2015 IEEE International Conference on Computer Vision (ICCV)最新文献_第10页

Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-view Stereo 大尺度多视点立体联合相机聚类与曲面分割

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.241

Runze Zhang, Shiwei Li, Tian Fang, Siyu Zhu, Long Quan

{"title":"Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-view Stereo","authors":"Runze Zhang, Shiwei Li, Tian Fang, Siyu Zhu, Long Quan","doi":"10.1109/ICCV.2015.241","DOIUrl":"https://doi.org/10.1109/ICCV.2015.241","url":null,"abstract":"In this paper, we propose an optimal decomposition approach to large-scale multi-view stereo from an initial sparse reconstruction. The success of the approach depends on the introduction of surface-segmentation-based camera clustering rather than sparse-point-based camera clustering, which suffers from the problems of non-uniform reconstruction coverage ratio and high redundancy. In details, we introduce three criteria for camera clustering and surface segmentation for reconstruction, and then we formulate these criteria into an energy minimization problem under constraints. To solve this problem, we propose a joint optimization in a hierarchical framework to obtain the final surface segments and corresponding optimal camera clusters. On each level of the hierarchical framework, the camera clustering problem is formulated as a parameter estimation problem of a probability model solved by a General Expectation-Maximization algorithm and the surface segmentation problem is formulated as a Markov Random Field model based on the probability estimated by the previous camera clustering process. The experiments on several Internet datasets and aerial photo datasets demonstrate that the proposed approach method generates more uniform and complete dense reconstruction with less redundancy, resulting in more efficient multi-view stereo algorithm.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"82 1","pages":"2084-2092"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83392891","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: 24

Discrete Tabu Search for Graph Matching 图匹配的离散禁忌搜索

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.21

Kamil Adamczewski, Yumin Suh, Kyoung Mu Lee

引用次数: 49

Learning Large-Scale Automatic Image Colorization 学习大规模自动图像着色

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.72

A. Deshpande, Jason Rock, D. Forsyth

引用次数: 206

Class-Specific Image Deblurring 特定类的图像去模糊

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.64

Saeed Anwar, C. P. Huynh, F. Porikli

引用次数: 33

Globally Optimal 2D-3D Registration from Points or Lines without Correspondences 全局最优的2D-3D配准从点或线没有对应

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.244

Mark Brown, David Windridge, Jean-Yves Guillemaut

引用次数: 30

FaceDirector: Continuous Control of Facial Performance in Video FaceDirector:在视频中连续控制面部表现

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.453

Charles Malleson, J. Bazin, Oliver Wang, D. Bradley, T. Beeler, A. Hilton, A. Sorkine-Hornung

{"title":"FaceDirector: Continuous Control of Facial Performance in Video","authors":"Charles Malleson, J. Bazin, Oliver Wang, D. Bradley, T. Beeler, A. Hilton, A. Sorkine-Hornung","doi":"10.1109/ICCV.2015.453","DOIUrl":"https://doi.org/10.1109/ICCV.2015.453","url":null,"abstract":"We present a method to continuously blend between multiple facial performances of an actor, which can contain different facial expressions or emotional states. As an example, given sad and angry video takes of a scene, our method empowers the movie director to specify arbitrary weighted combinations and smooth transitions between the two takes in post-production. Our contributions include (1) a robust nonlinear audio-visual synchronization technique that exploits complementary properties of audio and visual cues to automatically determine robust, dense spatiotemporal correspondences between takes, and (2) a seamless facial blending approach that provides the director full control to interpolate timing, facial expression, and local appearance, in order to generate novel performances after filming. In contrast to most previous works, our approach operates entirely in image space, avoiding the need of 3D facial reconstruction. We demonstrate that our method can synthesize visually believable performances with applications in emotion transition, performance correction, and timing control.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"3 1","pages":"3979-3987"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85236653","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: 18

Exploiting High Level Scene Cues in Stereo Reconstruction 利用立体重建中的高级场景线索

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.96

Simon Hadfield, R. Bowden

引用次数: 7

From Emotions to Action Units with Hidden and Semi-Hidden-Task Learning 从情绪到行动单位，隐藏和半隐藏任务学习

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.422

Adria Ruiz, Joost van de Weijer, Xavier Binefa

{"title":"From Emotions to Action Units with Hidden and Semi-Hidden-Task Learning","authors":"Adria Ruiz, Joost van de Weijer, Xavier Binefa","doi":"10.1109/ICCV.2015.422","DOIUrl":"https://doi.org/10.1109/ICCV.2015.422","url":null,"abstract":"Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units) for which samples are not available but, in contrast, training data is easier to obtain from a set of related Visible-Tasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"7 1","pages":"3703-3711"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87437985","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: 58

Context-Guided Diffusion for Label Propagation on Graphs 图上标签传播的上下文引导扩散

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.318

K. Kim, J. Tompkin, H. Pfister, C. Theobalt

引用次数: 13

Dual-Feature Warping-Based Motion Model Estimation 基于双特征翘曲的运动模型估计

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.487

Shiwei Li, Lu Yuan, Jian Sun, Long Quan

{"title":"Dual-Feature Warping-Based Motion Model Estimation","authors":"Shiwei Li, Lu Yuan, Jian Sun, Long Quan","doi":"10.1109/ICCV.2015.487","DOIUrl":"https://doi.org/10.1109/ICCV.2015.487","url":null,"abstract":"To break down the geometry assumptions of traditional motion models (e.g., homography, affine), warping-based motion model recently becomes very popular and is adopted in many latest applications (e.g., image stitching, video stabilization). With high degrees of freedom, the accuracy of model heavily relies on data-terms (keypoint correspondences). In some low-texture environments (e.g., indoor) where keypoint feature is insufficient or unreliable, the warping model is often erroneously estimated. In this paper we propose a simple and effective approach by considering both keypoint and line segment correspondences as data-term. Line segment is a prominent feature in artificial environments and it can supply sufficient geometrical and structural information of scenes, which not only helps guild to a correct warp in low-texture condition, but also prevents the undesired distortion induced by warping. The combination aims to complement each other and benefit for a wider range of scenes. Our method is general and can be ported to many existing applications. Experiments demonstrate that using dual-feature yields more robust and accurate result especially for those low-texture images.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"49 6","pages":"4283-4291"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91435873","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: 88