Pub Date : 2014-12-15DOI: 10.1109/SPAC.2014.6982709
Lu Gan, Long Zhou, Xinge You
This paper presents a new de-noising method for GPR image based on BEMD and wavelet. This method complies with the adaptability from BEMD. The method decomposes the image into a series of IMF components, then applies wavelet threshold de-noising on the selected high frequency IMF components for de-noising. In the reconstruction course, the de-noising IMF and low frequency IMF are combined. The experiment results shows the effectiveness of the method on GPR image.
{"title":"A new GPR image de-nosing method based on BEMD","authors":"Lu Gan, Long Zhou, Xinge You","doi":"10.1109/SPAC.2014.6982709","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982709","url":null,"abstract":"This paper presents a new de-noising method for GPR image based on BEMD and wavelet. This method complies with the adaptability from BEMD. The method decomposes the image into a series of IMF components, then applies wavelet threshold de-noising on the selected high frequency IMF components for de-noising. In the reconstruction course, the de-noising IMF and low frequency IMF are combined. The experiment results shows the effectiveness of the method on GPR image.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114038809","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982698
Chunna Tian, Xiangnan Zhang, Xinbo Gao, Wei Wei
Since color is an important visual clue of the pornographic image, this study presents a new framework for pornographic image classification based on the fusion of color and shape information for the bag of words representation. This framework contains three fusion patterns: The early fusion, late fusion and top down color-saliency based fusion, which are compared intensively. Based on the comparison, the top down color-saliency fusion based pornographic image classification method is proposed by using the statistical class prior of each color word to weight the shape word. In the late fusion and color-saliency based fusion, color name is adopt to represent the color information. To verify the effectiveness of spatial constrain on the words, we also compared the shape features quantized by vector quantization and locality-constrained linear coding. The experimental results show that our model combines the shape and color information properly and it is superior over the popular methods to distinguish the normal and pornographic-like images from the pornographic ones.
{"title":"Pornographic image classification based on top down color-saliency based BoW representation","authors":"Chunna Tian, Xiangnan Zhang, Xinbo Gao, Wei Wei","doi":"10.1109/SPAC.2014.6982698","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982698","url":null,"abstract":"Since color is an important visual clue of the pornographic image, this study presents a new framework for pornographic image classification based on the fusion of color and shape information for the bag of words representation. This framework contains three fusion patterns: The early fusion, late fusion and top down color-saliency based fusion, which are compared intensively. Based on the comparison, the top down color-saliency fusion based pornographic image classification method is proposed by using the statistical class prior of each color word to weight the shape word. In the late fusion and color-saliency based fusion, color name is adopt to represent the color information. To verify the effectiveness of spatial constrain on the words, we also compared the shape features quantized by vector quantization and locality-constrained linear coding. The experimental results show that our model combines the shape and color information properly and it is superior over the popular methods to distinguish the normal and pornographic-like images from the pornographic ones.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115576170","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982667
Yi Li, Zhenyu He, Shuangyan Yi, Wei-Guo Yang
Occlusion is one important problem in single object tracking. However, conventional methods are not capable of making full use of the spatial information because of occlusion, which may lead to the drift. In this paper, we propose a robust patches-based tracking method via sparse representation, namely RPSR, which selects the unoccluded patches, and adaptively assigns larger contribution factors to them. The experimental results on popular benchmark video sequences show that our RPSR method is effective and outperforms the state-of-the-art methods for single object tracking.
{"title":"The robust patches-based tracking method via sparse representation","authors":"Yi Li, Zhenyu He, Shuangyan Yi, Wei-Guo Yang","doi":"10.1109/SPAC.2014.6982667","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982667","url":null,"abstract":"Occlusion is one important problem in single object tracking. However, conventional methods are not capable of making full use of the spatial information because of occlusion, which may lead to the drift. In this paper, we propose a robust patches-based tracking method via sparse representation, namely RPSR, which selects the unoccluded patches, and adaptively assigns larger contribution factors to them. The experimental results on popular benchmark video sequences show that our RPSR method is effective and outperforms the state-of-the-art methods for single object tracking.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168372","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982718
Dong Xia Zheng, Xue Da Sun
First, the research status of knowledge acquisition is analyzed. Second, knowledge acquisition model in maritime domain based on ontology under semantic web environment is built, specifically, how to build maritime domain ontology and how to preprocess Chinese text are researched, the method of maritime domain knowledge acquisition from heterogeneous data sources in network is also researched; at last, ontology confirmation is discussed. In this study the modeling method of knowledge acquisition is not only applicable to the maritime domain, can also be extended to other fields learn to use.
