发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Image Representation and Retrieval Using Support Vector Machine and Fuzzy C-means Clustering Based Semantical Spaces 基于语义空间的模糊c均值聚类和支持向量机的图像表示与检索
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.687
P. Bhattacharya, Md. Mahmudur Rahman, B. Desai
{"title":"Image Representation and Retrieval Using Support Vector Machine and Fuzzy C-means Clustering Based Semantical Spaces","authors":"P. Bhattacharya, Md. Mahmudur Rahman, B. Desai","doi":"10.1109/ICPR.2006.687","DOIUrl":"https://doi.org/10.1109/ICPR.2006.687","url":null,"abstract":"This paper presents a learning based framework for content-based image retrieval to bridge the gap between low-level image features and high-level semantic information presented in the images on semantically organized collections. Both supervised (probabilistic multi-class support vector machine) and unsupervised (fuzzy c-means clustering) learning based techniques are investigated to associate global MPEG-7 based color and edge features with their high-level semantical and/or visual categories. It represents images in a successive semantic level of information abstraction based on confidence or membership scores obtained from the learning algorithms. A fusion-based similarity matching function is employed on these new image representations to rank and retrieve most similar images compared to a query image. Experimental results on a generic image database with manually assigned semantic categories and on a medical image database with different modalities and examined body parts demonstrate the effectiveness of the proposed approach compared to the commonly used Euclidean distance measure on MPEG-7 based descriptors","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"27 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":"121704826","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: 17
Face Representation By Using Non-tensor Product Wavelets 基于非张量积小波的人脸表示
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.534
Xinge You, Dan Zhang, Qiuhui Chen, P. Wang, Y. Tang
{"title":"Face Representation By Using Non-tensor Product Wavelets","authors":"Xinge You, Dan Zhang, Qiuhui Chen, P. Wang, Y. Tang","doi":"10.1109/ICPR.2006.534","DOIUrl":"https://doi.org/10.1109/ICPR.2006.534","url":null,"abstract":"This paper presents a new approach to represent face by using non-tensor product bivariate wavelet filters. A new non-tensor product bivariate wavelet filter banks with linear phase are constructed from the centrally symmetric matrices. Our investigations demonstrate that these filter banks have a matrix factorization and they are capable of representing facial features for recognition. The implementations of our algorithm are made of three parts: First, face images are represented by the lowest resolution sub-bands after 2-level new non-tensor product wavelet decomposition. Second, the principal component analysis (PCA) feature selection scheme is adopted to reduce the computational complexity of feature representation. Finally, support vector machines (SVM) is applied for classification. The experimental results show that our method is superior to other methods in terms of recognition accuracy and efficiency","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"326 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113998169","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: 33
Using Specularities to Recover Multiple Light Sources in the Presence of Texture 在纹理存在的情况下，使用镜面恢复多个光源
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.1156
Pascal Lagger, P. Fua
{"title":"Using Specularities to Recover Multiple Light Sources in the Presence of Texture","authors":"Pascal Lagger, P. Fua","doi":"10.1109/ICPR.2006.1156","DOIUrl":"https://doi.org/10.1109/ICPR.2006.1156","url":null,"abstract":"Recovering multiple point light sources from a sparse set of photographs in which objects of unknown texture can move is challenging. This is because both diffuse and specular reflections appear to slide across surfaces. What is seldom demonstrated, however, is that it can be taken advantage of to address the light-source recovery problem. In this paper, we therefore show that, if 3D models of the moving objects are available or can be computed from the images, we can solve the problem without any a priori constraints on the number of sources, on their color, or on the surface albedos. Our approach involves finding local maxima in individual images, checking them for consistency across images, retaining the apparently specular ones, and having them vote in a Hough-like scheme for potential light source directions. The precise directions of the sources and their relative power are then obtained by optimizing a standard lighting model. As a byproduct we also obtain an estimate of various material parameters such as the unlighted texture and specular properties. We show that the resulting algorithm can operate in presence of arbitrary textures and an unknown number of light sources of possibly different unknown colors. We also estimate its accuracy using ground-truth data","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"14 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":"124335045","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
