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2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)最新文献

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Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition 卷积自编码器在半空间雷达hrp识别中的应用
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521306
Shisen Yu, Y. Xie
{"title":"Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition","authors":"Shisen Yu, Y. Xie","doi":"10.1109/ICWAPR.2018.8521306","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521306","url":null,"abstract":"A Winner- Take-All convolutional autoencoder is applied to improve the performance on the half space radar high resolution range profile(HRRP) target recognition. Feature extraction is significantly important to the radar target recognition based on the HRRP. Conventional deep models of representation learning used for HRRP target recognition commonly use the vanilla autoen-coder and deep belief net (DBN), moreover, the simulated HRRP samples used in these related work are mostly under the free space condition which is different from the real world situation. In this paper, convolution architecture autoencoder, which is more efficient in spatial feature extraction and sparse coding, is proposed. Furthermore, the half space HRRP samples, which is much more close to the real world situation and is quite different from the free space HRRP samples, is used as the dataset. Half space simulated HRRP data is used to apply the convolutional architecture on the ground target recognition and got an accuracy promotion about 7% compared to conventional vector-based module.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114254755","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: 7
Hyperspectral Image Classification Based on Different Affinity Metrics 基于不同亲和度量的高光谱图像分类
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521381
Lina Yang, Hailong Su, Cheng Zhong, Lin Bai, Pu Wei, Xiaocui Dang, Huiwu Luo
{"title":"Hyperspectral Image Classification Based on Different Affinity Metrics","authors":"Lina Yang, Hailong Su, Cheng Zhong, Lin Bai, Pu Wei, Xiaocui Dang, Huiwu Luo","doi":"10.1109/ICWAPR.2018.8521381","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521381","url":null,"abstract":"With the development of hyperspectral sensor technologies, hyperspectral image classification has been a popular area in recent years. In this paper, we adopt different metric models: Euclidean distance and Spectral-spatial distance to learn the similarity ofhy-perspectral image (HSI) pixels. Then, we combine them with the smooth ordering model, which has been proposed in image processing to extract features of HSI. Finally, we utilize interpolation technology to create a decision function, which is to construct ultima classifier for the whole HSI pixels. The experiments demonstrate that these two metric combining multi-lDMEs can improve accuracy of HSI classification.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114308325","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
Non-Negative Half-Space Clustering with Sparseness Constraints 稀疏约束下的非负半空间聚类
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521380
L. Li, Jinyu Tian
{"title":"Non-Negative Half-Space Clustering with Sparseness Constraints","authors":"L. Li, Jinyu Tian","doi":"10.1109/ICWAPR.2018.8521380","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521380","url":null,"abstract":"This paper describes a novel clustering approach by revealing the non-negative half-space clustering with sparseness constraints (NHCS). Sparseness can make only few components of whole samples to be ‘active’. Especially, this method is more part-based compared to other matrix factorization methods, which is sensitive to the scale of the data. After obtaining the part-based structure, the samples can be grouped by spectral cutting techniques. It shows that our method has more robust with the increasing of the number of clusters. Both theoretical and experimental results show that NHCS performs better than other competitive algorithms on the two database CBCL and Reuters-21578.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415617","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
Feature Analysis on the Containment Time for Cyber Security Incidents 网络安全事件遏制时间特征分析
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521252
Gulsum Akkuzu, Benjamin Azizl, Hanliu
{"title":"Feature Analysis on the Containment Time for Cyber Security Incidents","authors":"Gulsum Akkuzu, Benjamin Azizl, Hanliu","doi":"10.1109/ICWAPR.2018.8521252","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521252","url":null,"abstract":"Data mining techniques have been widely used as a common goal to discover hidden patterns from big data sets, so researchers have been motivated to make use of data in discovering useful information. The main contribution of this paper lies in its identifying relevant features from an open data set to predict the containment time of Cyber incidents. In particular, 13 relevant features were identified and selected to come up with a predictive model. Our results are discussed in the context of the organization‘s' information security.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125317023","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: 2
Image Description Generation by Modeling the Relationship Between Objects 基于对象间关系建模的图像描述生成
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521291
Lin Bai, Lina Yang, Lin Huo, Taosheng Li
{"title":"Image Description Generation by Modeling the Relationship Between Objects","authors":"Lin Bai, Lina Yang, Lin Huo, Taosheng Li","doi":"10.1109/ICWAPR.2018.8521291","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521291","url":null,"abstract":"Automatically describing the content of an image is a challenging task in computer vision that connects the machine learning and natural language processing. In this paper, we present a framework, based on modeling image context, to generate natural sentences describing an image, which consists of two parts: relation modeling and description generating. By modeling the mapping from image spatial context to the logical relationship between objects, the former is trained to maximize the likelihood of the target linguistics phrase describing the relationship between object given the training image. By taking the the advantages of the syntactic-tree based method, the latter takes the predicted relationships as key ingredients to facilitate the image description generation within tree-growth process. We conduct extensive experimental evaluations on MS COCO dataset. Our framework outperforms the state-of-the-art methods. The results demonstrates that our framework provides robust and significant improvements for the relationship prediction between objects and the image description generation.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129282401","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 Enhancement of Seismic Signal by the Filtering Method Based on Synchrosqueezing Transform 基于同步压缩变换的滤波方法增强地震信号