{"title":"A knowledge acquisition model in maritime domain based on ontology","authors":"Dong Xia Zheng, Xue Da Sun","doi":"10.1109/SPAC.2014.6982718","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982718","url":null,"abstract":"First, the research status of knowledge acquisition is analyzed. Second, knowledge acquisition model in maritime domain based on ontology under semantic web environment is built, specifically, how to build maritime domain ontology and how to preprocess Chinese text are researched, the method of maritime domain knowledge acquisition from heterogeneous data sources in network is also researched; at last, ontology confirmation is discussed. In this study the modeling method of knowledge acquisition is not only applicable to the maritime domain, can also be extended to other fields learn to use.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132727021","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982661
Ling Du, Yuhang Li
This paper proposes a privacy preserving scheme for data security in the video surveillance. We firstly separate the foreground for each video frame, and obscure the separated human object by motion blur. For secure storage, each blurred foreground object is encrypted into N shares by visual cryptography, and stored into different servers. Each share is fully confidential and does not convey any meaningful information about the original video, so that breaking into one storage server do not induce any compromise. For legal requirement, the authorized users can recover the original content with better quality by non-blind deblurring algorithm. Moreover, thanks to our exploited foreground based encoding scheme, the data expansion introduced by distributed storage is greatly reduced. It is impossible for unauthorized users to recover the original content by the following reasons: 1) distributed video stream storage; 2) unknown blurring kernel; 3) inaccurate foreground content and mask. The performance evaluation on several surveillance scenarios demonstrates that our proposed method can effectively protect sensitive privacy information in surveillance videos.
{"title":"Privacy preserving for human object in video surveillance via visual cryptography","authors":"Ling Du, Yuhang Li","doi":"10.1109/SPAC.2014.6982661","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982661","url":null,"abstract":"This paper proposes a privacy preserving scheme for data security in the video surveillance. We firstly separate the foreground for each video frame, and obscure the separated human object by motion blur. For secure storage, each blurred foreground object is encrypted into N shares by visual cryptography, and stored into different servers. Each share is fully confidential and does not convey any meaningful information about the original video, so that breaking into one storage server do not induce any compromise. For legal requirement, the authorized users can recover the original content with better quality by non-blind deblurring algorithm. Moreover, thanks to our exploited foreground based encoding scheme, the data expansion introduced by distributed storage is greatly reduced. It is impossible for unauthorized users to recover the original content by the following reasons: 1) distributed video stream storage; 2) unknown blurring kernel; 3) inaccurate foreground content and mask. The performance evaluation on several surveillance scenarios demonstrates that our proposed method can effectively protect sensitive privacy information in surveillance videos.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132115016","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982690
Baodi Liu, Bin Shen, Yu-Xiong Wang
Recently, sparse representation based classification (SRC) has been successfully used for visual recognition and showed impressive performance. Given a testing sample, SRC computes its sparse linear representation with respect to all the training samples and calculates the residual error for each class of training samples. However, SRC considers the training samples in each class contributing equally to the dictionary in that class, i.e., the dictionary consists of the training samples in that class. This may lead to high residual error and instability. In this paper, a class specific dictionary learning algorithm is proposed. First, by introducing the dual form of dictionary learning, an explicit relationship between the bases vectors and the original image features is represented, which enhances the interpretability. SRC can be thus considered to be a special case of the proposed algorithm. Second, blockwise coordinate descent algorithm and Lagrange multipliers are then applied to optimize the corresponding objective function. Extensive experimental results on three benchmark face recognition datasets demonstrate that the proposed algorithm has achieved superior performance compared with conventional classification algorithms.
