2017 IEEE International Conference on Computer Vision (ICCV)最新文献_第9页

Robust Hand Pose Estimation during the Interaction with an Unknown Object 与未知物体交互过程中手部姿态的鲁棒估计

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.339

Chiho Choi, S. Yoon, China Chen, K. Ramani

引用次数: 53

Monocular Free-Head 3D Gaze Tracking with Deep Learning and Geometry Constraints 基于深度学习和几何约束的单目自由头3D凝视跟踪

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.341

Haoping Deng, Wangjiang Zhu

{"title":"Monocular Free-Head 3D Gaze Tracking with Deep Learning and Geometry Constraints","authors":"Haoping Deng, Wangjiang Zhu","doi":"10.1109/ICCV.2017.341","DOIUrl":"https://doi.org/10.1109/ICCV.2017.341","url":null,"abstract":"Free-head 3D gaze tracking outputs both the eye location and the gaze vector in 3D space, and it has wide applications in scenarios such as driver monitoring, advertisement analysis and surveillance. A reliable and low-cost monocular solution is critical for pervasive usage in these areas. Noticing that a gaze vector is a composition of head pose and eyeball movement in a geometrically deterministic way, we propose a novel gaze transform layer to connect separate head pose and eyeball movement models. The proposed decomposition does not suffer from head-gaze correlation overfitting and makes it possible to use datasets existing for other tasks. To add stronger supervision for better network training, we propose a two-step training strategy, which first trains sub-tasks with rough labels and then jointly trains with accurate gaze labels. To enable good cross-subject performance under various conditions, we collect a large dataset which has full coverage of head poses and eyeball movements, contains 200 subjects, and has diverse illumination conditions. Our deep solution achieves state-of-the-art gaze tracking accuracy, reaching 5.6° cross-subject prediction error using a small network running at 1000 fps on a single CPU (excluding face alignment time) and 4.3° cross-subject error with a deeper network.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"6 1","pages":"3162-3171"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91169621","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: 107

Surface Registration via Foliation 通过叶理进行表面配准

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.107

Xiaopeng Zheng, Chengfeng Wen, Na Lei, Ming Ma, X. Gu

引用次数: 8

PolyFit: Polygonal Surface Reconstruction from Point Clouds PolyFit:从点云的多边形表面重建

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.258

L. Nan, Peter Wonka

{"title":"PolyFit: Polygonal Surface Reconstruction from Point Clouds","authors":"L. Nan, Peter Wonka","doi":"10.1109/ICCV.2017.258","DOIUrl":"https://doi.org/10.1109/ICCV.2017.258","url":null,"abstract":"We propose a novel framework for reconstructing lightweight polygonal surfaces from point clouds. Unlike traditional methods that focus on either extracting good geometric primitives or obtaining proper arrangements of primitives, the emphasis of this work lies in intersecting the primitives (planes only) and seeking for an appropriate combination of them to obtain a manifold polygonal surface model without boundary.,,We show that reconstruction from point clouds can be cast as a binary labeling problem. Our method is based on a hypothesizing and selection strategy. We first generate a reasonably large set of face candidates by intersecting the extracted planar primitives. Then an optimal subset of the candidate faces is selected through optimization. Our optimization is based on a binary linear programming formulation under hard constraints that enforce the final polygonal surface model to be manifold and watertight. Experiments on point clouds from various sources demonstrate that our method can generate lightweight polygonal surface models of arbitrary piecewise planar objects. Besides, our method is capable of recovering sharp features and is robust to noise, outliers, and missing data.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"34 1","pages":"2372-2380"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83117546","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: 152

Shadow Detection with Conditional Generative Adversarial Networks 基于条件生成对抗网络的阴影检测

