2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第5页

Towards automated large scale discovery of image families 朝着自动大规模发现图像族的方向发展

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204177

M. Aly, P. Welinder, Mario E. Munich, P. Perona

引用次数: 23

Multi-view reconstruction for projector camera systems based on bundle adjustment 基于束调整的投影摄像系统多视点重建

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204318

Furukawa Ryo, K. Inose, Hiroshi Kawasaki

{"title":"Multi-view reconstruction for projector camera systems based on bundle adjustment","authors":"Furukawa Ryo, K. Inose, Hiroshi Kawasaki","doi":"10.1109/CVPRW.2009.5204318","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204318","url":null,"abstract":"Range scanners using projector-camera systems have been studied actively in recent years as methods for measuring 3D shapes accurately and cost-effectively. To acquire an entire 3D shape of an object with such systems, the shape of the object should be captured from multiple directions and the set of captured shapes should be aligned using algorithms such as ICPs. Then, the aligned shapes are integrated into a single 3D shape model. However, the captured shapes are often distorted due to errors of intrinsic or extrinsic parameters of the camera and the projector. Because of these distortions, gaps between overlapped surfaces remain even after aligning the 3D shapes. In this paper, we propose a new method to capture an entire shape with high precision using an active stereo range scanner which consists of a projector and a camera with fixed relative positions. In the proposed method, minimization of calibration errors of the projector-camera pair and registration errors between 3D shapes from different viewpoints are simultaneously achieved. The proposed method can be considered as a variation of bundle adjustment techniques adapted to projector-camera systems. Since acquisition of correspondences between different views is not easy for projector-camera systems, a solution for the problem is also presented.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132854845","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: 8

Learning a hierarchical compositional representation of multiple object classes 学习多个对象类的分层组合表示

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204332

A. Leonardis

引用次数: 0

A syntax for image understanding 用于图像理解的语法

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204337

N. Ahuja

引用次数: 0

Accurate estimation of pulmonary nodule's growth rate in CT images with nonrigid registration and precise nodule detection and segmentation 利用非刚性配准和精确的结节检测与分割，准确估计CT图像中肺结节的生长速度

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204050

Yuanjie Zheng, C. Kambhamettu, T. Bauer, K. Steiner

引用次数: 16

Feature based person detection beyond the visible spectrum 超越可见光谱的基于特征的人检测

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204085

K. Jüngling, Michael Arens

引用次数: 62

Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos 红外视频运动目标检测动态背景的模糊统计建模

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204109

Fida El Baf, T. Bouwmans, B. Vachon

引用次数: 51

A framework for automated measurement of the intensity of non-posed Facial Action Units 一个用于自动测量非姿势面部动作单元强度的框架

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204259

M. Mahoor, S. Cadavid, D. Messinger, J. Cohn

{"title":"A framework for automated measurement of the intensity of non-posed Facial Action Units","authors":"M. Mahoor, S. Cadavid, D. Messinger, J. Cohn","doi":"10.1109/CVPRW.2009.5204259","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204259","url":null,"abstract":"This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126236149","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: 122

Inference and learning with hierarchical compositional models 基于分层组合模型的推理和学习

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204336

Iasonas Kokkinos, A. Yuille

引用次数: 2

An affine Invariant hyperspectral texture descriptor based upon heavy-tailed distributions and fourier analysis 基于重尾分布和傅立叶分析的仿射不变高光谱纹理描述子

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPRW.2009.5204126

P. Khuwuthyakorn, A. Robles-Kelly, J. Zhou

{"title":"An affine Invariant hyperspectral texture descriptor based upon heavy-tailed distributions and fourier analysis","authors":"P. Khuwuthyakorn, A. Robles-Kelly, J. Zhou","doi":"10.1109/CVPRW.2009.5204126","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204126","url":null,"abstract":"In this paper, we address the problem of recovering a hyperspectral texture descriptor. We do this by viewing the wavelength-indexed bands corresponding to the texture in the image as those arising from a stochastic process whose statistics can be captured making use of the relationships between moment generating functions and Fourier kernels. In this manner, we can interpret the probability distribution of the hyper-spectral texture as a heavy-tailed one which can be rendered invariant to affine geometric transformations on the texture plane making use of the spectral power of its Fourier cosine transform. We do this by recovering the affine geometric distortion matrices corresponding to the probability density function for the texture under study. This treatment permits the development of a robust descriptor which has a high information compaction property and can capture the space and wavelength correlation for the spectra in the hyperspectral images. We illustrate the utility of our descriptor for purposes of recognition and provide results on real-world datasets. We also compare our results to those yielded by a number of alternatives.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126655433","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