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18th International Conference on Pattern Recognition (ICPR'06)最新文献

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Shadow Detection by Integrating Multiple Features 基于多特征集成的阴影检测
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.1047
K. Lo, Mau-Tsuen Yang
{"title":"Shadow Detection by Integrating Multiple Features","authors":"K. Lo, Mau-Tsuen Yang","doi":"10.1109/ICPR.2006.1047","DOIUrl":"https://doi.org/10.1109/ICPR.2006.1047","url":null,"abstract":"Cast shadows of moving foreground objects in a scene often result in problems for many applications such as surveillance, object tracking/recognition, video content analysis and intelligent transportation systems. In this paper we presented an algorithm exploiting information of color, shading, texture, neighborhoods and temporal consistency to detect shadows in a scene efficiently and reliably. The experimental results showed that the proposed method can detect umbra as well as penumbra in different kinds of scenarios under various illumination conditions","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091901","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
Observation-Switching Linear Dynamic Systems for Tracking Humans Through Unexpected Partial Occlusions by Scene Objects 通过场景对象的意外局部遮挡跟踪人类的观察切换线性动态系统
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.888
Patrick Peursum, S. Venkatesh, G. West
{"title":"Observation-Switching Linear Dynamic Systems for Tracking Humans Through Unexpected Partial Occlusions by Scene Objects","authors":"Patrick Peursum, S. Venkatesh, G. West","doi":"10.1109/ICPR.2006.888","DOIUrl":"https://doi.org/10.1109/ICPR.2006.888","url":null,"abstract":"This paper focuses on the problem of tracking people through occlusions by scene objects. Rather than relying on models of the scene to predict when occlusions will occur as other researchers have done, this paper proposes a linear dynamic system that switches between two alternatives of the position measurement in order to handle occlusions as they occur. The filter automatically switches between a foot-based measure of position (assuming z = 0) to a head-based position measure (given the person's height) when an occlusion of the person's lower body occurs. No knowledge of the scene or its occluding objects is used. Unlike similar research (Fleuret et al., 2005; Zhao and Nevatia, 2004), the approach does not assume a fixed height for people and so is able to track humans through occlusions even when they change height during the occlusion. The approach is evaluated on three furnished scenes containing tables, chairs, desks and partitions. Occlusions range from occlusions of legs, occlusions whilst being seated and near-total occlusions where only the person's head is visible. Results show that the approach provides a significant reduction in false-positive tracks in a multi-camera environment, and more than halves the number of lost tracks in single monocular camera views","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129363772","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: 12
A Peer Dataset Comparison Outlier Detection Model Applied to Financial Surveillance 一种应用于金融监控的同行数据比较离群点检测模型
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.150
Tang Jun
{"title":"A Peer Dataset Comparison Outlier Detection Model Applied to Financial Surveillance","authors":"Tang Jun","doi":"10.1109/ICPR.2006.150","DOIUrl":"https://doi.org/10.1109/ICPR.2006.150","url":null,"abstract":"Outlier detection is a key element for intelligent financial surveillance system. The detection procedures generally fall into two categories: comparing every transaction against its account history and further more, comparing against a peer group to determine if the behavior is unusual. The later approach shows particular merits in efficiently extracting suspicious transaction and reducing false positive rate. Peer group analysis concept is largely dependent on a cross-datasets outlier detection model. In this paper, we propose a new cross outlier detection model based on distance definition incorporated with the financial transaction data features. An approximation algorithm accompanied with the model is provided to optimize the computation of the deviation from tested data point to the reference dataset. An experiment based on real bank data blended with synthetic outlier cases shows promising results of our model in reducing false positive rate while enhancing the discriminative rate remarkably","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124643967","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
Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis 基于内容的支持向量机和独立分量分析音频分类
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.407
Jia-Ching Wang, Jhing-Fa Wang, Cai-Bei Lin, Kun-Ting Jian, Wai-He Kuok
{"title":"Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis","authors":"Jia-Ching Wang, Jhing-Fa Wang, Cai-Bei Lin, Kun-Ting Jian, Wai-He Kuok","doi":"10.1109/ICPR.2006.407","DOIUrl":"https://doi.org/10.1109/ICPR.2006.407","url":null,"abstract":"In this paper, we present a new audio classification system. First, a frame-based multiclass support vector machine (SVM) for audio classification is proposed. The accuracy rate has significant improvements over conventional file-based SVM audio classifier. In feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ICA). This new audio feature is combined with mel-frequency cepstral coefficients (MFCCs) and five perceptual features to form an audio feature set. The superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743878","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
Finding Gait in Space and Time 在空间和时间中寻找步态
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.562
Yang Ran, R. Chellappa, Q. Zheng
