2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第6页

Automatic estimation of left ventricular dysfunction from echocardiogram videos 从超声心动图视频自动估计左心室功能障碍

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204054

D. Beymer, T. Syeda-Mahmood, A. Amir, Fei Wang, Scott Adelman

引用次数: 15

A case for the average-half-face in 2D and 3D for face recognition 二维和三维平均半脸的人脸识别案例

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204304

Josh Harguess, J. Aggarwal

{"title":"A case for the average-half-face in 2D and 3D for face recognition","authors":"Josh Harguess, J. Aggarwal","doi":"10.1109/CVPRW.2009.5204304","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204304","url":null,"abstract":"We observe that the human face is inherently symmetric and we would like to exploit this symmetry in face recognition. The average-half-face has been previously shown to do just that for a set of 3D faces when using eigenfaces for recognition. We build upon that work and present a comparison of the use of the average-half-face to the use of the original full face with 6 different algorithms applied to two- and three-dimensional (2D and 3D) databases. The average-half-face is constructed from the full frontal face image in two steps; first the face image is centered and divided in half and then the two halves are averaged together (reversing the columns of one of the halves). The resulting average-half-face is then used as the input for face recognition algorithms. Previous work has shown that the accuracy of 3D face recognition using eigenfaces with the average-half-face is significantly better than using the full face. We compare the results using the average-half-face and the full face using six face recognition methods; eigenfaces, multi-linear principal components analysis (MPCA), MPCA with linear discriminant analysis (MPCALDA), Fisherfaces (LDA), independent component analysis (ICA), and support vector machines (SVM). We utilize two well-known 2D face database as well as a 3D face database for the comparison. Our results show that in most cases it is superior to employ the average-half-face for frontal face recognition. The consequences of this discovery may result in substantial savings in storage and computation time.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122087964","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: 60

Remote audio/video acquisition for human signature detection 用于人体签名检测的远程音频/视频采集

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204294

Yufu Qu, Tao Wang, Zhigang Zhu

引用次数: 11

Improving face recognition with a quality-based probabilistic framework 基于质量的概率框架改进人脸识别

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204299

N. Ozay, Yan Tong, F. Wheeler, Xiaoming Liu

{"title":"Improving face recognition with a quality-based probabilistic framework","authors":"N. Ozay, Yan Tong, F. Wheeler, Xiaoming Liu","doi":"10.1109/CVPRW.2009.5204299","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204299","url":null,"abstract":"This paper addresses the problem of developing facial image quality metrics that are predictive of the performance of existing biometric matching algorithms and incorporating the quality estimates into the recognition decision process to improve overall performance. The first task we consider is the separation of probe/gallery qualities since the match score depends on both. Given a set of training images of the same individual, we find the match scores between all possible probe/gallery image pairs. Then, we define symmetric normalized match score for any pair, model it as the average of the qualities of probe/gallery corrupted by additive noise, and estimate the quality values such that the noise is minimized. To utilize quality in the decision process, we employ a Bayesian network to model the relationships among qualities, predefined quality related image features and recognition. The recognition decision is made by probabilistic inference via this model. We illustrate with various face verification experiments that incorporating quality into the decision process can improve the performance significantly.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965886","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: 31

In between 3D Active Appearance Models and 3D Morphable Models 介于3D活动外观模型和3D变形模型之间

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204300

J. Heo, M. Savvides

{"title":"In between 3D Active Appearance Models and 3D Morphable Models","authors":"J. Heo, M. Savvides","doi":"10.1109/CVPRW.2009.5204300","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204300","url":null,"abstract":"In this paper we propose a novel method of generating 3D morphable models (3DMMs) from 2D images. We develop algorithms of 3D face reconstruction from a sparse set of points acquired from 2D images. In order to establish correspondence between images precisely, we combined active shape models (ASMs) and active appearance models (AAMs)(CASAAMs) in an intelligent way, showing improved performance on pixel-level accuracy and generalization to unseen faces. The CASAAMs are applied to the images of different views of the same person to extract facial shapes across pose. These 2D shapes are combined for reconstructing a sparse 3D model. The point density of the model is increased by the loop subdivision method, which generates new vertices by a weighted sum of the existing vertices. Then, the depth of the dense 3D model is modified with an average 3D depth-map in order to preserve facial structure more realistically. Finally, all 249 3D models with expression changes are combined to generate a 3DMM for a compact representation. The first session of the multi-PIE database, consisting of 249 persons with expression and illumination changes, is used for the modeling. Unlike typical 3DMMs, our model can generate 3D human faces more realistically and efficiently (2-3 seconds on P4 machine) under diverse illumination conditions.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720200","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: 14