Joint Optimization of Image Registration and Comparametric Exposure Compensation Based on the Lucas-Kanade Algorithm 基于Lucas-Kanade算法的图像配准与比较参数曝光补偿联合优化
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.735
Dong Sik Kim, Su Yeon Lee, Kiryung Lee
{"title":"Joint Optimization of Image Registration and Comparametric Exposure Compensation Based on the Lucas-Kanade Algorithm","authors":"Dong Sik Kim, Su Yeon Lee, Kiryung Lee","doi":"10.1109/ICPR.2006.735","DOIUrl":"https://doi.org/10.1109/ICPR.2006.735","url":null,"abstract":"An iterative registration algorithm, the Lucas-Kanade algorithm, is combined with an exposure compensation algorithm to jointly optimize the spatial registration and the exposure compensation. The coordinate descent method is employed to minimize a mean squared error between image pairs. Based on a simple regression model, a non-parametric estimator, the empirical conditional mean and its polynomial fitting are used as histogram transformation functions for the exposure compensation. The proposed algorithm performs a good registration for real perspective and microscopic images, and can easily adopt other exposure compensation approaches and variations of the Lucas-Kanade algorithms due to its implicit flexibility","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"4 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":"124083103","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 Hybrid Method of Unsupervised Feature Selection Based on Ranking 一种基于排序的混合无监督特征选择方法
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.84
Yun Li, Bao-Liang Lu, Zhong-fu Wu
{"title":"A Hybrid Method of Unsupervised Feature Selection Based on Ranking","authors":"Yun Li, Bao-Liang Lu, Zhong-fu Wu","doi":"10.1109/ICPR.2006.84","DOIUrl":"https://doi.org/10.1109/ICPR.2006.84","url":null,"abstract":"Feature selection is a key problem to pattern recognition. So far, most methods of feature selection focus on sample data where class information is available. For sample data without class labels, however, the related methods for feature selection are few. This paper proposes a new way of unsupervised feature selection. Our method is a hybrid approach based on ranking the features according to their relevance to clustering using a new ranking index which belongs to exponential entropy. Firstly a candidate feature subset is selected using a modified fuzzy feature evaluation index (FFEI) with a new method to calculate the feature weight, which makes the algorithm to be robust and independent of domain knowledge. Then a wrapper method is used to select compact feature subset from the candidate feature set based on the clustering performance. Experimental results on benchmark data sets indicate the effectiveness of the proposed method","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"25 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":"127881588","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: 26
Real-Time 3D Articulated Pose Tracking using Particle Filters Interacting through Belief Propagation 通过信念传播相互作用的粒子滤波器实时三维关节姿态跟踪
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.957
O. Bernier
{"title":"Real-Time 3D Articulated Pose Tracking using Particle Filters Interacting through Belief Propagation","authors":"O. Bernier","doi":"10.1109/ICPR.2006.957","DOIUrl":"https://doi.org/10.1109/ICPR.2006.957","url":null,"abstract":"This article proposes a new statistical model for fast 3D articulated body tracking, similar to the loose-limbed model, but where inter-frame coherence is taken into account by using the previous marginal probability of each limb as prior information. Belief propagation is used to estimate the current marginal for each limb. All probability distribution are represented as sums of weighted samples. The resulting algorithm corresponds to a set of particle filters, one for each limb, where the weight of each sample, after the standard evaluation, is recalculated by taking into account the interactions between limbs. Applied to upper-body tracking in disparity and color images, the resulting algorithm estimates the body pose in quasi real-time (12Hz)","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"118 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":"126227649","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
Recognizing Rotated Faces from Two Orthogonal Views in Mugshot Databases 疑犯照片数据库中两个正交视图的旋转人脸识别
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.978
Xiaozheng Zhang, Yongsheng Gao, Bailing Zhang
