2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第5页

An approximate shading model for object relighting 物体重照明的近似着色模型

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299168

Zicheng Liao, Kevin Karsch, D. Forsyth

引用次数: 15

Shape-based automatic detection of a large number of 3D facial landmarks 基于形状的大量三维人脸标志自动检测

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299095

S. Z. Gilani, F. Shafait, A. Mian

{"title":"Shape-based automatic detection of a large number of 3D facial landmarks","authors":"S. Z. Gilani, F. Shafait, A. Mian","doi":"10.1109/CVPR.2015.7299095","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7299095","url":null,"abstract":"We present an algorithm for automatic detection of a large number of anthropometric landmarks on 3D faces. Our approach does not use texture and is completely shape based in order to detect landmarks that are morphologically significant. The proposed algorithm evolves level set curves with adaptive geometric speed functions to automatically extract effective seed points for dense correspondence. Correspondences are established by minimizing the bending energy between patches around seed points of given faces to those of a reference face. Given its hierarchical structure, our algorithm is capable of establishing thousands of correspondences between a large number of faces. Finally, a morphable model based on the dense corresponding points is fitted to an unseen query face for transfer of correspondences and hence automatic detection of landmarks. The proposed algorithm can detect any number of pre-defined landmarks including subtle landmarks that are even difficult to detect manually. Extensive experimental comparison on two benchmark databases containing 6, 507 scans shows that our algorithm outperforms six state of the art algorithms.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116159905","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: 60

Elastic functional coding of human actions: From vector-fields to latent variables 人类行为的弹性功能编码:从向量场到潜在变量

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298934

Rushil Anirudh, P. Turaga, Jingyong Su, Anuj Srivastava

{"title":"Elastic functional coding of human actions: From vector-fields to latent variables","authors":"Rushil Anirudh, P. Turaga, Jingyong Su, Anuj Srivastava","doi":"10.1109/CVPR.2015.7298934","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7298934","url":null,"abstract":"Human activities observed from visual sensors often give rise to a sequence of smoothly varying features. In many cases, the space of features can be formally defined as a manifold, where the action becomes a trajectory on the manifold. Such trajectories are high dimensional in addition to being non-linear, which can severely limit computations on them. We also argue that by their nature, human actions themselves lie on a much lower dimensional manifold compared to the high dimensional feature space. Learning an accurate low dimensional embedding for actions could have a huge impact in the areas of efficient search and retrieval, visualization, learning, and recognition. Traditional manifold learning addresses this problem for static points in ℝn, but its extension to trajectories on Riemannian manifolds is non-trivial and has remained unexplored. The challenge arises due to the inherent non-linearity, and temporal variability that can significantly distort the distance metric between trajectories. To address these issues we use the transport square-root velocity function (TSRVF) space, a recently proposed representation that provides a metric which has favorable theoretical properties such as invariance to group action. We propose to learn the low dimensional embedding with a manifold functional variant of principal component analysis (mfPCA). We show that mf-PCA effectively models the manifold trajectories in several applications such as action recognition, clustering and diverse sequence sampling while reducing the dimensionality by a factor of ~ 250×. The mfPCA features can also be reconstructed back to the original manifold to allow for easy visualization of the latent variable space.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116450349","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: 93

Regularizing max-margin exemplars by reconstruction and generative models 基于重构和生成模型的最大边际样本正则化

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299049

José C. Rubio, B. Ommer

{"title":"Regularizing max-margin exemplars by reconstruction and generative models","authors":"José C. Rubio, B. Ommer","doi":"10.1109/CVPR.2015.7299049","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7299049","url":null,"abstract":"Part-based models are one of the leading paradigms in visual recognition. In the absence of costly part annotations, associating and aligning different training instances of a part classifier and finding characteristic negatives is challenging and computationally demanding. To avoid this costly mining of training samples, we estimate separate generative models for negatives and positives and integrate them into a max-margin exemplar-based model. The generative model and a sparsity constraint on the correlation between spatially neighboring feature dimensions regularize the part filters during learning and improve their generalization to similar instances. To suppress inappropriate positive part samples, we project the classifier back into the image domain and penalize against deviations from the original exemplar image patch. The part filter is then optimized to i) discriminate against clutter, to ii) generalize to similar instances of the part, and iii) to yield a good reconstruction of the original image patch. Moreover, we propose an approximation for estimating the geometric margin so that learning large numbers of parts becomes feasible. Experiments show improved part localization, object recognition, and part-based reconstruction performance compared to popular exemplar-based approaches on PASCAL VOC.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121903088","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: 4

