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Multi-class class classification of unconstrained handwritten Arabic words using machine learning approaches 使用机器学习方法的无约束手写阿拉伯语单词的多类分类
The Open Signal Processing Journal Pub Date : 2009-09-14 DOI: 10.2174/1876825300902010021
J. AlKhateeb, Jinchang Ren, Jianmin Jiang, S. Ipson
{"title":"Multi-class class classification of unconstrained handwritten Arabic words using machine learning approaches","authors":"J. AlKhateeb, Jinchang Ren, Jianmin Jiang, S. Ipson","doi":"10.2174/1876825300902010021","DOIUrl":"https://doi.org/10.2174/1876825300902010021","url":null,"abstract":"In this paper, we propose and describe efficient multiclass classification and recognition of unconstrained handwritten Arabic words using machine learning approaches which include the K-nearest neighbor (K-NN) clustering, and the neural network (NN). The technical details are presented in terms of three stages, namely preprocessing, feature extraction and classification. Firstly, words are segmented from input scripts and also normalized in size. Secondly, from each of the segmented words various feature extraction methods are introduced. Finally, these features are utilized to train the K-NN and the NN classifiers for classification. In order to validate the proposed techniques, extensive experiments are conducted using the K-NN and the NN. The proposed algorithms are tested on the IFN/ENIT database which contains 32492 Arabic words; the proposed algorithms give good accuracy when compared with other methods.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129811535","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: 20
Object Recognition Using Wavelet Based Salient Points 基于小波显著点的目标识别
The Open Signal Processing Journal Pub Date : 2009-09-11 DOI: 10.2174/1876825300902010014
S. Arivazhagan, R. Shebiah
{"title":"Object Recognition Using Wavelet Based Salient Points","authors":"S. Arivazhagan, R. Shebiah","doi":"10.2174/1876825300902010014","DOIUrl":"https://doi.org/10.2174/1876825300902010014","url":null,"abstract":"In this paper, an efficient method to recognize various objects using wavelet based salient points with the help of Moment features is presented. In the detection of salient points, a salient point detector is presented that extract points where variations occur in the image, whether they are corner-like or not. The detector is based on wavelet transform with full level decomposition to detect global variations as well as local ones. This method provides better retrieval performance when compared with other point detectors. After detecting the salient points, patches are extracted over those points. The patches have the advantage of being robust with respect to occlusion and background clutter in images. Then the features are extracted using Basic Moments method for the detected patches in order to give them to a classifier. Support Vector Machines scale relatively well to high dimensional data. SVM classifier recognizes the objects (positive images) from the background (negative images) and vice-versa. The experimental evaluation of the proposed method is done using the well-known and complex Caltech database with complex images. The results obtained here proved that the proposed method is able to successfully recognize the objects with good recognition rate along with the background using wavelet based salient points with full level decomposition under challenging conditions.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130281823","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
FPGA-Based Image Processor for Sensor Nodes in a Sensor Network 基于fpga的传感器网络节点图像处理器
The Open Signal Processing Journal Pub Date : 2009-03-24 DOI: 10.2174/1876825300902010007
M. Yoshimura, H. Kawai, T. Iyota, Yongwoon Choi
{"title":"FPGA-Based Image Processor for Sensor Nodes in a Sensor Network","authors":"M. Yoshimura, H. Kawai, T. Iyota, Yongwoon Choi","doi":"10.2174/1876825300902010007","DOIUrl":"https://doi.org/10.2174/1876825300902010007","url":null,"abstract":"A field-programmable-gate-array- (FPGA-) based image processor which can be used for sensor nodes in a sensor network has been proposed and developed. Image processors for the nodes must satisfy requirements such as low power consumption, small circuitry scale, and modifiability of the hardware architecture. By developing an image proces- sor designed using an FPGA, SRAM modules, and the vector code correlation method which is suitable for the construc- tion of the target hardware architecture, it was possible to ensure that the processor satisfies these requirements. In this paper, we present the details of this image processor, which employs the template matching method for target tracking as well as the background subtraction method for object extraction. In addition, in order to verify its applicability in sensor nodes, we demonstrate the usefulness of the image processor from the results of an experiment in which the template matching and background subtraction methods were implemented simultaneously.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115705753","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: 11
