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

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Extraction of Smooth and Thin Ridgelines from Fingerprint Images Using Geometric Prediction 基于几何预测的指纹图像光滑和细脊线提取
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.81
S. Ghosh, Partha Bhowmick
{"title":"Extraction of Smooth and Thin Ridgelines from Fingerprint Images Using Geometric Prediction","authors":"S. Ghosh, Partha Bhowmick","doi":"10.1109/ICAPR.2009.81","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.81","url":null,"abstract":"Ridgelines play a vital role in a modern fingerprint identification system.However, due to inherent limitations in a fingerprint acquisition device,detection of ridgelines from an acquired image becomes a challenging task.Hence, several methodologies have been suggested over the years to detect ridgelines in a gray-scale fingerprint image, most of which produce thick ridgelines requiring subsequent thinning and smoothing in order to obtain the thinned ridge topography.Proposed in this paper is an efficient algorithm of extracting ridgelines from gray-scale fingerprint image using geometric prediction based on an efficient carry-forward mechanism of the knowledge acquired while traversing along the concerned ridgeline.Such a geometric tracing finally produces a ridge segment that is simultaneously smooth and thin, wherein lies the novelty of the algorithm.The algorithm is robust, efficient, and has minimal dependence on shareholding.On testing with benchmark databases, it is found to produce the desired output in terms of both runtime and quality.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"18 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":"128536730","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
On the Breakdown Point of the Hough Transform 关于霍夫变换的击穿点
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.56
Shreyas B. Guruprasad
{"title":"On the Breakdown Point of the Hough Transform","authors":"Shreyas B. Guruprasad","doi":"10.1109/ICAPR.2009.56","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.56","url":null,"abstract":"The degree of robustness of the Hough Transform is examined through its breakdown point by using a noise model called the adversarial noise model. The result thus obtained is compared with existing values in literature and differences are described. An interesting link between the Hough Transform technique and error correction codes is also shown.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"18 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":"124161571","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
Efficient Learning of Finite Mixture Densities Using Mutual Information 利用互信息有效学习有限混合密度
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.91
Padmini Jaikumar, Abhishek Singh, S. Mitra
{"title":"Efficient Learning of Finite Mixture Densities Using Mutual Information","authors":"Padmini Jaikumar, Abhishek Singh, S. Mitra","doi":"10.1109/ICAPR.2009.91","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.91","url":null,"abstract":"This paper presents a technique of determining the optimum number of components in a mixture model. A count of the number of local maxima in the density of the data is first used to obtain a rough guess of the actual number of components. Mutual Information criteria are then used to judge if components need to be added or removed in order to reach the optimum number. An incremental K-means algorithm is used to add components to the mixture model if required. An obvious advantage of the proposed method is in terms of computational time, as a good guess of the optimum number of components is quickly obtained. The technique has been successfully tested on a variety of univariate as well as bivariate simulated data and the Iris dataset.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"12 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":"130195881","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
Detecting and Tracking People in a Homogeneous Environment Using Skin Color Model 基于肤色模型的同质环境中人检测与跟踪
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.93
B. Yogameena, S. Roomi, S. Abhaikumar
{"title":"Detecting and Tracking People in a Homogeneous Environment Using Skin Color Model","authors":"B. Yogameena, S. Roomi, S. Abhaikumar","doi":"10.1109/ICAPR.2009.93","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.93","url":null,"abstract":"The task of correctly identifying and tracking people in a shadow environment for understanding the group dynamics is of paramount importance in many vision systems. This work presents a real time system for detecting and tracking people, in an environment where, people have similar attire. The proposed frame work contains shadow removal in HSV color space, detection through occlusion, person identification by developing skin color model and tracking by extracting image features. Experimental results illustrate that the proposed approach works robustly in homogeneous environment.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"3 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":"128959439","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
A Novel Face Recognition Method Using Facial Landmarks 一种基于人脸标记的人脸识别新方法
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.59
Srinivas Nagamalla, B. Dhara
{"title":"A Novel Face Recognition Method Using Facial Landmarks","authors":"Srinivas Nagamalla, B. Dhara","doi":"10.1109/ICAPR.2009.59","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.59","url":null,"abstract":"In this paper we have presented a novel approach for face recognition. The proposed method is based on the facial landmarks such as eyes, nose, lips etc. In this approach, first the probable position of these landmarks is located from the gradient image. Secondly, the template matching is employed over a region around the probable positions to detect exact location of the landmarks. Then, statistical and geometric features are extracted from these regions. To reduce the dimension of the feature vector PCA is employed. In this experiment to classify the images Mahalanobis distance is employed. The performance of the proposed method is tested on ORL database.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"407 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":"115920992","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: 7
A Skin-Color and Template Based Technique for Automatic Ear Detection 基于皮肤颜色和模板的耳朵自动检测技术
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.31
S. Prakash, U. Jayaraman, Phalguni Gupta
