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

A robust parametric method for bias field estimation and segmentation of MR images 一种鲁棒的磁共振图像偏置场估计和分割方法

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206553

Chunming Li, Chris Gatenby, Li Wang, J. Gore

{"title":"A robust parametric method for bias field estimation and segmentation of MR images","authors":"Chunming Li, Chris Gatenby, Li Wang, J. Gore","doi":"10.1109/CVPR.2009.5206553","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206553","url":null,"abstract":"This paper proposes a new energy minimization framework for simultaneous estimation of the bias field and segmentation of tissues for magnetic resonance images. The bias field is modeled as a linear combination of a set of basis functions, and thereby parameterized by the coefficients of the basis functions. We define an energy that depends on the coefficients of the basis functions, the membership functions of the tissues in the image, and the constants approximating the true signal from the corresponding tissues. This energy is convex in each of its variables. Bias field estimation and image segmentation are simultaneously achieved as the result of minimizing this energy. We provide an efficient iterative algorithm for energy minimization, which converges to the optimal solution at a fast rate. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. The proposed method has been successfully applied to 3-Tesla MR images with desirable results. Comparisons with other approaches demonstrate the superior performance of this algorithm.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"127544805","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: 90

Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information 通过学习3D和2D信息，从超声体积中自动检测胎儿面部

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206527

Shaolei Feng, S. Zhou, Sara Good, D. Comaniciu

引用次数: 37

Learning semantic scene models by object classification and trajectory clustering 通过对象分类和轨迹聚类学习语义场景模型

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206809

Tianzhu Zhang, Hanqing Lu, S. Li

{"title":"Learning semantic scene models by object classification and trajectory clustering","authors":"Tianzhu Zhang, Hanqing Lu, S. Li","doi":"10.1109/CVPR.2009.5206809","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206809","url":null,"abstract":"Activity analysis is a basic task in video surveillance and has become an active research area. However, due to the diversity of moving objects category and their motion patterns, developing robust semantic scene models for activity analysis remains a challenging problem in traffic scenarios. This paper proposes a novel framework to learn semantic scene models. In this framework, the detected moving objects are first classified as pedestrians or vehicles via a co-trained classifier which takes advantage of the multiview information of objects. As a result, the framework can automatically learn motion patterns respectively for pedestrians and vehicles. Then, a graph is proposed to learn and cluster the motion patterns. To this end, trajectory is parameterized and the image is cut into multiple blocks which are taken as the nodes in the graph. Based on the parameters of trajectories, the primary motion patterns in each node (block) are extracted via Gaussian mixture model (GMM), and supplied to this graph. The graph cut algorithm is finally employed to group the motion patterns together, and trajectories are clustered to learn semantic scene models. Experimental results and applications to real world scenes show the validity of our proposed method.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"129134467","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: 117

Mutual information-based stereo matching combined with SIFT descriptor in log-chromaticity color space 对数色度空间中基于互信息的立体匹配与SIFT描述子相结合

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206507

Y. S. Heo, Kyoung Mu Lee, Sang Uk Lee

{"title":"Mutual information-based stereo matching combined with SIFT descriptor in log-chromaticity color space","authors":"Y. S. Heo, Kyoung Mu Lee, Sang Uk Lee","doi":"10.1109/CVPR.2009.5206507","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206507","url":null,"abstract":"Radiometric variations between input images can seriously degrade the performance of stereo matching algorithms. In this situation, mutual information is a very popular and powerful measure which can find any global relationship of intensities between two input images taken from unknown sources. The mutual information-based method, however, is still ambiguous or erroneous as regards local radiometric variations, since it only accounts for global variation between images, and does not contain spatial information properly. In this paper, we present a new method based on mutual information combined with SIFT descriptor to find correspondence for images which undergo local as well as global radiometric variations. We transform the input color images to log-chromaticity color space from which a linear relationship can be established. To incorporate spatial information in mutual information, we utilize the SIFT descriptor which includes near pixel gradient histogram to construct a joint probability in log-chromaticity color space. By combining the mutual information as an appearance measure and the SIFT descriptor as a geometric measure, we devise a robust and accurate stereo system. Experimental results show that our method is superior to the state-of-the art algorithms including conventional mutual information-based methods and window correlation methods under various radiometric changes.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"132861938","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

Adaptive image and video retargeting technique based on Fourier analysis 基于傅里叶分析的自适应图像和视频重定向技术

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206666

Jun-Seong Kim, Jin-Hwan Kim, Chang-Su Kim

引用次数: 77

Trajectory reconstruction for affine structure-from-motion by global and local constraints 基于全局和局部约束的仿射结构运动轨迹重建

