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2014 IEEE Conference on Computer Vision and Pattern Recognition最新文献

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3D Modeling from Wide Baseline Range Scans Using Contour Coherence 使用轮廓一致性从宽基线范围扫描的3D建模
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.513
Ruizhe Wang, Jongmoo Choi, G. Medioni
{"title":"3D Modeling from Wide Baseline Range Scans Using Contour Coherence","authors":"Ruizhe Wang, Jongmoo Choi, G. Medioni","doi":"10.1109/CVPR.2014.513","DOIUrl":"https://doi.org/10.1109/CVPR.2014.513","url":null,"abstract":"Registering 2 or more range scans is a fundamental problem, with application to 3D modeling. While this problem is well addressed by existing techniques such as ICP when the views overlap significantly at a good initialization, no satisfactory solution exists for wide baseline registration. We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization. Inspired by ICP, we maximize the contour coherence by building robust corresponding pairs on apparent contours and minimizing their distances in an iterative fashion. We use the contour coherence under a multi-view rigid registration framework, and this enables the reconstruction of accurate and complete 3D models from as few as 4 frames. We further extend it to handle articulations, and this allows us to model articulated objects such as human body. Experimental results on both synthetic and real data demonstrate the effectiveness and robustness of our contour coherence based registration approach to wide baseline range scans, and to 3D modeling.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"2013 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":"132016501","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
Super Normal Vector for Activity Recognition Using Depth Sequences 利用深度序列进行活动识别的超常规向量
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.108
Xiaodong Yang, Yingli Tian
{"title":"Super Normal Vector for Activity Recognition Using Depth Sequences","authors":"Xiaodong Yang, Yingli Tian","doi":"10.1109/CVPR.2014.108","DOIUrl":"https://doi.org/10.1109/CVPR.2014.108","url":null,"abstract":"This paper presents a new framework for human activity recognition from video sequences captured by a depth camera. We cluster hypersurface normals in a depth sequence to form the polynormal which is used to jointly characterize the local motion and shape information. In order to globally capture the spatial and temporal orders, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time grids. We then propose a novel scheme of aggregating the low-level polynormals into the super normal vector (SNV) which can be seen as a simplified version of the Fisher kernel representation. In the extensive experiments, we achieve classification results superior to all previous published results on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 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":"130286255","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: 373
A Hierarchical Probabilistic Model for Facial Feature Detection 一种用于人脸特征检测的层次概率模型
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.230
Yue Wu, Ziheng Wang, Q. Ji
{"title":"A Hierarchical Probabilistic Model for Facial Feature Detection","authors":"Yue Wu, Ziheng Wang, Q. Ji","doi":"10.1109/CVPR.2014.230","DOIUrl":"https://doi.org/10.1109/CVPR.2014.230","url":null,"abstract":"Facial feature detection from facial images has attracted great attention in the field of computer vision. It is a nontrivial task since the appearance and shape of the face tend to change under different conditions. In this paper, we propose a hierarchical probabilistic model that could infer the true locations of facial features given the image measurements even if the face is with significant facial expression and pose. The hierarchical model implicitly captures the lower level shape variations of facial components using the mixture model. Furthermore, in the higher level, it also learns the joint relationship among facial components, the facial expression, and the pose information through automatic structure learning and parameter estimation of the probabilistic model. Experimental results on benchmark databases demonstrate the effectiveness of the proposed hierarchical probabilistic model.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 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":"130459559","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
Hierarchical Feature Hashing for Fast Dimensionality Reduction 快速降维的分层特征哈希
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.263
Bin Zhao, E. Xing
{"title":"Hierarchical Feature Hashing for Fast Dimensionality Reduction","authors":"Bin Zhao, E. Xing","doi":"10.1109/CVPR.2014.263","DOIUrl":"https://doi.org/10.1109/CVPR.2014.263","url":null,"abstract":"Curse of dimensionality is a practical and challenging problem in image categorization, especially in cases with a large number of classes. Multi-class classification encounters severe computational and storage problems when dealing with these large scale tasks. In this paper, we propose hierarchical feature hashing to effectively reduce dimensionality of parameter space without sacrificing classification accuracy, and at the same time exploit information in semantic taxonomy among categories. We provide detailed theoretical analysis on our proposed hashing method. Moreover, experimental results on object recognition and scene classification further demonstrate the effectiveness of hierarchical feature hashing.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"28 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":"130488128","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: 11
Scene-Independent Group Profiling in Crowd 人群中与场景无关的群体侧写
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.285
Jing Shao, Chen Change Loy, Xiaogang Wang
{"title":"Scene-Independent Group Profiling in Crowd","authors":"Jing Shao, Chen Change Loy, Xiaogang Wang","doi":"10.1109/CVPR.2014.285","DOIUrl":"https://doi.org/10.1109/CVPR.2014.285","url":null,"abstract":"Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding. In this study we show that fundamental group-level properties, such as intra-group stability and inter-group conflict, can be systematically quantified by visual descriptors. This is made possible through learning a novel Collective Transition prior, which leads to a robust approach for group segregation in public spaces. From the prior, we further devise a rich set of group property visual descriptors. These descriptors are scene-independent, and can be effectively applied to public-scene with variety of crowd densities and distributions. Extensive experiments on hundreds of public scene video clips demonstrate that such property descriptors are not only useful but also necessary for group state analysis and crowd scene understanding.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 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":"130497876","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: 232
Local Layering for Joint Motion Estimation and Occlusion Detection 局部分层用于关节运动估计和遮挡检测
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.144
Deqing Sun, Ce Liu, H. Pfister