{"title":"Class specific dictionary learning for face recognition","authors":"Baodi Liu, Bin Shen, Yu-Xiong Wang","doi":"10.1109/SPAC.2014.6982690","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982690","url":null,"abstract":"Recently, sparse representation based classification (SRC) has been successfully used for visual recognition and showed impressive performance. Given a testing sample, SRC computes its sparse linear representation with respect to all the training samples and calculates the residual error for each class of training samples. However, SRC considers the training samples in each class contributing equally to the dictionary in that class, i.e., the dictionary consists of the training samples in that class. This may lead to high residual error and instability. In this paper, a class specific dictionary learning algorithm is proposed. First, by introducing the dual form of dictionary learning, an explicit relationship between the bases vectors and the original image features is represented, which enhances the interpretability. SRC can be thus considered to be a special case of the proposed algorithm. Second, blockwise coordinate descent algorithm and Lagrange multipliers are then applied to optimize the corresponding objective function. Extensive experimental results on three benchmark face recognition datasets demonstrate that the proposed algorithm has achieved superior performance compared with conventional classification algorithms.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397213","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982651
Liu Jing-Wen, Sun Wei-Ping, Xia Tao
Local features have been widely used in visual object tracking for their robustness in illumination, deformation, rotation and partial occlusion. Traditional feature selection algorithms based on accumulated knowledge of previous frames usually adopt the perspective of continuity of changes, which could lead to degradation. Exploiting discrimination and uniqueness of local sub-blocks, we build an automatic preselection mechanism for local features and propose the structured sub-blocks tracking algorithm under particle filter framework. Optimal sub-blocks are chosen automatically according to their discriminant function distribution in current frame. Furthermore, we reduce blocks search costs with help of historical prediction accuracy. Experiments validate the robustness of our algorithm in tackling with small deformation and partial occlusion.
{"title":"Adaptive structured sub-blocks tracking","authors":"Liu Jing-Wen, Sun Wei-Ping, Xia Tao","doi":"10.1109/SPAC.2014.6982651","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982651","url":null,"abstract":"Local features have been widely used in visual object tracking for their robustness in illumination, deformation, rotation and partial occlusion. Traditional feature selection algorithms based on accumulated knowledge of previous frames usually adopt the perspective of continuity of changes, which could lead to degradation. Exploiting discrimination and uniqueness of local sub-blocks, we build an automatic preselection mechanism for local features and propose the structured sub-blocks tracking algorithm under particle filter framework. Optimal sub-blocks are chosen automatically according to their discriminant function distribution in current frame. Furthermore, we reduce blocks search costs with help of historical prediction accuracy. Experiments validate the robustness of our algorithm in tackling with small deformation and partial occlusion.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114475127","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982733
Daiming Zhang, Bin Fang, Weibin Yang, Xiaosong Luo, Yuanyan Tang
Vision-based road signs detection and recognition has been widely used in intelligent robotics and automotive autonomous driving technology. Currently, one-time calibration of inverse perspective mapping (IPM) parameters is employed to eliminate the effect of perspective mapping, but it is not robust to the uphill and downhill road. We propose an automatic inverse perspective mapping method based on vanishing point, which is adaptive to the uphill and downhill road even with slight rotation of the main road direction. The proposed algorithm is composed of the following three steps: detecting the vanishing point, calculating the pitch and yaw angles and adopting inverse perspective mapping to obtain the “bird's eye view” image. Experimental results show that the adaptability of our inverse perspective mapping framework is comparable to existing state-of-the-art methods, which is conducive to the subsequent detection and recognition of road signs.
{"title":"Robust inverse perspective mapping based on vanishing point","authors":"Daiming Zhang, Bin Fang, Weibin Yang, Xiaosong Luo, Yuanyan Tang","doi":"10.1109/SPAC.2014.6982733","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982733","url":null,"abstract":"Vision-based road signs detection and recognition has been widely used in intelligent robotics and automotive autonomous driving technology. Currently, one-time calibration of inverse perspective mapping (IPM) parameters is employed to eliminate the effect of perspective mapping, but it is not robust to the uphill and downhill road. We propose an automatic inverse perspective mapping method based on vanishing point, which is adaptive to the uphill and downhill road even with slight rotation of the main road direction. The proposed algorithm is composed of the following three steps: detecting the vanishing point, calculating the pitch and yaw angles and adopting inverse perspective mapping to obtain the “bird's eye view” image. Experimental results show that the adaptability of our inverse perspective mapping framework is comparable to existing state-of-the-art methods, which is conducive to the subsequent detection and recognition of road signs.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134643061","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982725
Shujian Yu, Xinge You, Kexin Zhao, Xiubao Jiang, Yi Mou, Jie Zhu
Spectrograms provide an effective way for time-frequency representation (TFR). Among these, short-time Fourier transform (STFT) based spectrograms are extensively used for various applications. However, STFT spectrogram and its revised versions suffer from two main issues: (1) there is a trade-off between time resolution and frequency resolution, and (2) almost all existing TFR methods, including STFT spectrogram, are not suitable to deal with nonuniformly sampled data. In this paper, we address these two problems by presenting alternative approaches, namely short-time amplitude and phase estimation (ST-APES) and short-time sparse learning via iterative minimization (ST-SLIM), to improve the resolution of STFT based spectrogram, and extend the applicability of our approaches to signals with arbitrary sampling patterns. Apart from evenly sampled data, we will consider missing data as well as arbitrary nonuniformly sampled data, at the same time. We will demonstrate via simulation results the superiority of our proposed algorithms in terms of resolution, sidelobe suppression and applicability to signals with arbitrary sampling patterns.
