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2010 International Conference on Signal Acquisition and Processing最新文献

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Analysis of SPIHT Algorithm Using Tiling Operations 基于平铺运算的SPIHT算法分析
2010 International Conference on Signal Acquisition and Processing Pub Date : 2010-02-09 DOI: 10.1109/ICSAP.2010.34
G. Sadashivappa, M. Jayakar, K. A. Babu
{"title":"Analysis of SPIHT Algorithm Using Tiling Operations","authors":"G. Sadashivappa, M. Jayakar, K. A. Babu","doi":"10.1109/ICSAP.2010.34","DOIUrl":"https://doi.org/10.1109/ICSAP.2010.34","url":null,"abstract":"The aim of this paper is to study the performance of wavelet filters, using SPIHT algorithm with tiling operations for image compression. Tiling operations will be useful when images to be compressed are larger in size. Performance of different wavelets on image compression for different level of wavelet decomposition and for different tiling size is studied. Data redundancy is a fundamental issue in image compression. A lossy image compression (SPIHT with tiling) technique which provides a higher level of data reduction but result in a less than perfect reconstruction of original image is implemented here using MATLAB software. Two different resolution of Lena image are used for analysis. Image Quality is measured objectively using PSNR (peak signal to noise ratio) and execution time is verified with respect to the tiling size and level of wavelet decomposition.","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127877013","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: 4
A Novel Method for Information Prediction 一种新的信息预测方法
2010 International Conference on Signal Acquisition and Processing Pub Date : 2010-02-09 DOI: 10.1109/ICSAP.2010.29
Ting Zhang, Yi Du
{"title":"A Novel Method for Information Prediction","authors":"Ting Zhang, Yi Du","doi":"10.1109/ICSAP.2010.29","DOIUrl":"https://doi.org/10.1109/ICSAP.2010.29","url":null,"abstract":"Many interpolation methods were proposed to predict or reconstruct unknown information. However, when the conditional data are quite few or even there are no conditional data, predicted results are often poor. Originally, a method called multiple-point geostatistics (MPS) originated from geostatistical fields and it allows extracting multiple-point structures from training images, after that MPS can copy these structures to the regions to be simulated. However, original MPS can only predict discretized variables. To overcome the disadvantage, a method using continuous MPS based on filters is proposed to predict the unknown information composed of continuous variables. Filters are used to realize dimension reduction, and a filter score space can be created using filters. All similar training patterns fall into a cell in the filter score space to create a prototype. During prediction, a training pattern from a cell is randomly drawn, and then is pasted back onto the simulation grid. Experimental results show that our method can effectively predict the unknown information of a region.","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084444","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
Rule Based Part of Speech Tagging of Sindhi Language 基于规则的信德语词性标注
2010 International Conference on Signal Acquisition and Processing Pub Date : 2010-02-09 DOI: 10.1109/ICSAP.2010.27
J. Mahar, G. Q. Memon
{"title":"Rule Based Part of Speech Tagging of Sindhi Language","authors":"J. Mahar, G. Q. Memon","doi":"10.1109/ICSAP.2010.27","DOIUrl":"https://doi.org/10.1109/ICSAP.2010.27","url":null,"abstract":"Part of Speech (POS) tagging is a process of assigning correct syntactic categories to each word in the text. Tag set and word disambiguation rules are fundamental parts of any POS tagger. No work has hitherto been published of tag set in Sindhi language. The Sindhi lexicon for computational processing is also not available. In this study, the tag set for Sindhi POS, lexicon and word disambiguation rules are designed and developed. The Sindhi corpus is collected from a comprehensive Sindhi Dictionary. The corpus is based on the most recent available vocabulary used by local people. In this paper, preliminary achievements of rule based Sindhi Part of Speech (SPOS) tagger are presented. Tagging and tokenization algorithms are also designed for the implementation of SPOS. The outputs of SPOS are verified by Sindhi linguist. The development of SPOS tagger may have an important milestone towards computational Sindhi language processing.","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"21 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133111612","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: 25