{"title":"Recognizing Rotated Faces from Two Orthogonal Views in Mugshot Databases","authors":"Xiaozheng Zhang, Yongsheng Gao, Bailing Zhang","doi":"10.1109/ICPR.2006.978","DOIUrl":"https://doi.org/10.1109/ICPR.2006.978","url":null,"abstract":"Tolerance to pose variations is one of the key remaining problems in face recognition. It is of great interest in airport surveillance systems using mugshot databases to screen travellers' faces. This paper presents a novel pose-invariant face recognition approach using two orthogonal face images from mugshot databases. Virtual views under different poses are generated in two steps: shape modeling and texture synthesis. In the shape modeling step, a feature-based multilevel quadratic variation minimization approach is applied to generate smooth 3D face shapes. In the texture synthesis step, a non-Lambertian reflectance model is explored to synthesize facial textures taking into account both diffuse and specular reflections. A view-based face recognizer is used to examine the feasibility and effectiveness of the proposed pose-invariant face recognition. The experimental results show that the proposed method provides a new solution to the problem of recognizing rotated faces","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"83 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":"126399244","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: 10
Gesture Segmentation from a Video Sequence Using Greedy Similarity Measure 基于贪婪相似度度量的视频序列手势分割
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.608
Qiulei Dong, Yihong Wu, Zhanyi Hu
{"title":"Gesture Segmentation from a Video Sequence Using Greedy Similarity Measure","authors":"Qiulei Dong, Yihong Wu, Zhanyi Hu","doi":"10.1109/ICPR.2006.608","DOIUrl":"https://doi.org/10.1109/ICPR.2006.608","url":null,"abstract":"We propose a novel method of greedy similarity measure to segment long spatial-temporal video sequences. Firstly, a principal curve of motion region along frames of a video sequence is constructed to represent trajectory. Then from the constructed principal curves of trajectories of predefined gestures, HMMs are applied to modeling them. For a long input video sequence, greedy similarity measure is established to automatically segment it into gestures along with gesture recognition, where true breakpoints of its principal curve are found by maximizing the joint probability of two successive candidate segments conditioned on the gesture models obtained from HMMs. The method is flexible, of high accuracy, and robust to noise due to the exploitation of principal curves, the combination of two successive candidate segments, and the simultaneous recognition. Experiments including comparison with two established methods demonstrate the effectiveness of the proposed method","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"43 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":"126413731","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
Multimodal Range Image Segmentation by Curve Grouping 基于曲线分组的多模态距离图像分割
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.838
M. Haindl, Pavel Zid
{"title":"Multimodal Range Image Segmentation by Curve Grouping","authors":"M. Haindl, Pavel Zid","doi":"10.1109/ICPR.2006.838","DOIUrl":"https://doi.org/10.1109/ICPR.2006.838","url":null,"abstract":"A fast range image segmentation method for scenes comprising general faced objects is introduced. The range segmentation is based on a recursive adaptive probabilistic detection of step discontinuities which are present at object face borders in mutually registered range and intensity data. Detected face outlines guides the subsequent region growing step where the neighbouring face curves are grouped together. Region growing based on curve segments instead of pixels like in the classical approaches considerably speed up the algorithm. The exploitation of multimodal data significantly improves the segmentation quality","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"28 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":"126421103","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
Adaptive Binarization of Historical Document Images 历史文献图像的自适应二值化
18th International Conference on Pattern Recognition (ICPR'06) Pub Date : 2006-08-20 DOI: 10.1109/ICPR.2006.216
E. Kavallieratou, E. Stamatatos
{"title":"Adaptive Binarization of Historical Document Images","authors":"E. Kavallieratou, E. Stamatatos","doi":"10.1109/ICPR.2006.216","DOIUrl":"https://doi.org/10.1109/ICPR.2006.216","url":null,"abstract":"In this paper, we present a binarization technique specifically designed for historical document images. Existing methods for this problem focus on either finding a good global threshold or adapting the threshold for each area to remove smear, strains, uneven illumination etc. We propose a hybrid approach that first applies a global thresholding method and, then, identifies the image areas that are more likely to still contain noise. Each of these areas is re-processed separately to achieve better quality of binarization. We evaluate the proposed approach for different kinds of degradation problems. The results show that our method can handle hard cases while documents already in good condition are not affected drastically","PeriodicalId":236033,"journal":{"name":"18th International Conference on Pattern Recognition (ICPR'06)","volume":"29 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":"128046465","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: 49
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信