2022 7th International Conference on Communication, Image and Signal Processing (CCISP)最新文献_第10页

Robust Secret Image Sharing Based on Robust Chinese Reminder Theorem 基于鲁棒中文提醒定理的鲁棒秘密图像共享

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974309

Xiaohui Jin, Fuyou Miao

引用次数: 0

The Evaluation of Camouflage Based on Image Edge Contour Similarity 基于图像边缘轮廓相似度的伪装效果评价

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974259

Xianli Zhou, Wenwen Zhu, F. Liu, Wei Yang, Miao Chu

引用次数: 2

The Problem of Collecting Face Recognition Information in Colleges and Universities 高校人脸识别信息的采集问题

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974340

Zhao Feng

引用次数: 0

Passive Positioning from Azimuth Difference of Opportunistic Signals Without Attitude Measurement 无姿态测量的机会信号方位差被动定位

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974331

Junlin Li, Maoyan Ran, Ying He, Jinke Yang, Xiongfei Li, Q. Wan

{"title":"Passive Positioning from Azimuth Difference of Opportunistic Signals Without Attitude Measurement","authors":"Junlin Li, Maoyan Ran, Ying He, Jinke Yang, Xiongfei Li, Q. Wan","doi":"10.1109/CCISP55629.2022.9974331","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974331","url":null,"abstract":"Multi-station directional crossover technique is a commonly used passive localization technique. In contrast to this technique, in the field of radio localization of sensor network nodes, multiple radiation sources with known locations can also be used to localize nodes with unknown locations that are capable of receiving radio signals emitted from the sources, i.e., the nodal direction finding cross-location method. The nodal direction finding cross-location method requires attitude measurement, and since attitude measurement has errors and increases the size, weight and cost of the node. This paper proposes to use the incoming azimuth difference measured at the node to localize the node, which does not require attitude measurement. We verify through simulation that the method ensures localization accuracy while saving attitude measurement, which is of great importance in engineering practice. The influence of various measurement parameters in the positioning system on positioning accuracy is also analyzed, among which orientation accuracy is the main influencing factor of positioning accuracy.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125108413","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

The Application of Neural Network in Dish Recognition 神经网络在菜肴识别中的应用

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974546

Xinyi Sun

引用次数: 0

Sketch recognition based on attention mechanism and improved residual network 基于注意机制和改进残差网络的素描识别

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974584

Qiansheng Fang, Qiyu Li, Liangliang Su, Yalong Yang

{"title":"Sketch recognition based on attention mechanism and improved residual network","authors":"Qiansheng Fang, Qiyu Li, Liangliang Su, Yalong Yang","doi":"10.1109/CCISP55629.2022.9974584","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974584","url":null,"abstract":"Sketches are usually composed of simple strokes. Compared with the natural image, they lack the information of texture and color. Most of the existing works do not reduce the impact of blank region in sketches very well. This paper proposes a new deep convolutional neural network named Sketch Fusion Net (SFN) model to minimize the effect of the blank region and focus on information region. This model is mainly composed of Sketch Block Convolutional (SBConv) modules, the SBCnov module integrates an attention mechanism and an improved residual network. The first one can effectively extract the effective part of the sketch information. The other one uses multi-level feature combination strategy to extract richer semantic information, which can effectively relieve the problem of model degradation. Finally, two public datasets, namely the TU-Berlin and Sketchy, are used for sketch recognition experiments. The results demonstrate that this proposed method improves the recognition accuracy by 2.1% and 7.2% over several state-of-the-art methods and yields promising results.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121962634","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: 1

Research on PCEP Extension for VLAN-based Traffic Forwarding in cloud network integration 云网络集成中基于vlan的流量转发的PCEP扩展研究

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974205

Yue Wang, Aijun Wang, Honglei Xu, W. Wang, Huanan Li, Zhen-fu Cui

引用次数: 0

Simulation and Spatial-Temporal Characteristics Analysis of Underwater Bubble Screen Echo 水下气泡屏回波仿真及时空特征分析

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974294

Wei Lv, Na Sun, Wenxi Ni, Shen Shen

引用次数: 0

Magnetic Anomaly Detection Based on Singular Spectrum Analysis and Orthonormal Basis Functions 基于奇异谱分析和正交基函数的磁异常检测

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974291

C. Du, Chao Zhang, Xiang Peng, Hong Guo

{"title":"Magnetic Anomaly Detection Based on Singular Spectrum Analysis and Orthonormal Basis Functions","authors":"C. Du, Chao Zhang, Xiang Peng, Hong Guo","doi":"10.1109/CCISP55629.2022.9974291","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974291","url":null,"abstract":"Magnetic anomaly detection (MAD) is a method to find the ferromagnetic object by recognizing the weak target magnetic signal in the complex background magnetic noise. In the practical detection, background magnetic noise is usually complex colored noise, so the magnetic noise needs firstly to be suppressed before detecting the measurement magnetic data. In this paper, singular spectrum analysis (SSA) method is firstly introduced to decompose the test data to improve the signal-to-noise ratio (SNR) of detection data. In the decomposing procedure, the clustering method is used to classify the singular values and select out the singular values containing the information of target to reconstruct the new signal to be detected. And then the orthogonal basis functions (OBFs) is applied to detect the reconstructed signal considering that the OBFs has strong resistance to white noise. Some simulation experiments were conducted to show that the detection probability of this method in this paper for the target signal submerged in colored noise is improved by more than 25% compared with the traditional OBFs detection algorithm.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121106990","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: 1

A Robust Method for Electrical Equipment Infrared and Visible Image Registration 一种鲁棒的电气设备红外和可见光图像配准方法

2022 7th International Conference on Communication, Image and Signal Processing (CCISP) Pub Date : 2022-11-01 DOI: 10.1109/CCISP55629.2022.9974532

Ying Lin, Fengda Zhang, Meng Liu, Zhuangzhuang Li, Wenjie Zheng, Yi Yamg

{"title":"A Robust Method for Electrical Equipment Infrared and Visible Image Registration","authors":"Ying Lin, Fengda Zhang, Meng Liu, Zhuangzhuang Li, Wenjie Zheng, Yi Yamg","doi":"10.1109/CCISP55629.2022.9974532","DOIUrl":"https://doi.org/10.1109/CCISP55629.2022.9974532","url":null,"abstract":"The integration of infrared and visible images can take advantage of temperature information from infrared modality and sharp appearance from visible modality, and therefore it is helpful to improve the accuracy of localization and fault diagnosis of electrical equipment. A key step towards integration analysis is to register the images in infrared and visible modalities. In this paper, we propose a new method for infrared and visible image registration. In order to deal with large difference between these two modalities, we first transform both infrared and visible images into radiation-invariant maps. Then, LoFTR, which is a self-attention based deep neural network, is adopted to extract and match features based on the radiation-invariant maps. Finally, we utilize a progressive sample consensus (PROSAC) algorithm to estimate the transformation parameters, based on which the infrared image can be transformed into the corresponding visible image coordinates. Experiments on an electrical equipment dataset show that our proposed method is robust to both radiation and geometric variations.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126700117","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