2015 IEEE International Conference on Computer Vision (ICCV)最新文献_第8页

RGB-Guided Hyperspectral Image Upsampling rgb制导高光谱图像上采样

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.43

HyeokHyen Kwon, Yu-Wing Tai

引用次数: 49

Contractive Rectifier Networks for Nonlinear Maximum Margin Classification 非线性最大余量分类的收缩整流网络

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.289

S. An, Munawar Hayat, S. H. Khan, Bennamoun, F. Boussaïd, Ferdous Sohel

引用次数: 11

Towards Pointless Structure from Motion: 3D Reconstruction and Camera Parameters from General 3D Curves 从运动走向无意义的结构:从一般3D曲线的3D重建和相机参数

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.272

Irina Nurutdinova, A. Fitzgibbon

引用次数: 39

Multiple Hypothesis Tracking Revisited 重新审视多重假设跟踪

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.533

Chanho Kim, Fuxin Li, A. Ciptadi, James M. Rehg

引用次数: 571

Airborne Three-Dimensional Cloud Tomography 航空三维云层析成像

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.386

Aviad Levis, Y. Schechner, Amit Aides, A. Davis

引用次数: 69

The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms HCI立体度量:立体算法的几何感知性能分析

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.245

Katrin Honauer, L. Maier-Hein, D. Kondermann

{"title":"The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms","authors":"Katrin Honauer, L. Maier-Hein, D. Kondermann","doi":"10.1109/ICCV.2015.245","DOIUrl":"https://doi.org/10.1109/ICCV.2015.245","url":null,"abstract":"Performance characterization of stereo methods is mandatory to decide which algorithm is useful for which application. Prevalent benchmarks mainly use the root mean squared error (RMS) with respect to ground truth disparity maps to quantify algorithm performance. We show that the RMS is of limited expressiveness for algorithm selection and introduce the HCI Stereo Metrics. These metrics assess stereo results by harnessing three semantic cues: depth discontinuities, planar surfaces, and fine geometric structures. For each cue, we extract the relevant set of pixels from existing ground truth. We then apply our evaluation functions to quantify characteristics such as edge fattening and surface smoothness. We demonstrate that our approach supports practitioners in selecting the most suitable algorithm for their application. Using the new Middlebury dataset, we show that rankings based on our metrics reveal specific algorithm strengths and weaknesses which are not quantified by existing metrics. We finally show how stacked bar charts and radar charts visually support multidimensional performance evaluation. An interactive stereo benchmark based on the proposed metrics and visualizations is available at: http://hci.iwr.uni-heidelberg.de/stereometrics.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"30 1","pages":"2120-2128"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81434092","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: 21

Query Adaptive Similarity Measure for RGB-D Object Recognition RGB-D对象识别的查询自适应相似度度量

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.25

Yanhua Cheng, Rui Cai, Chi Zhang, Zhiwei Li, Xin Zhao, Kaiqi Huang, Y. Rui

{"title":"Query Adaptive Similarity Measure for RGB-D Object Recognition","authors":"Yanhua Cheng, Rui Cai, Chi Zhang, Zhiwei Li, Xin Zhao, Kaiqi Huang, Y. Rui","doi":"10.1109/ICCV.2015.25","DOIUrl":"https://doi.org/10.1109/ICCV.2015.25","url":null,"abstract":"This paper studies the problem of improving the top-1 accuracy of RGB-D object recognition. Despite of the impressive top-5 accuracies achieved by existing methods, their top-1 accuracies are not very satisfactory. The reasons are in two-fold: (1) existing similarity measures are sensitive to object pose and scale changes, as well as intra-class variations, and (2) effectively fusing RGB and depth cues is still an open problem. To address these problems, this paper first proposes a new similarity measure based on dense matching, through which objects in comparison are warped and aligned, to better tolerate variations. Towards RGB and depth fusion, we argue that a constant and golden weight doesn't exist. The two modalities have varying contributions when comparing objects from different categories. To capture such a dynamic characteristic, a group of matchers equipped with various fusion weights is constructed, to explore the responses of dense matching under different fusion configurations. All the response scores are finally merged following a learning-to-combination way, which provides quite good generalization ability in practice. The proposed approach win the best results on several public benchmarks, e.g., achieves 92.7% top-1 test accuracy on the Washington RGB-D object dataset, with a 5.1% improvement over the state-of-the-art.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"12 1","pages":"145-153"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83881035","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: 12

Motion Trajectory Segmentation via Minimum Cost Multicuts 基于最小成本多路分割的运动轨迹分割

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.374

M. Keuper, Bjoern Andres, T. Brox

引用次数: 188

Robust Image Segmentation Using Contour-Guided Color Palettes 使用轮廓引导调色板的鲁棒图像分割

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.189

Xiang Fu, Chien-Yi Wang, Chen Chen, Changhu Wang, C.-C. Jay Kuo

引用次数: 25

Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose 打开黑箱:手部姿态估计的分层采样优化

2015 IEEE International Conference on Computer Vision (ICCV) Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.380

Danhang Tang, Jonathan Taylor, Pushmeet Kohli, Cem Keskin, Tae-Kyun Kim, J. Shotton

{"title":"Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose","authors":"Danhang Tang, Jonathan Taylor, Pushmeet Kohli, Cem Keskin, Tae-Kyun Kim, J. Shotton","doi":"10.1109/ICCV.2015.380","DOIUrl":"https://doi.org/10.1109/ICCV.2015.380","url":null,"abstract":"We address the problem of hand pose estimation, formulated as an inverse problem. Typical approaches optimize an energy function over pose parameters using a 'black box' image generation procedure. This procedure knows little about either the relationships between the parameters or the form of the energy function. In this paper, we show that we can significantly improving upon black box optimization by exploiting high-level knowledge of the structure of the parameters and using a local surrogate energy function. Our new framework, hierarchical sampling optimization, consists of a sequence of predictors organized into a kinematic hierarchy. Each predictor is conditioned on its ancestors, and generates a set of samples over a subset of the pose parameters. The highly-efficient surrogate energy is used to select among samples. Having evaluated the full hierarchy, the partial pose samples are concatenated to generate a full-pose hypothesis. Several hypotheses are generated using the same procedure, and finally the original full energy function selects the best result. Experimental evaluation on three publically available datasets show that our method is particularly impressive in low-compute scenarios where it significantly outperforms all other state-of-the-art methods.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"11 1","pages":"3325-3333"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90270774","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: 144