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206664

H. Ackermann, B. Rosenhahn

{"title":"Trajectory reconstruction for affine structure-from-motion by global and local constraints","authors":"H. Ackermann, B. Rosenhahn","doi":"10.1109/CVPR.2009.5206664","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206664","url":null,"abstract":"The problem of reconstructing a 3D scene from a moving camera can be solved by means of the so-called Factorization method. It directly computes a global solution without the need to merge several partial reconstructions. However, if the trajectories are not complete, i.e. not every feature point could be observed in all the images, this method cannot be used. We use a Factorization-style algorithm for recovering the unobserved feature positions in a non-incremental way. This method uniformly utilizes all data and finds a global solution without any need of sequential or hierarchical merging. Two contributions are made in this work: Firstly, partially known trajectories are completed by minimizing the distance between the subspace and the trajectory within an affine subspace associated with the trajectory. This amounts to imposing a global constraint on the data. Secondly, we propose to further include local constraints derived from epipolar geometry into the estimation. It is shown how to simultaneously optimize both constraints. By using simulated and real image sequences we show the improvements achieved with our algorithm.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"126403555","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

Multiple view image denoising 多视图图像去噪

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206836

Li Zhang, Sundeep Vaddadi, Hailin Jin, S. Nayar

引用次数: 87

Actions in context 上下文中的动作

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206557

Marcin Marszalek, I. Laptev, C. Schmid

{"title":"Actions in context","authors":"Marcin Marszalek, I. Laptev, C. Schmid","doi":"10.1109/CVPR.2009.5206557","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206557","url":null,"abstract":"This paper exploits the context of natural dynamic scenes for human action recognition in video. Human actions are frequently constrained by the purpose and the physical properties of scenes and demonstrate high correlation with particular scene classes. For example, eating often happens in a kitchen while running is more common outdoors. The contribution of this paper is three-fold: (a) we automatically discover relevant scene classes and their correlation with human actions, (b) we show how to learn selected scene classes from video without manual supervision and (c) we develop a joint framework for action and scene recognition and demonstrate improved recognition of both in natural video. We use movie scripts as a means of automatic supervision for training. For selected action classes we identify correlated scene classes in text and then retrieve video samples of actions and scenes for training using script-to-video alignment. Our visual models for scenes and actions are formulated within the bag-of-features framework and are combined in a joint scene-action SVM-based classifier. We report experimental results and validate the method on a new large dataset with twelve action classes and ten scene classes acquired from 69 movies.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"121072092","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: 1350

Learning invariant features through topographic filter maps 通过地形滤波图学习不变特征

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206545

K. Kavukcuoglu, Marc'Aurelio Ranzato, R. Fergus, Yann LeCun

{"title":"Learning invariant features through topographic filter maps","authors":"K. Kavukcuoglu, Marc'Aurelio Ranzato, R. Fergus, Yann LeCun","doi":"10.1109/CVPR.2009.5206545","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206545","url":null,"abstract":"Several recently-proposed architectures for high-performance object recognition are composed of two main stages: a feature extraction stage that extracts locally-invariant feature vectors from regularly spaced image patches, and a somewhat generic supervised classifier. The first stage is often composed of three main modules: (1) a bank of filters (often oriented edge detectors); (2) a non-linear transform, such as a point-wise squashing functions, quantization, or normalization; (3) a spatial pooling operation which combines the outputs of similar filters over neighboring regions. We propose a method that automatically learns such feature extractors in an unsupervised fashion by simultaneously learning the filters and the pooling units that combine multiple filter outputs together. The method automatically generates topographic maps of similar filters that extract features of orientations, scales, and positions. These similar filters are pooled together, producing locally-invariant outputs. The learned feature descriptors give comparable results as SIFT on image recognition tasks for which SIFT is well suited, and better results than SIFT on tasks for which SIFT is less well suited.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"121089634","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: 351

Locally time-invariant models of human activities using trajectories on the grassmannian 利用格拉斯曼年轨迹的人类活动局部时不变模型

2009 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206710

P. Turaga, R. Chellappa

{"title":"Locally time-invariant models of human activities using trajectories on the grassmannian","authors":"P. Turaga, R. Chellappa","doi":"10.1109/CVPR.2009.5206710","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206710","url":null,"abstract":"Human activity analysis is an important problem in computer vision with applications in surveillance and summarization and indexing of consumer content. Complex human activities are characterized by non-linear dynamics that make learning, inference and recognition hard. In this paper, we consider the problem of modeling and recognizing complex activities which exhibit time-varying dynamics. To this end, we describe activities as outputs of linear dynamic systems (LDS) whose parameters vary with time, or a time-varying linear dynamic system (TV-LDS). We discuss parameter estimation methods for this class of models by assuming that the parameters are locally time-invariant. Then, we represent the space of LDS models as a Grassmann manifold. Then, the TV-LDS model is defined as a trajectory on the Grassmann manifold. We show how trajectories on the Grassmannian can be characterized using appropriate distance metrics and statistical methods that reflect the underlying geometry of the manifold. This results in more expressive and powerful models for complex human activities. We demonstrate the strength of the framework for activity-based summarization of long videos and recognition of complex human actions on two datasets.","PeriodicalId":386532,"journal":{"name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"115315126","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: 63