{"title":"A Skin-Color and Template Based Technique for Automatic Ear Detection","authors":"S. Prakash, U. Jayaraman, Phalguni Gupta","doi":"10.1109/ICAPR.2009.31","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.31","url":null,"abstract":"This paper proposes an efficient skin-color and template based technique for automatic ear detection in a side face image. The technique first separates skin regions from non skin regions and then searches for the ear within skin regions. Ear detection process involves three major steps. First, Skin Segmentation to eliminate all non-skin pixels from the image, second Ear Localization to perform ear detection using template matching approach, and third Ear Verification to validate the ear detection using the Zernike moments based shape descriptor. To handle the detection of ears of various shapes and sizes, an ear template is created considering the ears of various shapes (triangular, round, oval and rectangular) and resized automatically to a size suitable for the detection. Proposed technique is tested on the IIT Kanpur ear database consisting of 150 side face images and gives 94% accuracy.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"37 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":"132510113","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: 50
The Combination of Three Statistical Methods for Visual Inspection of Anomalies in Hyperspectral Imageries 三种统计方法的组合用于高光谱图像的目视异常检测
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.78
M. Alonso, J. Malpica
{"title":"The Combination of Three Statistical Methods for Visual Inspection of Anomalies in Hyperspectral Imageries","authors":"M. Alonso, J. Malpica","doi":"10.1109/ICAPR.2009.78","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.78","url":null,"abstract":"Outliers are important features that are of special interest to image analysts in their work. The objective of this paper is to show how several statistical techniques with different theoretical foundations can be successfully applied complementarily to detect anomalies in hyperspectral imageries. The methodology is shown in airborne hyperspectral imagery with 60 bands. The visual inspection of the last components of Principal Component Analysis (PCA), together with the analysis of the images provided by the Reed and Xiaoli Yu algorithm and projection pursuit algorithm, allows clear extraction of most of the anomalies, such as synthetic material of tennis court floors or metallic roofs of buildings. A discussion and comparison of the three methods is given.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"89 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":"132655949","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
Independent Component Analysis of Real Data 真实数据的独立成分分析
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.110
M. K. Nath
{"title":"Independent Component Analysis of Real Data","authors":"M. K. Nath","doi":"10.1109/ICAPR.2009.110","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.110","url":null,"abstract":"Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. The ICA algorithms are able to separate the sources according to the distribution of the data. The original Infomax algorithm for blind separation is better suited to estimation of super-Gaussian sources. FastICA can separate the sources having non-Gaussian distributions. Real data (e.g. functional magnetic resonance imaging (fMRI) data and speech signal obtained at cocktail party problem) is having Gaussian and non-Gaussian distributions. So existing ICA algorithms such as the Infomax, FastICA can not separate the independent components from the real data. For proper separation of independent components we have tried with different ICA algorithms. Recently developed Combi ICA can separate the independent components from real data faithfully. Because the Combi ICA can separate the sources having non-Gaussian and Gaussian distributions. In this paper, we find the independent components by a number of ICA algorithms from which Efficient FastICA and Combi ICA are found to be good because the accuracy in terms of the variance of the Gain matrix (Amari Performance Index) is more as compared to others. In our work we 1) used the kurtosis and negentropy to know the distribution of data 2) review the analysis methods for finding independent components from real data (specially fMRI data), 3) comparison of different ICA algorithms. The purpose of this work is to have an idea about the problems, challenges and methods about analysis of independent components from real data.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"152 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":"131615926","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
DA-IICT Cross-lingual and Multilingual Corpora for Speaker Recognition 基于DA-IICT的说话人识别跨语言和多语言语料库
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.72
H. Patil, Sunayana Sitaram, Esha Sharma
{"title":"DA-IICT Cross-lingual and Multilingual Corpora for Speaker Recognition","authors":"H. Patil, Sunayana Sitaram, Esha Sharma","doi":"10.1109/ICAPR.2009.72","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.72","url":null,"abstract":"In this paper the design and development of the DA-IICT Cross-lingual and Multilingual Speech Corpora is presented which includes unconventional sounds like cough, whistle, whisper, frication, idiosyncrasies, etc. from bilingual subjects (i.e., who can speak Hindi and Indian English) and trilingual subjects (who can speak Hindi, Indian English and mother tongue) for the development of Automatic Speaker Recognition System. Thirteen Indian languages and the Nepali language are considered as the subjects’ mother tongue/native languages. Unconventional sounds are considered to examine how much speaker-specific information they carry. Finally, an ASR system based on spectral or cepstral features (i.e., LPC, LPCC, MFCC) and polynomial classifier of 2nd order approximation is presented to evaluate the developed corpora.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"87 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":"128304323","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: 8
Driver Hypo-vigilance Detection Based on Eyelid Behavior 基于眼睑行为的驾驶员低警惕性检测
2009 Seventh International Conference on Advances in Pattern Recognition Pub Date : 2009-02-04 DOI: 10.1109/ICAPR.2009.108
M. Sigari
{"title":"Driver Hypo-vigilance Detection Based on Eyelid Behavior","authors":"M. Sigari","doi":"10.1109/ICAPR.2009.108","DOIUrl":"https://doi.org/10.1109/ICAPR.2009.108","url":null,"abstract":"Driver face monitoring system is a real-time system that can detect driver fatigue and driver distraction using machine vision approaches. In this paper, a new algorithm is proposed for driver hypo-vigilance detection based on eye-region processing and without explicit eye detection stage. In this method, horizontal projection of top half-segment of facial image is used to extract symptoms of fatigue and distraction. Percentage of eye closure (PERCLOS) and eyelid distance changes during time are used for fatigue detection; and eye closure rate is used for distraction detection. The novelty of our method is in adaptive feature extraction using spatio-temporal processing without explicit eye detection. Processing rate of proposed method is more than 5 frames per second.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"7 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114020253","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: 69
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