2009 Seventh International Conference on Advances in Pattern Recognition最新文献

Integration of Co-expression Networks for Gene Clustering 基因聚类的共表达网络整合

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.55

M. Bhattacharyya, S. Bandyopadhyay

引用次数: 5

Large Lump Detection Using a Particle Filter of Hybrid State Variable 基于混合状态变量粒子滤波的大块检测

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.52

Zhijie Wang, Hong Zhang

引用次数: 3

Empirical Evaluation of Character Classification Schemes 字符分类方案的实证评价

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.41

N. V. Neeba, C. V. Jawahar

引用次数: 18

Rough Set Based Ensemble Prediction for Topic Specific Web Crawling 基于粗糙集的特定主题网络抓取集成预测

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.17

S. Saha, C. A. Murthy, S. Pal

{"title":"Rough Set Based Ensemble Prediction for Topic Specific Web Crawling","authors":"S. Saha, C. A. Murthy, S. Pal","doi":"10.1109/ICAPR.2009.17","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.17","url":null,"abstract":"The rapid growth of the world wide web had made the problem of useful resource discovery an important one in recent years. Several techniques such as focused crawling and intelligent crawling have recently been proposed for topic specific resource discovery. All these crawlers use the hypertext features behavior in order to perform topic specific resource discovery. A focused crawler uses the relevance score of the crawled page to score the unvisited URLs extracted from it. The scored URLs are then added to the frontier. Then it picks up the best URL to crawl next.Focused crawlers rely on different types of features of the crawled pages to keep the crawling scope within the desired domain and they are obtained from URL, anchor text, link structure and text contents of the parent and ancestor pages.Different focused crawling algorithms use these different set of features to predict the relevance and quality of the unvisited Web pages. In this article a combined method based on Rough Set Theory has been proposed. It combines the available predictions using decision rules and can build much larger domain-specific collections with less noise. Our experiment in this regard has provided better Harvest rate and better Target recall for focused crawling.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129701449","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

Shape Recognition Using a New Spatial Representation and a D.P. Matching Algorithm 一种新的空间表示和D.P.匹配算法的形状识别

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.107

Shiyuan Gu, S. Kundu

引用次数: 1

Relative Orientations of Geometric Centroids for Off-line Signature Verification 离线签名验证中几何质心的相对方向

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.28

H. Prakash, D. S. Guru

引用次数: 15

Optimal Control Approach to Robust Control of Nonlinear Systems Using Neural Network Based HJB solution 基于HJB解的非线性系统鲁棒控制的最优控制方法

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1504/IJAAC.2009.025238

D. Adhyaru, I. Kar, M. Gopal

引用次数: 5

Bangla Speech Recognition System Using LPC and ANN 基于LPC和ANN的孟加拉语语音识别系统

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.80

Anupama Paul, Dipankar Das, M. Kamal

{"title":"Bangla Speech Recognition System Using LPC and ANN","authors":"Anupama Paul, Dipankar Das, M. Kamal","doi":"10.1109/ICAPR.2009.80","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.80","url":null,"abstract":"This paper presents the Bangla speech recognition system. Bangla speech recognition system is divided mainly into two major parts. The first part is speech signal processing and the second part is speech pattern recognition technique. The speech processing stage consists of speech starting and end point detection, windowing, filtering, calculating the Linear Predictive Coding(LPC) and Cepstral Coefficients and finally constructing the codebook by vector quantization. The second part consists of pattern recognition system using Artificial Neural Network(ANN). Speech signals are recorded using an audio wave recorder in the normal room environment. The recorded speech signal is passed through the speech starting and end-point detection algorithm to detect the presence of the speech signal and remove the silence and pauses portions of the signals. The resulting signal is then filtered for the removal of unwanted background noise from the speech signals. The filtered signal is then windowed ensuring half frame overlap. After windowing, the speech signal is then subjected to calculate the LPC coefficient and Cepstral coefficient. The feature extractor uses a standard LPC Cepstrum coder, which converts the incoming speech signal into LPC Cepstrum feature space. The Self Organizing Map(SOM) Neural Network makes each variable length LPC trajectory of an isolated word into a fixed length LPC trajectory and thereby making the fixed length feature vector, to be fed into to the recognizer. The structures of the neural network is designed with Multi Layer Perceptron approach and tested with 3, 4, 5 hidden layers using the Transfer functions of Tanh Sigmoid for the Bangla speech recognition system. Comparison among different structures of Neural Networks conducted here for a better understanding of the problem and its possible solutions.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133176597","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: 91

Sparse Intensity Histogram: Distinctive and Robust to the Space-distortion 稀疏强度直方图:对空间失真具有显著性和鲁棒性

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.16

Hyeokjune Jeon, Jaekyong Jeong, Joonwoon Bang, Chijung Hwang

引用次数: 0

Multimodal Medical Image Fusion Using Redundant Discrete Wavelet Transform 基于冗余离散小波变换的多模态医学图像融合

2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.97

Richa Singh, Mayank Vatsa, A. Noore

引用次数: 90