2009 IEEE 17th Signal Processing and Communications Applications Conference最新文献_第7页

Expression, pose and occlusion resistant 3D facial landmarking 抗表情、姿态和遮挡的3D面部标记

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136436

H. Dibeklioğlu, A. A. Salah, L. Akarun

引用次数: 0

Using wireless sensor network technologies for elder and child care: An application architecture proposal 在老年人和儿童护理中使用无线传感器网络技术:一个应用架构建议

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136496

H. Alemdar, Cem Ersoy

引用次数: 2

Traffic analysis of avenues and intersections based on video surveillance from fixed video cameras 基于固定摄像机视频监控的道路和十字路口交通分析

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136529

Saameh G. Ebrahimi, Nima SeifNaraghi, E. Ince

引用次数: 7

Complex signal transmission using real orthogonal space-time block codes with feedback 利用带反馈的实正交空时分组码传输复信号

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136538

S. Sahin, M. Çelebi, U. Aygolu

引用次数: 0

Steganalysis of the MELP quantization index modulation data hiding methods by using chaotic type features 利用混沌型特征对MELP量化指标调制数据隐藏方法进行隐写分析

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136334

A. Yargiçoglu, H. Ilk

引用次数: 0

A comparative study on polygonal mesh simplification algorithms 多边形网格简化算法的比较研究

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136501

Murat Yirci, ilkay Ulusoy

引用次数: 9

Mathematical analysis of colon glands for cancer diagnosis 结肠腺癌诊断的数学分析

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136462

Celal Cigir, C. Sokmensuer, C. Gunduz-Demir

引用次数: 0

Investigation of different dimension reduction and normalization methods for local appearance-based face recognition 基于局部外观的人脸识别中不同降维归一化方法的研究

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136425

Berkay Topp, Hakan Erdogan

{"title":"Investigation of different dimension reduction and normalization methods for local appearance-based face recognition","authors":"Berkay Topp, Hakan Erdogan","doi":"10.1109/SIU.2009.5136425","DOIUrl":"https://doi.org/10.1109/SIU.2009.5136425","url":null,"abstract":"Local appearance-based methods have been proposed recently for face recognition. We analyze the effects of different dimension reduction and normalization methods on local appearance-based face recognition in this paper. Each image is divided into equal sized blocks and six different dimension reduction methods are implemented for each block separately to create local visual feature vectors. On these local features, several normalization methods are applied in an attempt to eliminate the changes in lighting conditions and contrast differences among blocks of different face images. The experimental results show the improvements in recognition rates due to the effects of dimension reduction and normalization for three different classifiers. Usage of trainable dimension reduction methods instead of DCT and a new normalization method in our work (within-block normalization as referred in this paper) are two factors that makes difference from previous works in literature. The best performance is achieved using a block size of 16×16, performing dimension reduction using approximate pairwise accuracy criterion (aPAC) and applying within-block mean and variance normalization.","PeriodicalId":219938,"journal":{"name":"2009 IEEE 17th Signal Processing and Communications Applications Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127635350","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

Phonem-based isolated Turkish word recognition with subspace classifier 基于音素的土耳其孤立词子空间分类器识别

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136340

Serkan Keser, R. Edizkan

引用次数: 3

Classification of wheeze in respiratory sounds by linear discriminant method 用线性判别法对呼吸音喘音进行分类

2009 IEEE 17th Signal Processing and Communications Applications Conference Pub Date : 2009-04-09 DOI: 10.1109/SIU.2009.5136434

Sergul Aydore, I. Sen, M. K. Mihçak, Y. Kahya

{"title":"Classification of wheeze in respiratory sounds by linear discriminant method","authors":"Sergul Aydore, I. Sen, M. K. Mihçak, Y. Kahya","doi":"10.1109/SIU.2009.5136434","DOIUrl":"https://doi.org/10.1109/SIU.2009.5136434","url":null,"abstract":"The aim of this study is classification of wheeze and non-wheeze using some selected features and detection of wheeze in respiratory sound signals acquired from patients with asthma and COPD. Taking into consideration that wheeze, having a sinusoidal waveform, has a different behavior in time-frequency domain from that of non-wheeze signals, features are chosen as kurtosis, Renyi entrophy, f50/f90 ratio and zero-crossing irregularity. Upon calculation of these features for each wheeze and non-wheeze portion, the whole data scattered as two classes in four dimensional feature space is projected using Fisher Discriminant Analysis (FDA) onto the single dimensional space that separates the two classes best. Observing that the two classes are visually well separated in this new space, Neyman-Pearson hypothesis testing is applied. Finally, the success rate has been found to be %94.9 for the training set, and leave-one-out approach together with the above methodology yields a success rate of %93.5 for the test set. Ending up with these results for both training and test sets, one can conclude that selected features and methodology are meaningful.","PeriodicalId":219938,"journal":{"name":"2009 IEEE 17th Signal Processing and Communications Applications Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116340140","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