2014 19th International Conference on Digital Signal Processing最新文献_第2页

Compression for the feature points with binary descriptors 用二进制描述符压缩特征点

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900746

Jian-Jiun Ding, Szu-Wei Fu, Ching-Wen Hsiao, Pin-Xuan Lee, Yen-Chun Chen

引用次数: 1

Topographical segmentation: A new tool to optimally define temporal region-of-interests of significant difference in ERPs 地形分割:一个新的工具，以最佳地定义在erp显著差异的时间利益区域

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900772

Li Hu, Jiasi Shen, Zhiguo Zhang

{"title":"Topographical segmentation: A new tool to optimally define temporal region-of-interests of significant difference in ERPs","authors":"Li Hu, Jiasi Shen, Zhiguo Zhang","doi":"10.1109/ICDSP.2014.6900772","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900772","url":null,"abstract":"The statistical identification of temporal region-of-interests (ROIs) of the significant difference in event-related potentials (ERPs) was popularly achieved using the cluster-based approach, in which the clustering was achieved based on the temporal adjacency of statistical significance if data from single-electrode were tested, or based on the spatial and temporal adjacency of statistical significance if data from multi-electrodes were tested. However, this cluster-based approach would be problematic if the significant differences were strong and sustained in time, but varied greatly in space. In other words, neural generators, which contributed to the detected significant differences, changed markedly within the explored temporal-cluster. To solve this problem, we implemented a statistical approach based on topographical segmentation analysis, which did not only make use of the temporal adjacency of significance, but also utilized the scalp distribution of statistical difference. We applied this technique to assess the significant difference of SEPs between deviant and standard conditions, and we observed that temporal ROIs, captured distinct spatial distributions of statistical difference, could be correctly identified using the topographical segmentation analysis be means of quasi-stable scalp distribution.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114712352","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

Probability distribution estimation of music signals in time and frequency domains 音乐信号时频域概率分布估计

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900696

Vaibhav Arora, Ravi Kumar

引用次数: 10

CDMA-FMT: A novel multiple access scheme for 5G wireless communications CDMA-FMT: 5G无线通信的一种新型多址方案

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900798

Zongjie Wang, Shuju Fan, Yun Rui

引用次数: 13

A face hallucination method via HR-LLE coefficients constraint 一种基于HR-LLE系数约束的人脸幻觉方法

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900816

Zhenli Wei, Xiaoguang Li, L. Zhuo

引用次数: 0

Real-time voice activity detection for ECoG-based speech brain machine interfaces 基于ecog的语音脑机接口实时语音活动检测

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900790

V. G. Kanas, I. Mporas, H. Benz, K. Sgarbas, Anastasios Bezerianos, N. Crone

引用次数: 16

An alternating ℓp — ℓ2 projections algorithm (ALPA) for speech modeling using sparsity constraints 基于稀疏性约束的交替投影算法(ALPA)

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900673

A. Adiga, C. Seelamantula

{"title":"An alternating ℓp — ℓ2 projections algorithm (ALPA) for speech modeling using sparsity constraints","authors":"A. Adiga, C. Seelamantula","doi":"10.1109/ICDSP.2014.6900673","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900673","url":null,"abstract":"We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating ℓp - ℓ2 projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an ℓp-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123289215","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

Projection based algorithm for detecting exudates in color fundus images 基于投影的彩色眼底图像渗出物检测算法

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900707

C. Eswaran, M. D. Saleh, J. Abdullah

{"title":"Projection based algorithm for detecting exudates in color fundus images","authors":"C. Eswaran, M. D. Saleh, J. Abdullah","doi":"10.1109/ICDSP.2014.6900707","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900707","url":null,"abstract":"The detection and analysis of spot lesions associated with the retinal diseases, such as exudates, microaneurysms, and hemorrhages, play an important role in the screening of retinal diseases. This paper presents an algorithm for segmentation of automated exudates from color fundus images. The proposed algorithm comprises two major stages, namely, pre-processing and segmentation. A novel pre-processing method is employed for background removal through contrast enhancement and noise removal. In the second stage, the pre-processed image is sliced horizontally and vertically into a number of slices and then the corresponding projection values are obtained in order to select an appropriate threshold value for each of the image slices. Finally, optic disc is removed to facilitate the correct identification of exudates and to decrease the false positive cases. DIARETDB1 database is used to measure the accuracy of the proposed method. Based on the experiments which are conducted on pixel basis, it is found that the proposed algorithm achieves better results compared to known algorithms. With the proposed algorithm, average values of 71.2%, 72.77%, 99.98%, 97.72%, 99.74%, and 83.28% are obtained in terms of overlap, sensitivity, specificity, PPV, accuracy, and kappa coefficient respectively.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122305655","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: 9

Audio surveillance under noisy conditions using time-frequency image feature 基于时频图像特征的噪声条件下音频监控

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900815

R. Sharan, T. Moir

{"title":"Audio surveillance under noisy conditions using time-frequency image feature","authors":"R. Sharan, T. Moir","doi":"10.1109/ICDSP.2014.6900815","DOIUrl":"https://doi.org/10.1109/ICDSP.2014.6900815","url":null,"abstract":"In this paper, we use the novel method of using features extracted from the time-frequency image representation of a sound signal in an audio surveillance application. In particular, we investigate two image representations: linear grayscale and log grayscale. We first divide a sound signal into smaller frames and apply a windowing function. The absolute value of the Discrete Fourier Transform of each frame is then computed and normalized to get the intensity values for the linear grayscale image. The generation of the log grayscale image takes a similar approach but we take log power of the values before data normalization. Each image is then divided into blocks and central moments are computed in each block. We carry out experimentation under different noise conditions and varying signal-to-noise ratio using support vector machines for classification. Based on the classification accuracy, the linear grayscale image approach is found to be more noise robust than the log grayscale image approach. It was also found to perform better than using mel-frequency cepstral coefficients as features which is a common baseline feature in most sound recognition applications.","PeriodicalId":301856,"journal":{"name":"2014 19th International Conference on Digital Signal Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126059098","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: 9

Hausdorff symmetry operator towards retinal blood vessel segmentation 视网膜血管分割的Hausdorff对称算子

2014 19th International Conference on Digital Signal Processing Pub Date : 2014-09-18 DOI: 10.1109/ICDSP.2014.6900737

Rashmi Panda, N. Puhan, G. Panda

引用次数: 9