2014 IEEE Conference on Computer Vision and Pattern Recognition最新文献_第2页

Multiview Shape and Reflectance from Natural Illumination 自然光照下的多视图形状和反射率

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.277

Geoffrey Oxholm, K. Nishino

{"title":"Multiview Shape and Reflectance from Natural Illumination","authors":"Geoffrey Oxholm, K. Nishino","doi":"10.1109/CVPR.2014.277","DOIUrl":"https://doi.org/10.1109/CVPR.2014.277","url":null,"abstract":"The world is full of objects with complex reflectances, situated in complex illumination environments. Past work on full 3D geometry recovery, however, has tried to handle this complexity by framing it into simplistic models of reflectance (Lambetian, mirrored, or diffuse plus specular) or illumination (one or more point light sources). Though there has been some recent progress in directly utilizing such complexities for recovering a single view geometry, it is not clear how such single-view methods can be extended to reconstruct the full geometry. To this end, we derive a probabilistic geometry estimation method that fully exploits the rich signal embedded in complex appearance. Though each observation provides partial and unreliable information, we show how to estimate the reflectance responsible for the diverse appearance, and unite the orientation cues embedded in each observation to reconstruct the underlying geometry. We demonstrate the effectiveness of our method on synthetic and real-world objects. The results show that our method performs accurately across a wide range of real-world environments and reflectances that lies between the extremes that have been the focus of past work.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125093700","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: 62

Multimodal Learning in Loosely-Organized Web Images 松散组织的网络图像中的多模式学习

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.316

Kun Duan, David J. Crandall, Dhruv Batra

引用次数: 14

Looking Beyond the Visible Scene 超越可见场景

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.474

A. Khosla, Byoungkwon An, Joseph J. Lim, A. Torralba

{"title":"Looking Beyond the Visible Scene","authors":"A. Khosla, Byoungkwon An, Joseph J. Lim, A. Torralba","doi":"10.1109/CVPR.2014.474","DOIUrl":"https://doi.org/10.1109/CVPR.2014.474","url":null,"abstract":"A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123794460","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: 72

Automatic Face Reenactment 自动面部再现

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.537

Pablo Garrido, Levi Valgaerts, Ole Rehmsen, Thorsten Thormählen, P. Pérez, C. Theobalt

{"title":"Automatic Face Reenactment","authors":"Pablo Garrido, Levi Valgaerts, Ole Rehmsen, Thorsten Thormählen, P. Pérez, C. Theobalt","doi":"10.1109/CVPR.2014.537","DOIUrl":"https://doi.org/10.1109/CVPR.2014.537","url":null,"abstract":"We propose an image-based, facial reenactment system that replaces the face of an actor in an existing target video with the face of a user from a source video, while preserving the original target performance. Our system is fully automatic and does not require a database of source expressions. Instead, it is able to produce convincing reenactment results from a short source video captured with an off-the-shelf camera, such as a webcam, where the user performs arbitrary facial gestures. Our reenactment pipeline is conceived as part image retrieval and part face transfer: The image retrieval is based on temporal clustering of target frames and a novel image matching metric that combines appearance and motion to select candidate frames from the source video, while the face transfer uses a 2D warping strategy that preserves the user's identity. Our system excels in simplicity as it does not rely on a 3D face model, it is robust under head motion and does not require the source and target performance to be similar. We show convincing reenactment results for videos that we recorded ourselves and for low-quality footage taken from the Internet.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125569146","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: 157

Noising versus Smoothing for Vertex Identification in Unknown Shapes 噪声与平滑在未知形状顶点识别中的应用

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.530

Konstantinos A. Raftopoulos, Marin Ferecatu

引用次数: 7

Partial Occlusion Handling for Visual Tracking via Robust Part Matching 基于鲁棒部分匹配的视觉跟踪局部遮挡处理

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.164

Tianzhu Zhang, K. Jia, Changsheng Xu, Yi Ma, N. Ahuja

{"title":"Partial Occlusion Handling for Visual Tracking via Robust Part Matching","authors":"Tianzhu Zhang, K. Jia, Changsheng Xu, Yi Ma, N. Ahuja","doi":"10.1109/CVPR.2014.164","DOIUrl":"https://doi.org/10.1109/CVPR.2014.164","url":null,"abstract":"Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices. The proposed part matching tracker (PMT) has a number of attractive properties. (1) It exploits the spatial-temporal locality-constrained property for robust part matching. (2) It matches local parts from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle part appearance variations due to occlusion or noise. (3) The proposed PMT model has the inbuilt mechanism of leveraging multi-mode target templates, so that the dilemma of template updating when encountering occlusion in tracking can be better handled. This contrasts with existing methods that only do part matching between a pair of frames. We evaluate PMT and compare with 10 popular state-of-the-art methods on challenging benchmarks. Experimental results show that PMT consistently outperform these existing trackers.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"539 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116245469","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: 117

Joint Depth Estimation and Camera Shake Removal from Single Blurry Image 单幅模糊图像的联合深度估计和相机抖动去除

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.370

Zhe Hu, Li Xu, Ming-Hsuan Yang

{"title":"Joint Depth Estimation and Camera Shake Removal from Single Blurry Image","authors":"Zhe Hu, Li Xu, Ming-Hsuan Yang","doi":"10.1109/CVPR.2014.370","DOIUrl":"https://doi.org/10.1109/CVPR.2014.370","url":null,"abstract":"Camera shake during exposure time often results in spatially variant blur effect of the image. The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene. The objects close to the camera sensors are likely to appear more blurry than those at a distance in such cases. However, recent non-uniform deblurring methods do not explicitly consider the depth factor or assume fronto-parallel scenes with constant depth for simplicity. While single image non-uniform deblurring is a challenging problem, the blurry results in fact contain depth information which can be exploited. We propose to jointly estimate scene depth and remove non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with only single blurry image as input. To this end, we present a unified layer-based model for depth-involved deblurring. We provide a novel layer-based solution using matting to partition the layers and an expectation-maximization scheme to solve this problem. This approach largely reduces the number of unknowns and makes the problem tractable. Experiments on challenging examples demonstrate that both depth and camera shake removal can be well addressed within the unified framework.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122852688","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: 73

Learning Non-linear Reconstruction Models for Image Set Classification 学习用于图像集分类的非线性重建模型

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.246

Munawar Hayat, Bennamoun, S. An

引用次数: 72

Calibrating a Non-isotropic Near Point Light Source Using a Plane 用平面标定非各向同性近点光源

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.290

Jaesik Park, Sudipta N. Sinha, Y. Matsushita, Yu-Wing Tai, In-So Kweon

引用次数: 31

Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking 多样性增强凝聚算法及其在内窥镜三维运动跟踪中的应用

2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.163

Xióngbiao Luó, Ying Wan, Xiangjian He, Jie Yang, K. Mori

引用次数: 4