EM-Based Optimal Maximal Ratio Diversity Combiner for Constant Envelope Signals 基于em的恒包络信号最优最大比分集组合
The Open Signal Processing Journal Pub Date : 2009-01-28 DOI: 10.2174/1876825300902010001
A. El-Mahdy
{"title":"EM-Based Optimal Maximal Ratio Diversity Combiner for Constant Envelope Signals","authors":"A. El-Mahdy","doi":"10.2174/1876825300902010001","DOIUrl":"https://doi.org/10.2174/1876825300902010001","url":null,"abstract":"An optimal maximal ratio combiner (MRC) based on the expectation-maximization (EM) algorithm is devel- oped for noisy constant envelope signals transmitted over a Rayleigh fading channel. Instead of using a transmitted pilot signal with the data to estimate the combiner gains, the EM algorithm is used to perform this estimation. In the developed MRC, estimation of the transmitted data sequence is performed also by the EM algorithm. Estimation using the EM algo- rithm provides an iterative solution to the maximum likelihood (ML) approach. Therefore, the resulting receiver is opti- mum and does not suffer from the difficulties resulted from direct application of the ML procedure. One of these difficul- ties is the computational complexity which depends exponentially on the data sequence length. Introducing an iterative structure in the developed MRC achieves a linear computational complexity and enables efficient data extraction by the Viterbi algorithm when trellis coding is used.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128782456","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
Spectral Analysis of Irregularly Sampled Data with Time Series Models 不规则采样数据的时间序列谱分析
The Open Signal Processing Journal Pub Date : 2009-01-02 DOI: 10.2174/1876825300801010007
P. Broersen
{"title":"Spectral Analysis of Irregularly Sampled Data with Time Series Models","authors":"P. Broersen","doi":"10.2174/1876825300801010007","DOIUrl":"https://doi.org/10.2174/1876825300801010007","url":null,"abstract":"Slotted resampling transforms an irregularly sampled process into an equidistant missing-data problem. Equidistant resampling inevitably causes bias, due to aliasing and the shift of the irregular observation times to an equidistant grid. Taking a slot width smaller than the resampling time can diminish the shift bias. A dedicated estimator for time series models of multiple slotted data sets with missing observations has been developed for the estimation of the power spectral density and of the autocorrelation function. The algorithm estimates time series models and selects the order and type from a number of candidates. It is tested with benchmark data. Spectra can be estimated until frequencies higher than 100 times the mean data rate.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132090350","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: 6
Wavelet Multi-Scale Transform Based Foreground Segmentation and Shadow Elimination 基于小波多尺度变换的前景分割与阴影消除
The Open Signal Processing Journal Pub Date : 2008-11-28 DOI: 10.2174/1876825300801010001
Ye-peng Guan
{"title":"Wavelet Multi-Scale Transform Based Foreground Segmentation and Shadow Elimination","authors":"Ye-peng Guan","doi":"10.2174/1876825300801010001","DOIUrl":"https://doi.org/10.2174/1876825300801010001","url":null,"abstract":"An algorithm using wavelet multi-scale transform for segmenting foreground moving objects and suppressing shadow is proposed. The optimal selection of threshold is automatically determined which does not require any complex supervised training, manual calibration or hypothesis. The proposed algorithm is efficient enough to segment foreground moving objects with low contrast against the background. The reference image is used to extract foreground no matter the objects enter the field of view before captured or not. The developed method is highly computationally cost-effective since it does not concern with complex computation model, color model or background statistics at a time. By compari- sons, it has been shown that the proposed approach is more robust and efficient to detect foreground and suppress shadow during coping with different indoors or outdoors circumstances.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123027364","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: 29
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