{"title":"STFT-like time frequency representations for nonstationary signal — From evenly sampled data to arbitrary nonuniformly sampled data","authors":"Shujian Yu, Xinge You, Kexin Zhao, Xiubao Jiang, Yi Mou, Jie Zhu","doi":"10.1109/SPAC.2014.6982725","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982725","url":null,"abstract":"Spectrograms provide an effective way for time-frequency representation (TFR). Among these, short-time Fourier transform (STFT) based spectrograms are extensively used for various applications. However, STFT spectrogram and its revised versions suffer from two main issues: (1) there is a trade-off between time resolution and frequency resolution, and (2) almost all existing TFR methods, including STFT spectrogram, are not suitable to deal with nonuniformly sampled data. In this paper, we address these two problems by presenting alternative approaches, namely short-time amplitude and phase estimation (ST-APES) and short-time sparse learning via iterative minimization (ST-SLIM), to improve the resolution of STFT based spectrogram, and extend the applicability of our approaches to signals with arbitrary sampling patterns. Apart from evenly sampled data, we will consider missing data as well as arbitrary nonuniformly sampled data, at the same time. We will demonstrate via simulation results the superiority of our proposed algorithms in terms of resolution, sidelobe suppression and applicability to signals with arbitrary sampling patterns.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132749659","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 : 2014-12-15DOI: 10.1109/SPAC.2014.6982678
Yingyi Liang, Zhenyu He, Yi Li
Scalable Video Coding (SVC), provides different resolutions, different video quality and different video streaming rate after once compression according to various requirements of users. The characteristic performance can solve a series of video transmission problems encountered in the current complex and heterogeneous network environment conveniently and effectively, and provide a highly efficient solution for the new video network. Because of problems such as the SVC coding efficiency in multilayer and the coding cost, the research on SVC is mainly focused on how to improve the coding speed of the algorithm (fast SVC algorithm). For the macroblock mode selection in H.264/SVC, the paper selects the fast algorithm based on the macroblock in derived layer.
可伸缩视频编码(Scalable Video Coding, SVC)，可根据用户的不同需求，一次压缩后提供不同的分辨率、不同的视频质量和不同的视频流率。该特性可以方便有效地解决当前复杂异构的网络环境中遇到的一系列视频传输问题，为新型视频网络提供高效的解决方案。由于多层SVC编码效率和编码成本等问题，对SVC的研究主要集中在如何提高算法的编码速度(快速SVC算法)。对于H.264/SVC中的宏块模式选择，本文选择了基于派生层宏块的快速算法。
{"title":"Fast mode selection algorithm based on derived layer","authors":"Yingyi Liang, Zhenyu He, Yi Li","doi":"10.1109/SPAC.2014.6982678","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982678","url":null,"abstract":"Scalable Video Coding (SVC), provides different resolutions, different video quality and different video streaming rate after once compression according to various requirements of users. The characteristic performance can solve a series of video transmission problems encountered in the current complex and heterogeneous network environment conveniently and effectively, and provide a highly efficient solution for the new video network. Because of problems such as the SVC coding efficiency in multilayer and the coding cost, the research on SVC is mainly focused on how to improve the coding speed of the algorithm (fast SVC algorithm). For the macroblock mode selection in H.264/SVC, the paper selects the fast algorithm based on the macroblock in derived layer.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589230","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}
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