HANOLISTIC: A Hierarchical Automatic Image Annotation System Using Holistic Approach HANOLISTIC:一种采用整体方法的分层自动图像标注系统

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204236

Özge Öztimur Karadag, F. Yarman-Vural

{"title":"HANOLISTIC: A Hierarchical Automatic Image Annotation System Using Holistic Approach","authors":"Özge Öztimur Karadag, F. Yarman-Vural","doi":"10.1109/CVPRW.2009.5204236","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204236","url":null,"abstract":"Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the annotation is formulated as a multivariate mapping from a set of independent descriptor spaces, representing a whole image, to a set of words, representing class labels. For this purpose, a hierarchical annotation architecture, named as HANOLISTIC (hierarchical image annotation system using holistic approach), is defined with two layers. The first layer, called level 0 consists of annotators each of which is fed by a set of distinct descriptors, extracted from the whole image. This enables us to represent the image at each annotator by a different visual property of a descriptor. Since, we use the whole image, the problematic segmentation process is avoided. Training of each annotator is accomplished by a supervised learning paradigm, where each word is considered as a class label. Note that, this approach is slightly different then the classical training approaches, where each data has a unique label. In the proposed system, since each image has one or more annotating words, we assume that an image belongs to more than one class. The output of the level 0 annotators indicate the membership values of the words in the vocabulary, to belong an image. These membership values from each annotator is, then, aggregated at the second layer to obtain meta level annotator. Finally, a set of words from the vocabulary is selected based on the ranking of the output of meta level. The hierarchical annotation system proposed in this study outperforms state of the art annotation systems based on segmental and holistic approaches.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129572288","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

Bicycle chain shape models 自行车链条形状模型

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204053

S. Sommer, Aditya Tatu, Cheng Chen, D. Jurgensen, Marleen de Bruijne, M. Loog, M. Nielsen, F. Lauze

引用次数: 15

Pedestrian association and localization in monocular FIR video sequence 单目FIR视频序列中的行人关联与定位

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204132

Mayank Bansal, Shunguang Wu, J. Eledath

引用次数: 2

A bottom-up and top-down optimization framework for learning a compositional hierarchy of object classes 一个自底向上和自顶向下的优化框架，用于学习对象类的组合层次结构

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204327

S. Fidler, Marko Boben, A. Leonardis

引用次数: 0

Distance guided selection of the best base classifier in an ensemble with application to cervigram image segmentation 距离引导下集合中最佳基分类器的选择及其在图像分割中的应用

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204048

Wei Wang, Xiaolei Huang

{"title":"Distance guided selection of the best base classifier in an ensemble with application to cervigram image segmentation","authors":"Wei Wang, Xiaolei Huang","doi":"10.1109/CVPRW.2009.5204048","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204048","url":null,"abstract":"We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes between a pair of statistical classifiers. By generating an ensemble of base classifiers trained independently on separate training images, we can use the distance metric to select those classifiers in the ensemble whose decision boundaries are similar to that of an unknown test image. In an extreme case, we select the base classifier with the most similar decision boundary to accomplish classification and segmentation on the test image. Our approach is novel in the way that the nearest neighbor is picked and effectively solves classification problems in which base classifiers with good overall performance are not easy to construct due to a large variation in the training examples. In our experiments, we applied our method and several popular ensemble methods to segmenting acetowhite regions in cervical images. The overall classification accuracy of the proposed method is significantly better than that of a single classifier learned using the entire training set, and is also superior to other ensemble methods including majority voting, STAPLE, Boosting and Bagging.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125062195","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