2010 International Conference of Soft Computing and Pattern Recognition最新文献_第7页

3-D object recognition based on SVM and stereo-vision: Application in endoscopic imaging 基于支持向量机和立体视觉的三维目标识别:在内镜成像中的应用

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686096

J. Ayoub, B. Granado, O. Romain, Y. Mohanna

引用次数: 2

Automatic titling of electronic documents with noun phrase extraction 自动标题电子文档与名词短语提取

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686088

C. Lopez, V. Prince, M. Roche

引用次数: 10

Improved SOM search algorithm for high-dimensional data with application to face recognition across pose and illumination 改进的高维数据SOM搜索算法及其在姿态和光照下人脸识别中的应用

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686078

A. Sagheer

引用次数: 5

A novel speech enhancement approach based on Singular Value Decomposition and Genetic Algorithm 一种基于奇异值分解和遗传算法的语音增强方法

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686627

A. Zehtabian, H. Hassanpour, Shahrokh Zehtabian, V. Zarzoso

{"title":"A novel speech enhancement approach based on Singular Value Decomposition and Genetic Algorithm","authors":"A. Zehtabian, H. Hassanpour, Shahrokh Zehtabian, V. Zarzoso","doi":"10.1109/SOCPAR.2010.5686627","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686627","url":null,"abstract":"The Singular Value Decomposition (SVD) is a powerful tool used for subspace division. In this paper a novel approach for speech signal enhancement is presented which is based on SVD and Genetic Algorithm (GA). The method is derived from the effects of environmental noises on the singular vectors as well as the singular values of a clean speech. This article reviews the existing approaches for subspace estimation and proposes novel techniques for effectively enhancing the singular values and vectors of a noisy speech. The proposed approach clearly results in a considerable attenuation of the noise as well as retrieving the quality of the original speech. The efficiency of our proposed method is affected by a number of crucial parameters which are optimally set by utilizing the GA. Extensive sets of experiments have been carried out for both of additive white Gaussian noise as well as different types of realistic colored noise cases. The results of applying six superior speech enhancement methods are then evaluated by the objective (SNR) and subjective (PESQ) measures.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124693510","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: 13

Iris recognition based on multi-block Gabor statistical features encoding 基于多块Gabor统计特征编码的虹膜识别

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686412

Nadia Feddaoui, K. Hamrouni

{"title":"Iris recognition based on multi-block Gabor statistical features encoding","authors":"Nadia Feddaoui, K. Hamrouni","doi":"10.1109/SOCPAR.2010.5686412","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686412","url":null,"abstract":"Iris recognition has been recently given greater attention in human identification and it's becoming increasingly an active topic in research. This paper presents a personal identification method based on iris. The Method includes three steps. In the first one, the eye image is processed in order to obtain a segmented and normalized eye image by applying an integrodifferential operator, Hough transform and polar transformation. In the second step, the texture of iris image is analyzed by Gabor filters. Then, we have proposed a novel encoding method based on extracting invariant from local region of the iris to create an iris code of 144 bytes. We have studied different statistical descriptors of filtered image. We have calculated the modified Hamming distance between templates to find out the similarity between irises. The method is tested on the Casia v3 database. The experimental results illustrate the effectiveness of this coding in two modes of biometric iris: 100% of rank-one recognition rate and 1.97% of equal error rate in verification. Therefore the coding process is presented to achieve more satisfactory and convincing results than performed by traditional statistical based approaches and low storage requirements as an interesting alternative to Gabor phase coding.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991505","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

Iris features extraction using dual-tree complex wavelet transform 基于双树复小波变换的虹膜特征提取

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685843

Waheeda Al-Mayyan, H. Own, H. Zedan

引用次数: 4

Optimization of a fuzzy decision trees forest with artificial ant based clustering 基于人工蚂蚁聚类的模糊决策树森林优化

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686103

Nicolas Labroche, C. Marsala

引用次数: 1

Low resolution face recognition using combination of diverse classifiers 多分类器组合的低分辨率人脸识别

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686495

R. Ebrahimpour, Naser Sadeghnejad, A. Amiri, Abolfazl Moshtagh

引用次数: 10

FPGA based real time fuzzy fault detection algorithm 基于FPGA的实时模糊故障检测算法

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686535

I. Aydin, Mehmet Karaköse, E. Akin

引用次数: 3

Crowd behavior detection by statistical modeling of motion patterns 基于运动模式统计建模的人群行为检测

2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686403

S. S. Pathan, A. Al-Hamadi, B. Michaelis

{"title":"Crowd behavior detection by statistical modeling of motion patterns","authors":"S. S. Pathan, A. Al-Hamadi, B. Michaelis","doi":"10.1109/SOCPAR.2010.5686403","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686403","url":null,"abstract":"The governing behaviors of individuals in crowded places offer unique and difficult challenges, and limit the scope of conventional surveillance systems. In this paper, we investigate the crowd behaviors and localize the anomalies due to individual's abrupt dissipation. The novelty of the proposed approach can be described in three aspects. First, we introduce block-clips by sectioning the video segments into non-overlapping spatio-temporal patches to marginalize the arbitrarily complicated and dense flow field. Second, we treat the flow field in each block-clip as 2d distribution of samples and mixtures of Gaussian is used to parameterize it keeping generality of flow field intact. K-means algorithm is employed to initialize the mixture model and is followed by Expectation Maximization for optimization. These mixtures of Gaussian result in the distinct flow patterns precisely a sequence of dynamic patterns for each block-clip. Third, a bank of Conditional Random Field model is employed one for each block clip and is learned from the sequence of dynamic patterns and classifies each block-clip as normal and abnormal. We conduct experiment on two challenging benchmark crowd datasets PETS 2009 and University of Minnesota and results show that our method achieves higher recognition rates in detecting specific and overall crowd behaviors. In addition, the proposed approach shows dominating performance during the comparative analysis with similar approaches in crowd behavior detection.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130396602","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: 18