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.483

Vu Nguyen, Tomas F. Yago Vicente, Maozheng Zhao, Minh Hoai, D. Samaras

{"title":"Shadow Detection with Conditional Generative Adversarial Networks","authors":"Vu Nguyen, Tomas F. Yago Vicente, Maozheng Zhao, Minh Hoai, D. Samaras","doi":"10.1109/ICCV.2017.483","DOIUrl":"https://doi.org/10.1109/ICCV.2017.483","url":null,"abstract":"We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images. Previous methods for shadow detection focus on learning the local appearance of shadow regions, while using limited local context reasoning in the form of pairwise potentials in a Conditional Random Field. In contrast, the proposed adversarial approach is able to model higher level relationships and global scene characteristics. We train a shadow detector that corresponds to the generator of a conditional GAN, and augment its shadow accuracy by combining the typical GAN loss with a data loss term. Due to the unbalanced distribution of the shadow labels, we use weighted cross entropy. With the standard GAN architecture, properly setting the weight for the cross entropy would require training multiple GANs, a computationally expensive grid procedure. In scGAN, we introduce an additional sensitivity parameter w to the generator. The proposed approach effectively parameterizes the loss of the trained detector. The resulting shadow detector is a single network that can generate shadow maps corresponding to different sensitivity levels, obviating the need for multiple models and a costly training procedure. We evaluate our method on the large-scale SBU and UCF shadow datasets, and observe up to 17% error reduction with respect to the previous state-of-the-art method.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"18 1","pages":"4520-4528"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83463610","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: 156

Transformed Low-Rank Model for Line Pattern Noise Removal 变换的低秩线性模式去噪

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.191

Yi Chang, Luxin Yan, Sheng Zhong

{"title":"Transformed Low-Rank Model for Line Pattern Noise Removal","authors":"Yi Chang, Luxin Yan, Sheng Zhong","doi":"10.1109/ICCV.2017.191","DOIUrl":"https://doi.org/10.1109/ICCV.2017.191","url":null,"abstract":"This paper addresses the problem of line pattern noise removal from a single image, such as rain streak, hyperspectral stripe and so on. Most of the previous methods model the line pattern noise in original image domain, which fail to explicitly exploit the directional characteristic, thus resulting in a redundant subspace with poor representation ability for those line pattern noise. To achieve a compact subspace for the line pattern structure, in this work, we incorporate a transformation into the image decomposition model so that maps the input image to a domain where the line pattern appearance has an extremely distinct low-rank structure, which naturally allows us to enforce a low-rank prior to extract the line pattern streak/stripe from the noisy image. Moreover, the random noise is usually mixed up with the line pattern noise, which makes the challenging problem much more difficult. While previous methods resort to the spectral or temporal correlation of the multi-images, we give a detailed analysis between the noisy and clean image in both local gradient and nonlocal domain, and propose a compositional directional total variational and low-rank prior for the image layer, thus to simultaneously accommodate both types of noise. The proposed method has been evaluated on two different tasks, including remote sensing image mixed random-stripe noise removal and rain streak removal, all of which obtain very impressive performances.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"649 1","pages":"1735-1743"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77670966","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: 125

Image Super-Resolution Using Dense Skip Connections 使用密集跳过连接的图像超分辨率

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.514

T. Tong, Gen Li, Xiejie Liu, Qinquan Gao

引用次数: 989

High Order Tensor Formulation for Convolutional Sparse Coding 卷积稀疏编码的高阶张量公式

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.197

Adel Bibi, Bernard Ghanem

引用次数: 22

TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal 龙卷风:一个时空卷积回归网络的视频动作建议

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.619

Hongyuan Zhu, Romain Vial, Shijian Lu

{"title":"TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal","authors":"Hongyuan Zhu, Romain Vial, Shijian Lu","doi":"10.1109/ICCV.2017.619","DOIUrl":"https://doi.org/10.1109/ICCV.2017.619","url":null,"abstract":"Given a video clip, action proposal aims to quickly generate a number of spatio-temporal tubes that enclose candidate human activities. Recently, the regression-based networks and long-term recurrent convolutional network (L-RCN) have demonstrated superior performance in object detection and action recognition. However, the regression-based detectors perform inference without considering the temporal context among neighboring frames, and the LRC-N using global visual percepts lacks the capability to capture local temporal dynamics. In this paper, we present a novel framework called TORNADO for human action proposal detection in un-trimmed video clips. Specifically, we propose a spatio-temporal convolutional network that combines the advantages of regression-based detector and L-RCN by empowering Convolutional LSTM with regression capability. Our approach consists of a temporal convolutional regression network (T-CRN) and a spatial regression network (S-CRN) which are trained end-to-end on both RGB and optical flow streams. They fuse appearance, motion and temporal contexts to regress the bounding boxes of candidate human actions simultaneously in 28 FPS. The action proposals are constructed by solving dynamic programming with peak trimming of the generated action boxes. Extensive experiments on the challenging UCF-101 and UCF-Sports datasets show that our method achieves superior performance as compared with the state-of-the-arts.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"35 1","pages":"5814-5822"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77093847","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: 56

Space-Time Localization and Mapping 时空定位与映射

2017 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2017-10-01 DOI: 10.1109/ICCV.2017.422

Minhaeng Lee, Charless C. Fowlkes

引用次数: 4