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2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)最新文献

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Applying integrated nested laplace approximation to the superresolution problem 应用积分嵌套拉普拉斯近似求解超分辨率问题
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151568
M. O. Camponez, E. Salles, Mário Sarcinelli Filho
{"title":"Applying integrated nested laplace approximation to the superresolution problem","authors":"M. O. Camponez, E. Salles, Mário Sarcinelli Filho","doi":"10.1109/ISSPIT.2011.6151568","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151568","url":null,"abstract":"Superresolution is a term used to describe the generation of a high-resolution image from a sequence of similar low-resolution images. In 2011 we derived a closed form to resolve the superresolution problem, thus proposing a new algorithm to generate the high-resolution image. However, the choice of an hyperparameter (λ), involved in the fusion of the low-resolution images, is still heuristically defined. Thus, to get a good value for such hyperparameter is somewhat troublesome, demanding much experience or a lot of attempts. In this context, this paper proposes a fully automatic method for choosing such hyperparameter, thus providing a fully analytical solution for the superresolution problem. In the solution it is used, by the first time in the image processing field, a new Bayesian inference method known as Integrated Nested Laplace Approximation (INLA). Several simulations, from which two results are here presented, show that the proposed algorithm performs better than other superresolution algorithms yet available in the literature.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122829283","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
Mammography analysis using a soft perceptual segmentation approach 乳房x线摄影分析使用软感知分割方法
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151592
D. Cherifi, Azeddine Beghdadi, P. V. D. Lesegno, A. H. Belbachir
{"title":"Mammography analysis using a soft perceptual segmentation approach","authors":"D. Cherifi, Azeddine Beghdadi, P. V. D. Lesegno, A. H. Belbachir","doi":"10.1109/ISSPIT.2011.6151592","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151592","url":null,"abstract":"A new mammography image segmentation method based on a perceptual and fuzzy approach is proposed. The main idea is to exploit some properties of the Human Visual System namely the directional and frequency selectivity and the fuzzy sets theory in order to segment the mammography images into meaningful components. A contrast enhancement is used in the case of suspicious cases identified during the segmentation process. The performance of the proposed method is objectively evaluated using the just-noticeable difference measure expressed through a saliency map of the mammography image before and after processing. The obtained results confirm the efficiency of the method in segmenting the images into meaningful regions.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"27 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125155527","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
Mining rare cases in post-operative pain by means of outlier detection 用异常值检测方法挖掘手术后疼痛的罕见病例
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151532
Mobyen Uddin Ahmed, P. Funk
{"title":"Mining rare cases in post-operative pain by means of outlier detection","authors":"Mobyen Uddin Ahmed, P. Funk","doi":"10.1109/ISSPIT.2011.6151532","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151532","url":null,"abstract":"Rare cases are often interesting for health professionals, physicians, researchers and clinicians in order to reuse and disseminate experiences in healthcare. However, mining, i.e. identification of rare cases in electronic patient records, is non-trivial for information technology. This paper investigates a number of well-known clustering algorithms and finally applies a 2nd order clustering approach by combining the Fuzzy C-means algorithm with the Hierarchical one. The approach was used to identify rare cases from 1572 patient cases in the domain of post-operative pain treatment. The results show that the approach enables the identification of rare cases in the domain of post-operative pain treatment and 18% of cases were identified as rare.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116994018","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
Aerial image classification using structural texture similarity 基于结构纹理相似性的航空图像分类
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151558
V. Risojevic, Z. Babic
{"title":"Aerial image classification using structural texture similarity","authors":"V. Risojevic, Z. Babic","doi":"10.1109/ISSPIT.2011.6151558","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151558","url":null,"abstract":"There is an increasing need for algorithms for automatic analysis of remote sensing images and in this paper we address the problem of semantic classification of aerial images. For the task at hand we propose and evaluate local structural texture descriptor and similarity measure. Nearest neighbor classifier based on the proposed descriptor and similarity measure, as well as image-to-class similarity, improves classification rates over the state-of-the-art on two datasets of aerial images. We evaluate the design choices and show that rich subband statistics, perceptually-based structural texture similarity measure and image-to-class similarity all contribute to the good performance of our classifier.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115173723","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: 38
A new hypothesis testing based technique for the simultaneous detection of seismic events 基于假设检验的地震事件同步检测新技术
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151604
Erion-Vasilis M. Pikoulis, E. Psarakis
{"title":"A new hypothesis testing based technique for the simultaneous detection of seismic events","authors":"Erion-Vasilis M. Pikoulis, E. Psarakis","doi":"10.1109/ISSPIT.2011.6151604","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151604","url":null,"abstract":"One of the most difficult tasks in the solution of a hypothesis testing problem is the estimation of the probability density functions of the null and the alternative hypothesis. In this paper, the simultaneous detection of seismic events contained in a given data record is formulated as such a problem and an approximation of the probability density function under the null hypothesis, of a ratio based test statistic is proposed. By exploiting some interesting properties satisfied by the ratio of identical distributed Random Variables, as well as the sparsity of the seismic events in the data record, we succeed in obtaining such an approximation. From a series of experiments we have conducted in both synthetic and real seismic data, the effectiveness of the proposed technique is confirmed.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155869","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
Target classification based on sensor fusion in multi-channel seismic network 基于传感器融合的多通道地震台网目标分类