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521352
Yan-ping Liu, Li Liu, Qi Zhang, Jing Shi
{"title":"The Enhancement of Seismic Signal by the Filtering Method Based on Synchrosqueezing Transform","authors":"Yan-ping Liu, Li Liu, Qi Zhang, Jing Shi","doi":"10.1109/ICWAPR.2018.8521352","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521352","url":null,"abstract":"The Synchrosqueezing Transform (SST) is a new time-frequency analysis method which is adaptive and invertible. It can obtain high time-frequency resolution by condensing and rearranging time-frequency representation (TFR) along the frequency axis. This paper proposes a filtering method based on SST for seismic signal enhancement and random noise reduction. Through experiments on synthetic signals, it demonstrates that the performance of the new method is better than the filtering methods based on conventional time-frequency transforms such as wavelet transform and so on.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123441643","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 International Conference on Wavelet Analysis and Pattern Recognition 小波分析与模式识别国际会议论文集
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/icwapr.2018.8521360
{"title":"Proceedings of International Conference on Wavelet Analysis and Pattern Recognition","authors":"","doi":"10.1109/icwapr.2018.8521360","DOIUrl":"https://doi.org/10.1109/icwapr.2018.8521360","url":null,"abstract":"","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116690921","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
An Information Source Localization Algorithm Based on Node Reachability Measurement 基于节点可达性测量的信息源定位算法
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521294
Hong Zhang, Bing Guo, Yan Shen, Xuliang Duan, Yuncheng Shen, Xiangqian Dong
{"title":"An Information Source Localization Algorithm Based on Node Reachability Measurement","authors":"Hong Zhang, Bing Guo, Yan Shen, Xuliang Duan, Yuncheng Shen, Xiangqian Dong","doi":"10.1109/ICWAPR.2018.8521294","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521294","url":null,"abstract":"In order to research the localization problem of information source in social network, this paper constructs complex network node model based on cellular automaton (Random-Susceptible-Infected-Recovered, R-SIS). The number of forwarding paths and the probability of the forwarding path of other nodes are then utilized and a method of localization information source based on node reachability measurement is submitted. Experimental results show that the algorithm can avoid the time complexity of maximum likelihood estimation and overcome the inaccuracy of step estimation in the reachability measure which is of great significance for information protection in social networks.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117277855","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
A Novel Approach to Traffic Sign Recognition Based on the Optimization Under Transformations 一种基于变换下优化的交通标志识别新方法
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521275
Runzong Liu, Bin Fang, Linchang Zhao, Jing Wen
{"title":"A Novel Approach to Traffic Sign Recognition Based on the Optimization Under Transformations","authors":"Runzong Liu, Bin Fang, Linchang Zhao, Jing Wen","doi":"10.1109/ICWAPR.2018.8521275","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521275","url":null,"abstract":"An automatic traffic sign recognition system can provide drivers with extremely important information for safe and successful driving. This paper proposes an automatic approach for traffic sign recognition in uncontrolled environments. The main idea of this method is to transform the patterns of the traffic signs into an optimal state defined by a function on the transformations. Under the optimal state, the undesired transformation effects on the traffic sign patterns are removed, and this can help improving the precision of traffic sign recognition. A coarse to fine traffic sign recognition system based on template matching is designed in the consideration of both efficiency and precision. Experimental results showed that the proposed approach performed well for traffic sign recognition.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133001831","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
Design and Application of Image Enhancement Operator Based on Fractional Differential Interpolation Operation 基于分数阶微分插值运算的图像增强算子的设计与应用
2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) Pub Date : 2018-07-01 DOI: 10.1109/ICWAPR.2018.8521398
Wen Yang, Chaobang Gao, Yudie Zhong, Qiang-feng Zhou
{"title":"Design and Application of Image Enhancement Operator Based on Fractional Differential Interpolation Operation","authors":"Wen Yang, Chaobang Gao, Yudie Zhong, Qiang-feng Zhou","doi":"10.1109/ICWAPR.2018.8521398","DOIUrl":"https://doi.org/10.1109/ICWAPR.2018.8521398","url":null,"abstract":"Image enhancement is an important part of image processing, affected by the process of image acquisition, transformation, transmission, etc., the image quality will be reduced, the subsequent processing will be limited by the problems. Based on the characteristic of texture enhancement and noise suppression, fractional differentiation operation has been used in image processing, since it can not only strengthen the high and middle frequency components of the signal, but also can preserve the low frequency components. The paper designs an improved image enhanced operator based on the combination of fractional differentiation and interpolating operation called the maximum of interpolating operation fractional differentiation (MIOFD). Experiments show that the texture and edges of the images processed by MIOFD are enhanced. The enhancement is better than the integer differentiation, compared with traditional fractional differentiation, our method can preserve non-node pixels information and enlarge the difference of details, also makes the enhancement better.","PeriodicalId":385478,"journal":{"name":"2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130107620","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
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