Active learning and discovery of object categories in the presence of unnameable instances 在存在不可命名实例的情况下主动学习和发现对象类别

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299063

Christoph Käding, Alexander Freytag, E. Rodner, P. Bodesheim, Joachim Denzler

引用次数: 58

Nested motion descriptors 嵌套运动描述符

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298648

J. Byrne

引用次数: 9

Beyond Principal Components: Deep Boltzmann Machines for face modeling 超越主成分:面部建模的深度玻尔兹曼机器

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299111

C. Duong, Khoa Luu, Kha Gia Quach, T. D. Bui

{"title":"Beyond Principal Components: Deep Boltzmann Machines for face modeling","authors":"C. Duong, Khoa Luu, Kha Gia Quach, T. D. Bui","doi":"10.1109/CVPR.2015.7299111","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7299111","url":null,"abstract":"The “interpretation through synthesis”, i.e. Active Appearance Models (AAMs) method, has received considerable attention over the past decades. It aims at “explaining” face images by synthesizing them via a parameterized model of appearance. It is quite challenging due to appearance variations of human face images, e.g. facial poses, occlusions, lighting, low resolution, etc. Since these variations are mostly non-linear, it is impossible to represent them in a linear model, such as Principal Component Analysis (PCA). This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations. In this approach, three crucial components represented in hierarchical layers are modeled using the Deep Boltzmann Machines (DBM) to robustly capture the variations of facial shapes and appearances. DAMs are therefore superior to AAMs in inferring a representation for new face images under various challenging conditions. In addition, DAMs have ability to generate a compact set of parameters in higher level representation that can be used for classification, e.g. face recognition and facial age estimation. The proposed approach is evaluated in facial image reconstruction, facial super-resolution on two databases, i.e. LFPW and Helen. It is also evaluated on FG-NET database for the problem of age estimation.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127123400","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: 50

MatchNet: Unifying feature and metric learning for patch-based matching MatchNet:统一基于补丁匹配的特征和度量学习

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7298948

Xufeng Han, Thomas Leung, Yangqing Jia, R. Sukthankar, A. Berg

{"title":"MatchNet: Unifying feature and metric learning for patch-based matching","authors":"Xufeng Han, Thomas Leung, Yangqing Jia, R. Sukthankar, A. Berg","doi":"10.1109/CVPR.2015.7298948","DOIUrl":"https://doi.org/10.1109/CVPR.2015.7298948","url":null,"abstract":"Motivated by recent successes on learning feature representations and on learning feature comparison functions, we propose a unified approach to combining both for training a patch matching system. Our system, dubbed Match-Net, consists of a deep convolutional network that extracts features from patches and a network of three fully connected layers that computes a similarity between the extracted features. To ensure experimental repeatability, we train MatchNet on standard datasets and employ an input sampler to augment the training set with synthetic exemplar pairs that reduce overfitting. Once trained, we achieve better computational efficiency during matching by disassembling MatchNet and separately applying the feature computation and similarity networks in two sequential stages. We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods. Our results confirm that our unified approach improves accuracy over previous state-of-the-art results on patch matching datasets, while reducing the storage requirement for descriptors. We make pre-trained MatchNet publicly available.","PeriodicalId":444472,"journal":{"name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123692738","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: 770

Fine-grained recognition without part annotations 没有部件注释的细粒度识别

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299194

J. Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei

引用次数: 434

Simultaneous Time-of-Flight sensing and photometric stereo with a single ToF sensor 同时飞行时间传感和光度计立体与一个单一的ToF传感器

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2015-06-07 DOI: 10.1109/CVPR.2015.7299062

Changpeng Ti, Ruigang Yang, James Davis, Zhigeng Pan

引用次数: 18