2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第8页

Physiological modelling for improved reliability in silhouette-driven gradient-based hand tracking 提高轮廓驱动的基于梯度的手部跟踪可靠性的生理建模

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204252

Paris Kaimakis, Joan Lasenby

引用次数: 8

Event detection using local binary pattern based dynamic textures 使用基于局部二进制模式的动态纹理进行事件检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204204

Yunqian Ma, P. Císar̆

引用次数: 34

Robust facial action recognition from real-time 3D streams 鲁棒面部动作识别实时3D流

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204281

F. Tsalakanidou, S. Malassiotis

引用次数: 27

Incremental Bayesian learning of feature points from natural images 自然图像特征点的增量贝叶斯学习

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204292

M. Toivanen, J. Lampinen

{"title":"Incremental Bayesian learning of feature points from natural images","authors":"M. Toivanen, J. Lampinen","doi":"10.1109/CVPRW.2009.5204292","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204292","url":null,"abstract":"Selecting automatically feature points of an object appearing in images is a difficult but vital task for learning the feature point based representation of the object model. In this work we present an incremental Bayesian model that learns the feature points of an object from natural un-annotated images by matching the corresponding points. The training set is recursively expanded and the model parameters updated after matching each image. The set of nodes in the first image is matched in the second image, by sampling the un-normalized posterior distribution with particle filters. For each matched node the model assigns a probability for it to be associated with the object, and having matched few images, the nodes with low association probabilities are replaced with new ones to increase the number of the object nodes. A feature point based representation of the object model is formed from the matched corresponding points. In the tested images, the model matches the corresponding points better than the well-known elastic bunch graph matching batch method and gives promising results in recognizing learned object models in novel images.","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":"123432864","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

Computer vision on tap 计算机视觉开启

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204229

Kevin Chiu, R. Raskar

引用次数: 6

Beyond one-to-one feature correspondence: The need for many-to-many matching and image abstraction 除了一对一的特征对应:需要多对多匹配和图像抽象

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204333

Sven J. Dickinson

引用次数: 1

Automatic detection of body parts in x-ray images x射线图像中人体部位的自动检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204353

V. Jeanne, D. Ünay, Vincent Jacquet

引用次数: 18

Robust estimation of stem cell lineages using local graph matching 利用局部图匹配稳健估计干细胞谱系

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204045

Min Liu, A. Roy-Chowdhury, G. Reddy

{"title":"Robust estimation of stem cell lineages using local graph matching","authors":"Min Liu, A. Roy-Chowdhury, G. Reddy","doi":"10.1109/CVPRW.2009.5204045","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204045","url":null,"abstract":"In this paper, we present a local graph matching based method for tracking cells and cell divisions. This will allow us to estimate the lineages of the cells in a 4D spatio-temporal image stack obtained using fluorescence imaging techniques. We work with plant cells, where the cells are tightly clustered in space and computing correspondences in space and time can be very challenging. The local graph matching method is able to compute the lineages even when significant portions of the images are corrupted due to sensor noise in the imaging process or segmentation errors. The geometric structure and topology of the cells' relative positions are efficiently exploited to solve the tracking problem using the local graph matching technique. The process not only computes the correspondences of cells across spatial and temporal image slices, but is also able to find out where and when cells divide, identify new cells and detect missing ones. Using this method we show experimental results to track the properly segmented cells, and compute cell lineages from images captured over 72 hours, even when some of those images are highly noisy (e.g., missing cells).","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":"126935679","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

Automatic symmetry-integrated brain injury detection in MRI sequences MRI序列中对称性集成脑损伤自动检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204052

Yu Sun, B. Bhanu, Shiv Bhanu

{"title":"Automatic symmetry-integrated brain injury detection in MRI sequences","authors":"Yu Sun, B. Bhanu, Shiv Bhanu","doi":"10.1109/CVPRW.2009.5204052","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204052","url":null,"abstract":"This paper presents a fully automated symmetry-integrated brain injury detection method for magnetic resonance imaging (MRI) sequences. One of the limitations of current injury detection methods often involves a large amount of training data or a prior model that is only applicable to a limited domain of brain slices, with low computational efficiency and robustness. Our proposed approach can detect injuries from a wide variety of brain images since it makes use of symmetry as a dominant feature, and does not rely on any prior models and training phases. The approach consists of the following steps: (a) symmetry integrated segmentation of brain slices based on symmetry affinity matrix, (b) computation of kurtosis and skewness of symmetry affinity matrix to find potential asymmetric regions, (c) clustering of the pixels in symmetry affinity matrix using a 3D relaxation algorithm, (d) fusion of the results of (b) and (c) to obtain refined asymmetric regions, (e) Gaussian mixture model for unsupervised classification of potential asymmetric regions as the set of regions corresponding to brain injuries. Experimental results are carried out to demonstrate the efficacy of the approach.","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":"132318944","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

Deformable tree models for 2D and 3D branching structures extraction 可变形树模型用于二维和三维分支结构提取

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204049

J. Mille, L. Cohen

引用次数: 30