":"https://doi.org/10.1109/ACPR.2013.145","url":null,"abstract":"Natural image matting is a useful and challenging task when processing image or editing video. It aims at solving the problem of accurately extracting the foreground object of arbitrary shape from an image by use of user-provided extra information, such as trimap. In this paper, we present a new sampling criterion based on random search for image matting. This improved random search algorithm can effectively avoid leaving good samples out and can also deal well with the relation between nearby samples and distant samples. In addition, an effective cost function is adopted to evaluate the candidate samples. The experimental results show that our method can produce high-quality mattes.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123384406","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：