2013 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第7页

Manhattan Scene Understanding via XSlit Imaging 通过XSlit成像了解曼哈顿场景

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.18

Jinwei Ye, Yu Ji, Jingyi Yu

{"title":"Manhattan Scene Understanding via XSlit Imaging","authors":"Jinwei Ye, Yu Ji, Jingyi Yu","doi":"10.1109/CVPR.2013.18","DOIUrl":"https://doi.org/10.1109/CVPR.2013.18","url":null,"abstract":"A Manhattan World (MW) [3] is composed of planar surfaces and parallel lines aligned with three mutually orthogonal principal axes. Traditional MW understanding algorithms rely on geometry priors such as the vanishing points and reference (ground) planes for grouping coplanar structures. In this paper, we present a novel single-image MW reconstruction algorithm from the perspective of non-pinhole cameras. We show that by acquiring the MW using an XSlit camera, we can instantly resolve co planarity ambiguities. Specifically, we prove that parallel 3D lines map to 2D curves in an XSlit image and they converge at an XSlit Vanishing Point (XVP). In addition, if the lines are coplanar, their curved images will intersect at a second common pixel that we call Coplanar Common Point (CCP). CCP is a unique image feature in XSlit cameras that does not exist in pinholes. We present a comprehensive theory to analyze XVPs and CCPs in a MW scene and study how to recover 3D geometry in a complex MW scene from XVPs and CCPs. Finally, we build a prototype XSlit camera by using two layers of cylindrical lenses. Experimental results on both synthetic and real data show that our new XSlit-camera-based solution provides an effective and reliable solution for MW understanding.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"47 1","pages":"81-88"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90353546","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: 6

Finding Group Interactions in Social Clutter 在社会混乱中发现群体互动

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.351

Ruonan Li, Parker Porfilio, Todd E. Zickler

{"title":"Finding Group Interactions in Social Clutter","authors":"Ruonan Li, Parker Porfilio, Todd E. Zickler","doi":"10.1109/CVPR.2013.351","DOIUrl":"https://doi.org/10.1109/CVPR.2013.351","url":null,"abstract":"We consider the problem of finding distinctive social interactions involving groups of agents embedded in larger social gatherings. Given a pre-defined gallery of short exemplar interaction videos, and a long input video of a large gathering (with approximately-tracked agents), we identify within the gathering small sub-groups of agents exhibiting social interactions that resemble those in the exemplars. The participants of each detected group interaction are localized in space, the extent of their interaction is localized in time, and when the gallery of exemplars is annotated with group-interaction categories, each detected interaction is classified into one of the pre-defined categories. Our approach represents group behaviors by dichotomous collections of descriptors for (a) individual actions, and (b) pair-wise interactions, and it includes efficient algorithms for optimally distinguishing participants from by-standers in every temporal unit and for temporally localizing the extent of the group interaction. Most importantly, the method is generic and can be applied whenever numerous interacting agents can be approximately tracked over time. We evaluate the approach using three different video collections, two that involve humans and one that involves mice.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"74 1","pages":"2722-2729"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86560998","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: 20

Poselet Key-Framing: A Model for Human Activity Recognition 键框架:人类活动识别的一个模型

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.342

Michalis Raptis, L. Sigal

引用次数: 235

Representing Videos Using Mid-level Discriminative Patches 使用中级判别补丁表示视频

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.332

Arpit Jain, A. Gupta, Mikel D. Rodriguez, L. Davis

引用次数: 155

Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity 基于单目模板的局部线弹性可扩展曲面三维重建

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.200

Abed C. Malti, R. Hartley, A. Bartoli, Jae-Hak Kim

引用次数: 63

Discriminative Non-blind Deblurring 判别非盲去模糊

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.84

Uwe Schmidt, C. Rother, Sebastian Nowozin, Jeremy Jancsary, S. Roth

引用次数: 122

Learning Class-to-Image Distance with Object Matchings 通过对象匹配学习类到图像的距离

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.108

Guang-Tong Zhou, Tian Lan, Weilong Yang, Greg Mori

引用次数: 8

GeoF: Geodesic Forests for Learning Coupled Predictors GeoF:用于学习耦合预测器的测地线森林

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.16

P. Kontschieder, Pushmeet Kohli, J. Shotton, A. Criminisi

{"title":"GeoF: Geodesic Forests for Learning Coupled Predictors","authors":"P. Kontschieder, Pushmeet Kohli, J. Shotton, A. Criminisi","doi":"10.1109/CVPR.2013.16","DOIUrl":"https://doi.org/10.1109/CVPR.2013.16","url":null,"abstract":"Conventional decision forest based methods for image labelling tasks like object segmentation make predictions for each variable (pixel) independently [3, 5, 8]. This prevents them from enforcing dependencies between variables and translates into locally inconsistent pixel labellings. Random field models, instead, encourage spatial consistency of labels at increased computational expense. This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on. Such correlations are captured via new long-range, soft connectivity features, computed via generalized geodesic distance transforms. Our model can be thought of as a generalization of the successful Semantic Texton Forest, Auto-Context, and Entangled Forest models. A second contribution is to show the connection between the typical Conditional Random Field (CRF) energy and the forest training objective. This analysis yields a new objective for training decision forests that encourages more accurate structured prediction. Our GeoF model is validated quantitatively on the task of semantic image segmentation, on four challenging and very diverse image datasets. GeoF outperforms both state of-the-art forest models and the conventional pair wise CRF.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"61 11 1","pages":"65-72"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83546441","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: 71

Large Displacement Optical Flow from Nearest Neighbor Fields 来自最近邻场的大位移光流

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.316

Zhuoyuan Chen, Hailin Jin, Zhe L. Lin, Scott D. Cohen, Ying Wu

{"title":"Large Displacement Optical Flow from Nearest Neighbor Fields","authors":"Zhuoyuan Chen, Hailin Jin, Zhe L. Lin, Scott D. Cohen, Ying Wu","doi":"10.1109/CVPR.2013.316","DOIUrl":"https://doi.org/10.1109/CVPR.2013.316","url":null,"abstract":"We present an optical flow algorithm for large displacement motions. Most existing optical flow methods use the standard coarse-to-fine framework to deal with large displacement motions which has intrinsic limitations. Instead, we formulate the motion estimation problem as a motion segmentation problem. We use approximate nearest neighbor fields to compute an initial motion field and use a robust algorithm to compute a set of similarity transformations as the motion candidates for segmentation. To account for deviations from similarity transformations, we add local deformations in the segmentation process. We also observe that small objects can be better recovered using translations as the motion candidates. We fuse the motion results obtained under similarity transformations and under translations together before a final refinement. Experimental validation shows that our method can successfully handle large displacement motions. Although we particularly focus on large displacement motions in this work, we make no sacrifice in terms of overall performance. In particular, our method ranks at the top of the Middlebury benchmark.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"44 1","pages":"2443-2450"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78886969","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: 168

Intrinsic Characterization of Dynamic Surfaces 动态曲面的内在表征

2013 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.37

Tony Tung, T. Matsuyama

引用次数: 6