2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第4页

An implicit spatiotemporal shape model for human activity localization and recognition 人类活动定位与识别的隐式时空形状模型

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204262

A. Oikonomopoulos, I. Patras, M. Pantic

{"title":"An implicit spatiotemporal shape model for human activity localization and recognition","authors":"A. Oikonomopoulos, I. Patras, M. Pantic","doi":"10.1109/CVPRW.2009.5204262","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204262","url":null,"abstract":"In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic, sparse, `visual words' and `visual verbs'. Evidence for the spatiotemporal localization of the activity are accumulated in a probabilistic spatiotemporal voting scheme. The local nature of our voting framework allows us to recover multiple activities that take place in the same scene, as well as activities in the presence of clutter and occlusions. We construct class-specific codebooks using the descriptors in the training set, where we take the spatial co-occurrences of pairs of codewords into account. The positions of the codeword pairs with respect to the object centre, as well as the frame in the training set in which they occur are subsequently stored in order to create a spatiotemporal model of codeword co-occurrences. During the testing phase, we use mean shift mode estimation in order to spatially segment the subject that performs the activities in every frame, and the Radon transform in order to extract the most probable hypotheses concerning the temporal segmentation of the activities within the continuous stream.","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":"133820322","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: 35

Fusion of a camera and a laser range sensor for vehicle recognition 融合了摄像头和激光测距传感器，用于车辆识别

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204099

S. Mohottala, Shintaro Ono, M. Kagesawa, K. Ikeuchi

引用次数: 12

GPU-accelerated, gradient-free MI deformable registration for atlas-based MR brain image segmentation 基于阿特拉斯的磁共振脑图像分割的gpu加速，无梯度MI可变形配准

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204043

Xiao Han, L. Hibbard, V. Willcut

引用次数: 33

Is there a general structure for grammars? 语法有一个通用的结构吗?

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204334

D. Mumford

{"title":"Is there a general structure for grammars?","authors":"D. Mumford","doi":"10.1109/CVPRW.2009.5204334","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204334","url":null,"abstract":"Summary form only given. Linguists have proposed dozens of formalisms for grammars and now vision is weighing in with its versions based on its needs. Ulf Grenander has proposed general pattern theory, and has used grammar-like graphical parses of \"thoughts\" in the style of AI. One wants a natural, simple formalism treating all these cases. I want to pose this as a central problem in modeling intelligence. Pattern theory started in the 70's with the ideas of Ulf Grenander and his school at Brown. The aim is to analyze from a statistical point of view the patterns in all \"signals\" generated by the world, whether they be images, sounds, written text, DNA or protein strings, spike trains in neurons, time series of prices or weather, etc. Pattern theory proposes that the types of patterns-and the hidden variables needed to describe these patterns - found in one class of signals will often be found in the others and that their characteristic variability will be similar. The underlying idea is to find classes of stochastic models which can capture all the patterns that we see in nature, so that random samples from these models have the same \"look and feel\" as the samples from the world itself. Then the detection of patterns in noisy and ambiguous samples can be achieved by the use of Bayes' rule, a method that can be described as \"analysis by synthesis\".","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":"123248761","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

3D stochastic completion fields for fiber tractography 纤维束成像的三维随机完井场

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204044

P. MomayyezSiahkal, Kaleem Siddiqi

引用次数: 10

Nonparametric bottom-up saliency detection by self-resemblance 基于自相似的非参数自底向上显著性检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204207

H. Seo, P. Milanfar

引用次数: 116

Robust feature matching in 2.3µs 在2.3µs内实现鲁棒特征匹配

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204314

S. Taylor, E. Rosten, T. Drummond

{"title":"Robust feature matching in 2.3µs","authors":"S. Taylor, E. Rosten, T. Drummond","doi":"10.1109/CVPRW.2009.5204314","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204314","url":null,"abstract":"In this paper we present a robust feature matching scheme in which features can be matched in 2.3µs. For a typical task involving 150 features per image, this results in a processing time of 500µs for feature extraction and matching. In order to achieve very fast matching we use simple features based on histograms of pixel intensities and an indexing scheme based on their joint distribution. The features are stored with a novel bit mask representation which requires only 44 bytes of memory per feature and allows computation of a dissimilarity score in 20ns. A training phase gives the patch-based features invariance to small viewpoint variations. Larger viewpoint variations are handled by training entirely independent sets of features from different viewpoints. A complete system is presented where a database of around 13,000 features is used to robustly localise a single planar target in just over a millisecond, including all steps from feature detection to model fitting. The resulting system shows comparable robustness to SIFT [8] and Ferns [14] while using a tiny fraction of the processing time, and in the latter case a fraction of the memory as well.","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":"125116364","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: 78

A method for selecting and ranking quality metrics for optimization of biometric recognition systems 一种用于优化生物特征识别系统的质量度量的选择和排序方法

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204309

N. Schmid, Francesco Nicolo

{"title":"A method for selecting and ranking quality metrics for optimization of biometric recognition systems","authors":"N. Schmid, Francesco Nicolo","doi":"10.1109/CVPRW.2009.5204309","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204309","url":null,"abstract":"In the field of biometrics evaluation of quality of biometric samples has a number of important applications. The main applications include (1) to reject poor quality images during acquisition, (2) to use as enhancement metric, and (3) to apply as a weighting factor in fusion schemes. Since a biometric-based recognition system relies on measures of performance such as matching scores and recognition probability of error, it becomes intuitive that the metrics evaluating biometric sample quality have to be linked to the recognition performance of the system. The goal of this work is to design a method for evaluating and ranking various quality metrics applied to biometric images or signals based on their ability to predict recognition performance of a biometric recognition system. The proposed method involves: (1) Preprocessing algorithm operating on pairs of quality scores and generating relative scores, (2) Adaptive multivariate mapping relating quality scores and measures of recognition performance and (3) Ranking algorithm that selects the best combinations of quality measures. The performance of the method is demonstrated on face and iris biometric data.","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":"128244961","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

Multiple label prediction for image annotation with multiple Kernel correlation models 基于多核相关模型的图像标注多标签预测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204274

Oksana Yakhnenko, Vasant G Honavar

{"title":"Multiple label prediction for image annotation with multiple Kernel correlation models","authors":"Oksana Yakhnenko, Vasant G Honavar","doi":"10.1109/CVPRW.2009.5204274","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204274","url":null,"abstract":"Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of an image into some semantic space, and then recovers the caption by performing another linear transformation from the semantic space into the label space. The model is trained so that model parameters minimize the error of reconstruction directly. This model is related to Canonical Correlation Analysis (CCA) which maps both images and caption into the semantic space to minimize the distance of mapping in the semantic space. Kernel trick is then used for the MLR resulting in Kernel Multiple Linear Regression model. The solution to KMLR is a solution to the generalized eigen-value problem, related to KCCA (Kernel Canonical Correlation Analysis). We then extend Kernel Multiple Linear Regression and Kernel Canonical Correlation analysis models to multiple kernel setting, to allow various representations of images and captions. We present results for image annotation using Multiple Kernel Learning CCA and MLR on Oliva and Torralba (2001) scene recognition that show kernel selection behaviour.","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":"130887149","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: 15

On conversion from color to gray-scale images for face detection 用于人脸检测的彩色图像到灰度图像的转换

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204297

Juwei Lu, K. Plataniotis

引用次数: 29