A Comparison of Kekre's Fast Search and Exhaustive Search for Various Grid Sizes Used for Colouring a Greyscale Image Kekre快速搜索和穷举搜索在灰度图像着色中不同网格大小的比较
2010 International Conference on Signal Acquisition and Processing Pub Date : 2010-02-01 DOI: 10.1109/ICSAP.2010.48
H. B. Kekre, Sudeep D. Thepade, Adib Parkar
{"title":"A Comparison of Kekre's Fast Search and Exhaustive Search for Various Grid Sizes Used for Colouring a Greyscale Image","authors":"H. B. Kekre, Sudeep D. Thepade, Adib Parkar","doi":"10.1109/ICSAP.2010.48","DOIUrl":"https://doi.org/10.1109/ICSAP.2010.48","url":null,"abstract":"There are various techniques using various colour spaces that can be used to colour a greyscale image such as those described in [1], [2], [3] and [4]. In this paper we focus on the procedure described in [4] extended to seven different colour spaces – RGB, LUV, YCgCb, YCbCr, YUV, XYZ and YIQ. We also vary the grid (pixel window) sizes used in the aforementioned procedure and apply both the exhaustive search (ES) algorithm as well as Kekre’s Fast Search (KFS) algorithm to find the mapping with the least mean squared error and compare the results obtained across grid sizes.","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127197071","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: 21
A High Resolution Algorithm for Nonplanar Array with Arbitrary Geometry without Source Number Estimation 一种无源数估计的任意几何非平面阵列高分辨率算法
2010 International Conference on Signal Acquisition and Processing Pub Date : 2010-02-01 DOI: 10.1109/ICSAP.2010.25
H. Ke, Z. Xiaomin, Han Peng, Zhao Yan-an, Yu Yang
{"title":"A High Resolution Algorithm for Nonplanar Array with Arbitrary Geometry without Source Number Estimation","authors":"H. Ke, Z. Xiaomin, Han Peng, Zhao Yan-an, Yu Yang","doi":"10.1109/ICSAP.2010.25","DOIUrl":"https://doi.org/10.1109/ICSAP.2010.25","url":null,"abstract":"The performances of most of the high resolution methods always depend on the estimation of the source number. In real application, when the estimated number of signals is not correct, the orthogonality between signal subspace and noise subspace can not be maintained any more. And the performance of DOA estimation algorithm will deteriorate severely. In this paper, a high resolution algorithm called m-MVM without source number estimation and Eigen decomposition for Direction-Of-Arrival (DOA) estimation is proposed, which is an improvement of the Minimum Variance Method (MVM). Furthermore, it is suitable to nonplanar arrays of arbitrary geometries. Some representative computer simulations are presented to illustrate the performance comparison between different algorithms and different arrays.","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127899149","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
G-Filter's Gaussianization Function for Interference Background 干扰背景下G-Filter的高斯化函数
2010 International Conference on Signal Acquisition and Processing Pub Date : 2009-12-31 DOI: 10.1109/WCSP.2009.5371444
Wang Pingbo, Liu Feng, C. Zhiming, Tang Suofu
{"title":"G-Filter's Gaussianization Function for Interference Background","authors":"Wang Pingbo, Liu Feng, C. Zhiming, Tang Suofu","doi":"10.1109/WCSP.2009.5371444","DOIUrl":"https://doi.org/10.1109/WCSP.2009.5371444","url":null,"abstract":"By weakening the bigger and strengthening the smaller, gaussianization can enhance the gaussianity of samples and improve performance of subsequent correlation test. Firstly, an explicit definition on gaussianizing filter and a practical method to evaluate the filtering performance are given. Secondly, based on the cumulative distribution function and its inverse, one typical gaussianizing filters, so-called G-filter, are proposed and studied. Finally, instances with lake trial data are illustrated.","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123095313","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
Face Recognition Using Principle Component Analysis, Eigenface and Neural Network 基于主成分分析、特征脸和神经网络的人脸识别
2010 International Conference on Signal Acquisition and Processing Pub Date : 2009-11-07 DOI: 10.1109/ICSAP.2010.51
Mayank Agarwal, H. Agrawal, Nikunj Jain, Manish Kumar
{"title":"Face Recognition Using Principle Component Analysis, Eigenface and Neural Network","authors":"Mayank Agarwal, H. Agrawal, Nikunj Jain, Manish Kumar","doi":"10.1109/ICSAP.2010.51","DOIUrl":"https://doi.org/10.1109/ICSAP.2010.51","url":null,"abstract":"Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages – Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database. Test results gave a recognition rate of 97.018%","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117215089","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: 95
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