{"title":"Local Layering for Joint Motion Estimation and Occlusion Detection","authors":"Deqing Sun, Ce Liu, H. Pfister","doi":"10.1109/CVPR.2014.144","DOIUrl":"https://doi.org/10.1109/CVPR.2014.144","url":null,"abstract":"Most motion estimation algorithms (optical flow, layered models) cannot handle large amount of occlusion in textureless regions, as motion is often initialized with no occlusion assumption despite that occlusion may be included in the final objective. To handle such situations, we propose a local layering model where motion and occlusion relationships are inferred jointly. In particular, the uncertainties of occlusion relationships are retained so that motion is inferred by considering all the possibilities of local occlusion relationships. In addition, the local layering model handles articulated objects with self-occlusion. We demonstrate that the local layering model can handle motion and occlusion well for both challenging synthetic and real sequences.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"80 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":"129179155","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: 66
Orientational Pyramid Matching for Recognizing Indoor Scenes 面向金字塔匹配的室内场景识别
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.477
Lingxi Xie, Jingdong Wang, B. Guo, Bo Zhang, Qi Tian
{"title":"Orientational Pyramid Matching for Recognizing Indoor Scenes","authors":"Lingxi Xie, Jingdong Wang, B. Guo, Bo Zhang, Qi Tian","doi":"10.1109/CVPR.2014.477","DOIUrl":"https://doi.org/10.1109/CVPR.2014.477","url":null,"abstract":"Scene recognition is a basic task towards image understanding. Spatial Pyramid Matching (SPM) has been shown to be an efficient solution for spatial context modeling. In this paper, we introduce an alternative approach, Orientational Pyramid Matching (OPM), for orientational context modeling. Our approach is motivated by the observation that the 3D orientations of objects are a crucial factor to discriminate indoor scenes. The novelty lies in that OPM uses the 3D orientations to form the pyramid and produce the pooling regions, which is unlike SPM that uses the spatial positions to form the pyramid. Experimental results on challenging scene classification tasks show that OPM achieves the performance comparable with SPM and that OPM and SPM make complementary contributions so that their combination gives the state-of-the-art performance.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"20 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":"125362704","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: 49
Histograms of Pattern Sets for Image Classification and Object Recognition 用于图像分类和目标识别的模式集直方图
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.36
Winn Voravuthikunchai, B. Crémilleux, F. Jurie
{"title":"Histograms of Pattern Sets for Image Classification and Object Recognition","authors":"Winn Voravuthikunchai, B. Crémilleux, F. Jurie","doi":"10.1109/CVPR.2014.36","DOIUrl":"https://doi.org/10.1109/CVPR.2014.36","url":null,"abstract":"This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. The method relies on (i) multiple random projections of the input space followed by local binarization of projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of Pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, KTH Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each one of these datasets.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"2008 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":"125602196","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: 29
From Stochastic Grammar to Bayes Network: Probabilistic Parsing of Complex Activity 从随机语法到贝叶斯网络:复杂活动的概率解析
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.338
Nam N. Vo, A. Bobick
{"title":"From Stochastic Grammar to Bayes Network: Probabilistic Parsing of Complex Activity","authors":"Nam N. Vo, A. Bobick","doi":"10.1109/CVPR.2014.338","DOIUrl":"https://doi.org/10.1109/CVPR.2014.338","url":null,"abstract":"We propose a probabilistic method for parsing a temporal sequence such as a complex activity defined as composition of sub-activities/actions. The temporal structure of the high-level activity is represented by a string-length limited stochastic context-free grammar. Given the grammar, a Bayes network, which we term Sequential Interval Network (SIN), is generated where the variable nodes correspond to the start and end times of component actions. The network integrates information about the duration of each primitive action, visual detection results for each primitive action, and the activity's temporal structure. At any moment in time during the activity, message passing is used to perform exact inference yielding the posterior probabilities of the start and end times for each different activity/action. We provide demonstrations of this framework being applied to vision tasks such as action prediction, classification of the high-level activities or temporal segmentation of a test sequence, the method is also applicable in Human Robot Interaction domain where continual prediction of human action is needed.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"15 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":"126692521","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: 81
Good Vibrations: A Modal Analysis Approach for Sequential Non-rigid Structure from Motion 良好振动:序列非刚性结构的运动模态分析方法
2014 IEEE Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-23 DOI: 10.1109/CVPR.2014.202
Antonio Agudo, L. Agapito, B. Calvo, J. Montiel
{"title":"Good Vibrations: A Modal Analysis Approach for Sequential Non-rigid Structure from Motion","authors":"Antonio Agudo, L. Agapito, B. Calvo, J. Montiel","doi":"10.1109/CVPR.2014.202","DOIUrl":"https://doi.org/10.1109/CVPR.2014.202","url":null,"abstract":"We propose an online solution to non-rigid structure from motion that performs camera pose and 3D shape estimation of highly deformable surfaces on a frame-by-frame basis. Our method models non-rigid deformations as a linear combination of some mode shapes obtained using modal analysis from continuum mechanics. The shape is first discretized into linear elastic triangles, modelled by means of finite elements, which are used to pose the force balance equations for an undamped free vibrations model. The shape basis computation comes down to solving an eigenvalue problem, without the requirement of a learning step. The camera pose and time varying weights that define the shape at each frame are then estimated on the fly, in an online fashion, using bundle adjustment over a sliding window of image frames. The result is a low computational cost method that can run sequentially in real-time. We show experimental results on synthetic sequences with ground truth 3D data and real videos for different scenarios ranging from sparse to dense scenes. Our system exhibits a good trade-off between accuracy and computational budget, it can handle missing data and performs favourably compared to competing methods.","PeriodicalId":319578,"journal":{"name":"2014 IEEE Conference on Computer Vision and Pattern Recognition","volume":"11 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":"126852003","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
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