{"title":"Finding Gait in Space and Time","authors":"Yang Ran, R. Chellappa, Q. Zheng","doi":"10.1109/ICPR.2006.562","DOIUrl":"https://doi.org/10.1109/ICPR.2006.562","url":null,"abstract":"We describe an approach to characterize the signatures generated by walking humans in spatio-temporal domain. To describe the computational model for this periodic pattern, we take the mathematical theory of geometry group theory, which is widely used in crystallographic structure research. Both empirical and theoretical analyses prove that spatio-temporal helical patterns generated by legs belong to the Frieze Groups because they can be characterized by a repetitive motif along the direction of walking. The theory is applied to an automatic detection-and-tracking system capable of counting heads and handling occlusion by recognizing such patterns. Experimental results for videos acquired from both static and moving ground sensors are presented. Our algorithm demonstrates robustness to non-rigid human deformation as well as background clutter","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124761298","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: 19
Regression Nearest Neighbor in Face Recognition 人脸识别中的最近邻回归
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.989
Shu Yang, Chao Zhang
{"title":"Regression Nearest Neighbor in Face Recognition","authors":"Shu Yang, Chao Zhang","doi":"10.1109/ICPR.2006.989","DOIUrl":"https://doi.org/10.1109/ICPR.2006.989","url":null,"abstract":"In this paper, we introduce a regression nearest neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129471653","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
Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection 基于积分图像的旋转局部不变量特征计算用于实时目标检测
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.399
M. Villamizar, A. Sanfeliu, J. Andrade-Cetto
{"title":"Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection","authors":"M. Villamizar, A. Sanfeliu, J. Andrade-Cetto","doi":"10.1109/ICPR.2006.399","DOIUrl":"https://doi.org/10.1109/ICPR.2006.399","url":null,"abstract":"We present a framework for object detection that is invariant to object translation, scale, rotation, and to some degree, occlusion, achieving high detection rates, at 14 fps in color images and at 30 fps in gray scale images. Our approach is based on boosting over a set of simple local features. In contrast to previous approaches, and to efficiently cope with orientation changes, we propose the use of non-Gaussian steerable filters, together with a new orientation integral image for a speedy computation of local orientation","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129641798","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: 22
Image Categorization Using Local Probabilistic Descriptors 基于局部概率描述符的图像分类
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.680
K. Mele, J. Maver, D. Suc
{"title":"Image Categorization Using Local Probabilistic Descriptors","authors":"K. Mele, J. Maver, D. Suc","doi":"10.1109/ICPR.2006.680","DOIUrl":"https://doi.org/10.1109/ICPR.2006.680","url":null,"abstract":"Image categorization involves the well known difficulties with different visual appearances of a single object, but introduces also the problem of within-category variation. This within-category variation makes highly distinctive local descriptors less appropriate for categorization. In this paper we propose a family of local image descriptors, called probabilistic patch descriptors (PPDs). PPDs encode the appearance of image fragments as well as their variability within a category. PPDs extend the usual local descriptors by modelling also the variance of the descriptors' elements, e.g. pixels or bins in a histogram. We apply PPDs to image categorization by using machine learning where the features are the matching scores between images and PPDs. We experiment with two variants of PPDs that are based on complementary local descriptors. An interesting observation is that combining the two PPD variants improves categorization accuracy. Experiments indicate benefits of modelling the within-category variation and show good robustness with respect to noise","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129647485","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
An Efficient Text Capture Method for Moving Robots Using DCT Feature and Text Tracking 基于DCT特征和文本跟踪的移动机器人文本捕获方法
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.243
Hiroki Shiratori, Hideaki Goto, Hiroaki Kobayashi
{"title":"An Efficient Text Capture Method for Moving Robots Using DCT Feature and Text Tracking","authors":"Hiroki Shiratori, Hideaki Goto, Hiroaki Kobayashi","doi":"10.1109/ICPR.2006.243","DOIUrl":"https://doi.org/10.1109/ICPR.2006.243","url":null,"abstract":"When a moving robot tries to find text in the surrounding scene by an onboard video camera, the same text strings appear in many image frames. Since it is a waste of time to recognize the same text strings repeatedly it is necessary to decrease text candidate regions for recognition. This paper presents a text capture system that can look around the environment by an active camera, reducing the number of text strings to be recognized. The text candidate regions are extracted from the images by an improved DCT feature. The text regions are tracked in a video sequence to reduce the text candidate strings. In experiments, we tested 55 images of corridor with seven text strings. The text candidate regions are reduced by 86.8% by our method","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129892599","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: 22
Function Dot Product Kernels for Support Vector Machine 支持向量机的函数点积核
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.586
Guangyi Chen, P. Bhattacharya
{"title":"Function Dot Product Kernels for Support Vector Machine","authors":"Guangyi Chen, P. Bhattacharya","doi":"10.1109/ICPR.2006.586","DOIUrl":"https://doi.org/10.1109/ICPR.2006.586","url":null,"abstract":"A new family of kernels for support vector machine is proposed by taking the dot product of two function vectors. These kernels are proved to be admissible support vector kernels, and the dot product function in the kernels can be selected as the polynomial, the Gaussian radial basis function, the exponential radial basis function, the wavelet function, the autocorrelation wavelet function, the probability function, etc. Experiments show the feasibility of the proposed kernels for pattern recognition. The dual-tree complex wavelet is used to extract invariant features for recognizing similar handwritten numerals, and the recognition rate is about 99.50% for a training data set of 800 samples and a testing data set of 400 samples. It is also possible to apply the proposed kernels to function regression","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126888766","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
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