2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第9页

Classification trees for fast segmentation of DTI brain fiber tracts DTI脑纤维束快速分割的分类树

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562998

Gali Zimmerman-Moreno, Arnaldo Mayer, H. Greenspan

引用次数: 4

Face verification on color images using local features 基于局部特征的彩色图像人脸验证

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563123

M. Villegas, Roberto Paredes Palacios, Alfons Juan-Císcar, E. Vidal

引用次数: 18

Reliable detection of core and delta in fingerprints by using singular candidate method 基于奇异候选方法的指纹核心和增量的可靠检测

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563119

T. Ohtsuka, Daisuke Watanabe, Daisuke Tomizawa, Yuta Hasegawa, H. Aoki

{"title":"Reliable detection of core and delta in fingerprints by using singular candidate method","authors":"T. Ohtsuka, Daisuke Watanabe, Daisuke Tomizawa, Yuta Hasegawa, H. Aoki","doi":"10.1109/CVPRW.2008.4563119","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563119","url":null,"abstract":"The singular points of fingerprints, namely, core and delta, are important referential points for the classification of fingerprints. Several conventional approaches such as the Poincare index method have been proposed; however, these approaches cannot achieve the reliable detection of poor-quality fingerprints. In this paper, we propose a new core and delta detection method by singular candidate analysis using an extended relational graph. In order to use both the local and global features of the ridge direction patterns and to realize a method with high tolerance to local image noise, singular candidate analysis is adopted in the detection process; this analysis involves the extraction of locations in which the probability of the existence of a singular point is high. The experimental results show that the success rate of this approach is higher than that of the Poincare index method by 10% for singularity detection using the fingerprint image databases FVC2000 and FVC2002. These databases contain several poor quality images, even though the average computation time is 15%-30% greater than the Poincare index method.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"2676 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122646448","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: 21

Tracking objects in 6D for reconstructing static scenes 跟踪对象在6D重建静态场景

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563155

Agnes Swadzba, Niklas Beuter, Joachim Schmidt, G. Sagerer

引用次数: 23

Model-based perceptual grouping and shape abstraction 基于模型的感知分组与形状抽象

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562979

Pablo Sala, Sven J. Dickinson

引用次数: 12

Particle filtering with rendered models: A two pass approach to multi-object 3D tracking with the GPU 渲染模型的粒子滤波:用GPU实现多目标3D跟踪的两步方法

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563102

E. Murphy-Chutorian, M. Trivedi

引用次数: 14

Multivariate analysis of thalamo-cortical connectivity loss in TBI 脑外伤丘脑-皮质连通性丧失的多变量分析

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562992

J. Duda, B. Avants, Junghoon J. Kim, Hui Zhang, Sunil Patel, J. Whyte, J. Gee

{"title":"Multivariate analysis of thalamo-cortical connectivity loss in TBI","authors":"J. Duda, B. Avants, Junghoon J. Kim, Hui Zhang, Sunil Patel, J. Whyte, J. Gee","doi":"10.1109/CVPRW.2008.4562992","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4562992","url":null,"abstract":"Diffusion tensor (DT) images quantify connectivity patterns in the brain while the T1 modality provides high-resolution images of tissue interfaces. Our objective is to use both modalities to build subject-specific, quantitative models of fiber connections in order to discover effects specific to a neural system. The health of this thalamo-cortical network is compromised by traumatic brain injury, and we hypothesize that these effects are due to a primary injury to the thalamus which results in subsequent compromise of radiating fibers. We first use a population-specific average T1 and DT template to label the thalamus and Brodmann areas (BA) 9,10 and 11 in each subject. We also build an expected connection model within this template space that is transferred to subject space in order to provide a prior restriction on probabilistic tracking performed in subject space. We evaluate the effect of traumatic brain injury on this prefrontal-thalamus network by quantifying, in 10 subjects and 8 controls, the mean diffusion and fractional anisotropy along fiber tracts, along with the mean diffusion within the thalamus and cortical regions. We contrast results gained by a template-based tract definition with those gained by performing analysis in the subject space. Both approaches reveal connectivity effects of TBI, specifically a region of reduced FA in the white matter connecting the thalamus to BA 10.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126268144","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

3D Spatio-Temporal face recognition using dynamic range model sequences 基于动态范围模型序列的三维时空人脸识别

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563125

Yi Sun, L. Yin

引用次数: 10

TOF imaging in Smart room environments towards improved people tracking TOF成像在智能房间环境中改善人员跟踪

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563154

Sigurjón Árni Guðmundsson, R. Larsen, H. Aanæs, M. Pardàs, J. Casas

引用次数: 52

Model-based mapping of a nonrigid image registration algorithm to heterogeneous architectures 基于模型的非刚性图像配准算法到异构体系结构的映射

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563151

Y. Hemaraj, M. Sen, W. Plishker, R. Shekhar, S. Bhattacharyya

{"title":"Model-based mapping of a nonrigid image registration algorithm to heterogeneous architectures","authors":"Y. Hemaraj, M. Sen, W. Plishker, R. Shekhar, S. Bhattacharyya","doi":"10.1109/CVPRW.2008.4563151","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563151","url":null,"abstract":"This work targets the design of customized accelerators for image registration algorithms, which are required for many important computer vision applications. By capturing key, domain-specific characteristics of application structure, signal-processing-oriented models of computation provide a valuable foundation for structured development of efficient image registration accelerators. Building upon the meta-modeling framework of homogeneous parameterized dataflow, we develop in this paper an approach for automatically generating streamlined implementations of image registration algorithms according to performance metrics such as image size, area and overall processing speed. Results from hardware synthesis demonstrate the efficiency of our methods. Our approach provides designers an effective way to explore different architectures, and systematically provide acceleration for high-performance nonrigid image registration based on a variety of requirements. Our dataflow-based framework can be adapted to explore different architectures for other kinds of image processing algorithms as well.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115066765","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