Proceedings of the 2023 8th International Conference on Multimedia and Image Processing最新文献_第2页

Simulating Quantum Turing Machine in Augmented Reality 增强现实中的量子图灵机模拟

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599606

Wanwan Li

引用次数: 1

Reconstruction of hyperspectral images with compressed sensing based on linear mixing model and affinity propagation clustering algorithm 基于线性混合模型和亲和传播聚类算法的压缩感知高光谱图像重建

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599602

Youli zou, Zhi-yun Xiao, Kuntao Ye

{"title":"Reconstruction of hyperspectral images with compressed sensing based on linear mixing model and affinity propagation clustering algorithm","authors":"Youli zou, Zhi-yun Xiao, Kuntao Ye","doi":"10.1145/3599589.3599602","DOIUrl":"https://doi.org/10.1145/3599589.3599602","url":null,"abstract":"The increasing spatial and spectral resolution of hyperspectral images results in a significant rise in data volume, which poses a challenge for data storage and transmission. Therefore, improving the efficiency of storage and transmission by enhancing the reconstruction performance of hyperspectral images at low sampling rates or same sampling rates conditions is a crucial topic in compressed sensing. Previous research has shown that a linear mixing model and distributed compressed sensing method outperform traditional compressed sensing reconstruction algorithms in recovering original data. However, the low estimating accuracy of both the endmembers matrix and abundance matrix due to the random selection of reference bands limits the reconstruction performance. To address this problem, we proposed a compressed sensing reconstruction algorithm based on a linear mixing model and affinity propagation clustering algorithm. Our method improves reconstruction performance by enhancing the estimating accuracy of the endmembers and abundance matrices. During the sampling stage, the affinity propagation clustering algorithm is used to group the spectral bands according to the spectral correlation of hyperspectral images, where the clustering center serving as the reference band and the other bands as non-reference bands. During the reconstruction stage, the number of endmembers from the reference band is estimated fist, and the endmembers matrix and the abundance matrix are then estimated. Finally, the endmembers matrix and estimated abundance matrix are used for reconstruction. Experimental results show that our proposed algorithm achieves higher performance in reconstructing hyperspectral images than the linear mixing model-based distributed compressed sensing method.","PeriodicalId":123753,"journal":{"name":"Proceedings of the 2023 8th International Conference on Multimedia and Image Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125431466","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}

引用次数: 0

Fast Recognition of Distributed Fiber Optic Vibration Sensing Signal based on Machine Vision in High-speed Railway Security 高速铁路安防中基于机器视觉的分布式光纤振动传感信号快速识别

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599603

Nachuan Yang, Yongjun Zhao, Fuqiang Wang

{"title":"Fast Recognition of Distributed Fiber Optic Vibration Sensing Signal based on Machine Vision in High-speed Railway Security","authors":"Nachuan Yang, Yongjun Zhao, Fuqiang Wang","doi":"10.1145/3599589.3599603","DOIUrl":"https://doi.org/10.1145/3599589.3599603","url":null,"abstract":"Accurate and effective identification of multi-vibration events detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is an effective method to achieve precise alarm. This study proposes a real-time classification method of Φ-OTDR multi-vibration events based on the combination of convolutional neural network (CNN), bi-directional long short-term memory network (Bi-LSTM) and connectionist temporal classification (CTC), which can quickly and effectively identify the type and number of vibrations contained in the data image when multiple vibration signals are present in a single image, and manual alignment is not required for model training. Noncoherent integration and pulse cancellers are used for raw signal processing to generate spatio-temporal images. CNN is used to extract spatial dimensional features in spatio-temporal images, Bi-LSTM extracts temporal dimensional correlation features, and the hybrid features are automatically aligned with the labels by CTC. A dataset of 8,000 vibration images containing 17,589 abnormal vibration events is collected for model training, validation and testing. Experiments show that the recognition model C3B3 trained with this method can achieve 210 FPS and 99.62% F1 score on the test set. The system can achieve the real-time classification of multiple vibration targets at the perimeter of high-speed railway and effectively reduce the false alarm rate of the system.","PeriodicalId":123753,"journal":{"name":"Proceedings of the 2023 8th International Conference on Multimedia and Image Processing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126732946","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}

引用次数: 0

Research on the Sudden Scientific Public Opinion Theme Map and Science Communication Path Method in the Sina Weibo: Case on "2022 Nobel Prize in Physics" 新浪微博突发科学舆情主题图与科学传播路径方法研究——以“2022年诺贝尔物理学奖”为例

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599608

Xiaojue Huo, Ruxue Fan

引用次数: 0

Comparative Study of Different Ground Objects Classification Based on UAV Orthophoto 基于无人机正射影像的不同地物分类比较研究

