Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第5页

The research and practice of medical image 3D reconstruction platform 医学图像三维重建平台的研究与实践

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982704

Yanqiu Chen, Peili Sun

引用次数: 5

Reading numbers in natural scene images with convolutional neural networks 用卷积神经网络读取自然场景图像中的数字

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982655

Qiang Guo, Jun Lei, D. Tu, Guohui Li

{"title":"Reading numbers in natural scene images with convolutional neural networks","authors":"Qiang Guo, Jun Lei, D. Tu, Guohui Li","doi":"10.1109/SPAC.2014.6982655","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982655","url":null,"abstract":"Reading text from natural images is a hard computer vision task. We present a method for applying deep convolutional neural networks to recognize numbers in natural scene images. In this paper, we proposed a noval method to eliminating the need of explicit segmentation when deal with multi-digit number recognition in natural scene images. Convolution Neural Network(CNN) requires fixed dimensional input while number images contain unknown amount of digits. Our method integrats CNN with probabilistic graphical model to deal with the problem. We use hidden Markov model(HMM) to model the image and use CNN to model digits appearance. This method combines the advantages of both the two models and make them fit to the problem. By using this method we can perform the training and recognition procedure both at word level. There is no explicit segmentation operation at all which save lots of labour for sophisticated segmentation algorithm design or finegrained character labeling. Experiments show that deep CNN can dramaticly improve the performance compared with using Gaussian Mixture model as the digit model. We obtaied competitive results on the street view house number(SVHN) dataset.","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":"130514552","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: 5

Identifying user behavior on Twitter based on multi-scale entropy 基于多尺度熵的Twitter用户行为识别

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982720

Suiyuan He, Hui Wang, Zhihong Jiang

{"title":"Identifying user behavior on Twitter based on multi-scale entropy","authors":"Suiyuan He, Hui Wang, Zhihong Jiang","doi":"10.1109/SPAC.2014.6982720","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982720","url":null,"abstract":"Twitter as an online social network is used for many reasons, including information dissemination, marketing, political organizing, spamming, promotion, conversations and so on. Characterizing these activities and categorizing users is a challenging task. Traditional user classification models are based on individual user's profile information such as age, location, register time, interests and tweets, which have not considered the whole complexity of posting behavior. In this paper we introduce Multi-scale Entropy for analyzing and identifying user behavior on Twitter, and separate users to different categories. We have identified five distinct categories of tweeting activity on Twitter: individual activity, newsworthy information dissemination activity, advertising and promotion activity, automatic/robotic activity and other activities. Through the experiment we achieved good separation of different activities of these five categories based on Multi-scale Entropy of users' posting time series. The method based on Multi-scale Entropy is computationally efficient; it has many applications, including automatic spam-detection, trend identification, trust management, user-modeling in online social media.","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":"134022733","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: 15

Performance evaluation of Abrupt motion trackers 突发运动跟踪器的性能评价

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982650

Xucheng Li, Fasheng Wang, Mingyu Lu, Yaohua Xiong, Wei Sun

引用次数: 2

Touch-sensitive interactive projection system 触摸感应交互式投影系统

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982729

Ming He, Jun Cheng, Dapeng Tao

引用次数: 1

Learning large number of local statistical models via variational Bayesian inference for brain voxel classification in magnetic resonance images 利用变分贝叶斯推理学习大量局部统计模型，用于磁共振图像脑体素分类

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982657

Yong Xia, Yanning Zhang

{"title":"Learning large number of local statistical models via variational Bayesian inference for brain voxel classification in magnetic resonance images","authors":"Yong Xia, Yanning Zhang","doi":"10.1109/SPAC.2014.6982657","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982657","url":null,"abstract":"As an essential step in brain studies, measuring the distribution of major brain tissues, including gray matter, white matter and cerebrospinal fluid (CSF), using magnetic resonance imaging (MRI) has attracted extensive research efforts over the past years. Many brain tissue differentiation methods resulted from these efforts are based on the finite statistical mixture model, which however, in spite of its computational efficiency, is not strictly followed due to the intrinsically limited quality of MRI data and may lead to less accurate results. In this paper, a novel large-scale variational Bayesian inference (LS-VBI) learning algorithm is proposed for automated brain MRI voxels classification. To cope with the complexity and dynamic nature of MRI data, this algorithm uses a large number of local statistical models, in each of which all statistical parameters are assumed to be random variables sampled from conjugate prior distributions. Those models are learned using variational Bayesian inference and combined to predict the class label of each brain voxel. This algorithm has been evaluated against several state-of-the-art brain tissue segmentation methods on both synthetic and clinical brain MRI data sets. Our results show that the proposed algorithm can classify brain voxels more effectively and provide more precise distribution of major brain tissues.","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":"117070712","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

Dollar bill denomination recognition algorithm based on local texture feature 基于局部纹理特征的美元面额识别算法

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982697

Xinge You, Qingjiang Hu, Duanquan Xu, Xian-cheng Fu, Qixin Sun

引用次数: 0

Multi-label learning with co-training based on semi-supervised regression 基于半监督回归的协同训练多标签学习

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982681

Meixiang Xu, Fuming Sun, Xiaojun Jiang

引用次数: 6

Adaptive intra prediction filtering (AIPF) 自适应内预测滤波(AIPF)

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982715

Yuanfeng He, Qijun Wang, Xinge You, Duanquan Xu

{"title":"Adaptive intra prediction filtering (AIPF)","authors":"Yuanfeng He, Qijun Wang, Xinge You, Duanquan Xu","doi":"10.1109/SPAC.2014.6982715","DOIUrl":"https://doi.org/10.1109/SPAC.2014.6982715","url":null,"abstract":"Intra prediction is an important coding tool to exploit correlation within one picture in image and video compression. Before the ultimate intra prediction values are generated for current block along oblique angles, a fixed low-pass filtering with 3-tap filter (1, 2, 1) will be applied to the three prediction pixel values to avoid the effect of pulse noise. In this paper, we use adaptive intra prediction filter (AIPF) to replace the fixed filter to minimize the prediction errors. To get the adaptive filter coefficients in an on-line way with an acceptable accuracy and no coding overhead, we combine it with template matching (TM). After the best estimation of current block through template matching, the optimal adaptive filter coefficients are calculated with least-square optimization through considering the best estimation as `current' block. The adaptive filter is used to obtain intra prediction values instead of the 3-tap fixed low-pass filter. Experimental results show that the AIPF can get a stable coding gain on all test sequences, and reduce the bit-rate by up to 1.74% comparing with that using only TM.","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":"133769769","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

Multi-scale sparse denoising model based on non-separable wavelet 基于不可分小波的多尺度稀疏去噪模型

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2014-12-15 DOI: 10.1109/SPAC.2014.6982710

W. Zeng, Long Zhou, Renhong Xu, Biao Li

引用次数: 1