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151602
M. Zubair, K. Hartmann
{"title":"Target classification based on sensor fusion in multi-channel seismic network","authors":"M. Zubair, K. Hartmann","doi":"10.1109/ISSPIT.2011.6151602","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151602","url":null,"abstract":"Target classification plays a vital role for outdoor security applications. The main focus of this paper is to describe a strategy to classify a target in a multi-channel seismic network. A technique of sensor level fusion is applied in a seismic network. This technique is based on correlation method. The method determines the weights of each seismic sensor present in the network. These weights are then adjusted adaptively as the change of correlation is observed among the sensors for real-time data. The self-clustering of the sensors is then evaluated based on the Euclidean distance measure of these weighted values in a network. This technique is not only helpful to reduce the computational cost of the network since the features of a target is extracted only from a fused signal but also to identify the failure state of the sensor. The shape statistics and peak values in a frequency domain are extracted as the features of the target. Principal component analysis is used to optimize the feature vectors. Then, the AdaBoost classifier is applied on these feature vectors for target classification.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143318","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
Performance of unknown and arbitrary sparse signal detection using convex programming method with compressive measurements 基于压缩测量的凸规划方法的未知和任意稀疏信号检测性能
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151591
Chuan Lei, Jun Zhang, Q. Gao
{"title":"Performance of unknown and arbitrary sparse signal detection using convex programming method with compressive measurements","authors":"Chuan Lei, Jun Zhang, Q. Gao","doi":"10.1109/ISSPIT.2011.6151591","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151591","url":null,"abstract":"We consider the detection of arbitrary and unknown sparse signals against background noise. Under a Neyman-Pearson framework, a new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is proposed and analyzed. The error probability of LRT-SE is characterized with respect to the signal-to-noise ratio (SNR) and the estimation error under the high SNR regime. For the low SNR regime, it is shown that there exists a detection boundary on the SNR, above which Chernoff-consistent detection is achievable for LRT-SE. The detection boundary can be calculated using fidelity results on the sparse estimation, and it allows the signal to be consistently detected under vanishing SNR. The error exponent of LRT-SE is also characterized and compared with the oracle exponent assuming signal knowledge. Numerical experiments are used to shown that the proposed method performs in the vicinity of the LRT method and the error probability decays exponentially with the number of observations. Results in this paper also have important implications in showing how well the performance of sparse estimation technique transforms into a hypothesis testing setup.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128938619","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
Color image encryption through a novel chess based confusion scheme using chaotic map 一种基于混沌映射的基于象棋的彩色图像加密方法
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151603
Rohit Joshi, Sumit Joshi
{"title":"Color image encryption through a novel chess based confusion scheme using chaotic map","authors":"Rohit Joshi, Sumit Joshi","doi":"10.1109/ISSPIT.2011.6151603","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151603","url":null,"abstract":"A novel color image encryption method is proposed which uses Henon map for generating pseudo random numbers from original pixel values. Furthermore, these values are shuffled and confused using chess based confusion scheme based on logistic map. Security analysis of the encryption scheme is shown governing very high key space, very high key sensitivity, and ability to sustain statistical and differential attacks.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126825958","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: 3
Statistical analysis of parkinson disease gait classification using Artificial Neural Network 基于人工神经网络的帕金森病步态分类统计分析
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151536
H. H. Manap, N. Tahir, A. Yassin
{"title":"Statistical analysis of parkinson disease gait classification using Artificial Neural Network","authors":"H. H. Manap, N. Tahir, A. Yassin","doi":"10.1109/ISSPIT.2011.6151536","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151536","url":null,"abstract":"The aim of this study is to investigate the parameters that could be used to identify abnormal gait pattern in Parkinson's disease subjects during normal walking. Hence, three types of gait parameters namely basic, kinematic and kinetic are evaluated. Initial findings showed that the average mean of cadence, step length and walking speed for Parkinson's disease patients are lower than normal subjects, while the mean of stride time for Parkinson's disease patients are higher. Further, for kinematic parameter, overall joint angle of hip, knee and ankle mean values are lower for Parkinson's disease patients as compared to normal group. In addition, for kinetic parameter, all mean values of ground reaction force parameters are higher for normal subjects with walking speed contributed as the major determinant. To evaluate the significant features that could be used as identification between PD and normal subjects, statistical analysis is conducted. Hence, based on the statistical analysis results, it was found that step length, walking speed, knee angle as well as vertical parameter of ground reaction force are the four significant features as indicators for classification of subject with Parkinson's disease based on the accuracy attained with Artificial Neural Network as classifier.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"34 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988278","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: 63
Motion estimation in real deformation processes based on block-matching techniques 基于块匹配技术的真实变形过程中的运动估计
2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Pub Date : 2011-12-14 DOI: 10.1109/ISSPIT.2011.6151584
Alvaro Rodríguez, C. Fernandez-Lozano, José Antonio Seoane Fernández, J. Rabuñal, J. Dorado
{"title":"Motion estimation in real deformation processes based on block-matching techniques","authors":"Alvaro Rodríguez, C. Fernandez-Lozano, José Antonio Seoane Fernández, J. Rabuñal, J. Dorado","doi":"10.1109/ISSPIT.2011.6151584","DOIUrl":"https://doi.org/10.1109/ISSPIT.2011.6151584","url":null,"abstract":"Non rigid motion estimation is one of the main issues in several fields, ranging from medical image analysis to civil engineering.","PeriodicalId":288042,"journal":{"name":"2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130899046","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
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