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599598

Huijun Han, Zhang Shuo, S. Xie

{"title":"Comparative Study of Different Ground Objects Classification Based on UAV Orthophoto","authors":"Huijun Han, Zhang Shuo, S. Xie","doi":"10.1145/3599589.3599598","DOIUrl":"https://doi.org/10.1145/3599589.3599598","url":null,"abstract":"UAV technology is characterized by strong environmental adaptability, flexibility, low cost and high resolution, and has been gradually applied in the field of land use classification in recent years. In order to explore a fast ground objects feature extraction method suitable for high resolution UAV orthophoto image, three commonly used supervised classification methods (Maximum likelihood classification, Mahalanobis distance classification, and Minimum distance classification) are selected to compare and analyze the ground objects feature classification in the study area. The results show that the maximum likelihood classification method is better than the other two classification methods, the classification results are basically consistent with the actual situation, and the overall classification accuracy is 94.21%,1.93% and 11.61% higher than the other two methods respectively; Kappa coefficient can reach 88.29%, which is 4.32% and 14.59% higher than other three methods respectively. Therefore, when the supervised classification method is selected for UAV orthophoto classification, the maximum likelihood method performs best among the three ground objects feature classification methods, and can be given priority in the application of high-resolution UAV orthophoto classification.","PeriodicalId":123753,"journal":{"name":"Proceedings of the 2023 8th International Conference on Multimedia and Image Processing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399110","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}

引用次数: 0

Overlapping Community Discovery Algorithm Based on Seed Node Importance Selection 基于种子节点重要性选择的重叠社区发现算法

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599607

Chen Liu, Xiaoyan Zheng, Pengcheng Zhao

引用次数: 0

Application of Deep Learning in Lunar Volcanic Dome Identification 深度学习在月球火山穹窿识别中的应用

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599597

Chen Sun

{"title":"Application of Deep Learning in Lunar Volcanic Dome Identification","authors":"Chen Sun","doi":"10.1145/3599589.3599597","DOIUrl":"https://doi.org/10.1145/3599589.3599597","url":null,"abstract":"Lunar domes have always been one of the important windows to understand lunar volcanic activity, however traditional identification methods for geological domes are expensive, so this study attempts to establish an automatic identification method for lunar volcanic domes. Given that no previous research in this area has attempted to automate the identification of lunar volcanic domes, our team attempted to automate the process for the first time. To achieve the purpose of this research, the researchers first obtained the dome coordinates from the list of known lunar domes and intercepted the data we needed from the corresponding coordinates on the CCD and DEM moon pictures. Subsequently, the researchers screened the data to find data with more obvious features and used these data to train 9 mainstream image recognition models and compared their accuracy rates to verify the feasibility of this study. Finally, the researchers counted the mAP and AP (IoU=0.5) of the nine models and found that the highest of them could reach 0.64 (mAP) and 0.74 (AP). Therefore, this study can conclude that an automated method for identifying lunar volcanic domes should be feasible.","PeriodicalId":123753,"journal":{"name":"Proceedings of the 2023 8th International Conference on Multimedia and Image Processing","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133040199","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}

引用次数: 0

MFC-Net: A Multiple Feature Complementation Network for Person Re-identification in Aerial Imagery MFC-Net:航空图像中人物再识别的多特征互补网络

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599593

Zichen Yin, Dongmei Liu

引用次数: 0

GCA-Net: Global Cross Aware Network for salient object detection GCA-Net:用于显著目标检测的全局交叉感知网络

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 2023-04-21 DOI: 10.1145/3599589.3599594

Zixuan Zhang, Fan Shi, Xinbo Geng, Y. Tao, Jing He

{"title":"GCA-Net: Global Cross Aware Network for salient object detection","authors":"Zixuan Zhang, Fan Shi, Xinbo Geng, Y. Tao, Jing He","doi":"10.1145/3599589.3599594","DOIUrl":"https://doi.org/10.1145/3599589.3599594","url":null,"abstract":"In recent years, significant progress has been made in salient object detection. Nevertheless, there remains a need for further improvements in the effective combination of local and global perspectives. Combining global perception with the local focus can enhance the ability to capture the integrity of protruding objects while achieving accurate segmentation. In this paper, we introduce a newly proposed salient object detection network named GCA-Net, which is built upon three essential components. Firstly, we utilize atrous convolution to effectively aggregate contextual information and refine high-level semantic information via spatial pooling. Secondly, we employ 1-dim crossover operations to achieve global perception while minimizing computational effort. Lastly, We have innovatively integrated a contour-aware loss into our salient object detection task to constrain our model’s predictions, leading to more accurate segmentation. After evaluating our model on the established salient object detection benchmark dataset, we conducted extensive experiments to demonstrate the exceptional performance of our method. As a result, our method achieved 1st in both Sm and MAE.","PeriodicalId":123753,"journal":{"name":"Proceedings of the 2023 8th International Conference on Multimedia and Image Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114396541","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}

引用次数: 0

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing 2023第八届多媒体与图像处理国际会议论文集

Proceedings of the 2023 8th International Conference on Multimedia and Image Processing Pub Date : 1900-01-01 DOI: 10.1145/3599589

引用次数: 0