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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是51-60 订阅

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Learning Important Spatial Pooling Regions for Scene Classification 27

Learning Important Spatial Pooling Regions for Scene Classif...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lin, Di Lu, Cewu Liao, Renjie Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. It is often caused by the complexity of latent image structure when convolving part filters with input images. this problem makes mid-level representation, even after pooling, not distinct enough to classify input data correctly to categories. Our solution is to learn important spatial pooling regions along with their appearance. the experiments show that this new framework suppresses false response and produces improved results on several datasets, including MIT-Indoor, 15-Scene, and UIUC 8-Sport. When combined with global image features, our method achieves state-of-the-art performance on these datasets.

关键词： pattern recognition

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A Hierarchical Probabilistic Model for Facial Feature Detection 27

A Hierarchical Probabilistic Model for Facial Feature Detect...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wu, Yue Wang, Ziheng Ji, Qiang Rensselaer Polytech Inst ECSE Dept Troy NY 12181 USA

ISBN: (纸本)9781479951178

Facial feature detection from facial images has attracted great attention in the field of computer vision. It is a non-trivial 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.

关键词： Face recognition

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Illumination-Aware Age Progression 27

Illumination-Aware Age Progression

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kemelmacher-Shlizerman, Ira Suwajanakorn, Supasorn Seitz, Steven M. Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781479951178

We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination. Leveraging thousands of photos of children and adults at many ages from the Internet, we first show how to compute average image subspaces that are pixel-to-pixel aligned and model variable lighting. these averages depict a prototype man and woman aging from 0 to 80, under any desired illumination, and capture the differences in shape and texture between ages. Applying these differences to a new photo yields an age progressed result. Contributions include relightable age subspaces, a novel technique for subspace-to-subspace alignment, and the most extensive evaluation of age progression techniques in the literature(1).

关键词： age automatic computer vision faces lighting optical flow progression synthesis

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Seeing What You're Told: Sentence-Guided Activity recognition In Video 27

Seeing What You're Told: Sentence-Guided Activity Recognitio...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Siddharth, N. Barbu, Andrei Siskind, Jeffrey Mark Stanford Univ Stanford CA 94305 USA MIT Cambridge MA 02139 USA Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781479951178

We present a system that demonstrates how the compositional structure of events, in concert with the compositional structure of language, can interplay with the underlying focusing mechanisms in video action recognition, providing a medium for top-down and bottom-up integration as well as multi-modal integration between vision and language. We show how the roles played by participants (nouns), their characteristics (adjectives), the actions performed (verbs), the manner of such actions (adverbs), and changing spatial relations between participants (prepositions), in the form of whole-sentence descriptions mediated by a grammar, guides the activity-recognition process. Further, the utility and expressiveness of our framework is demonstrated by performing three separate tasks in the domain of multi-activity video: sentence-guided focus of attention, generation of sentential description, and query-based search, simply by leveraging the framework in different manners.

关键词： pattern recognition

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Single Image Layer Separation using Relative Smoothness 27

Single Image Layer Separation using Relative Smoothness

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Yu Brown, Michael S. Natl Univ Singapore Sch Comp Singapore 117548 Singapore

ISBN: (纸本)9781479951178

this paper addresses extracting two layers from an image where one layer is smoother than the other. this problem arises most notably in intrinsic image decomposition and reflection interference removal. Layer decomposition from a single-image is inherently ill-posed and solutions require additional constraints to be enforced. We introduce a novel strategy that regularizes the gradients of the two layers such that one has a long tail distribution and the other a short tail distribution. While imposing the long tail distribution is a common practice, our introduction of the short tail distribution on the second layer is unique. We formulate our problem in a probabilistic framework and describe an optimization scheme to solve this regularization with only a few iterations. We apply our approach to the intrinsic image and reflection removal problems and demonstrate high quality layer separation on par with other techniques but being significantly faster than prevailing methods.

关键词： pattern recognition

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Surface-from-Gradients: An Approach Based on Discrete Geometry Processing 27

Surface-from-Gradients: An Approach Based on Discrete Geomet...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Xie, Wuyuan Zhang, Yunbo Wang, Charlie C. L. Chung, Ronald C. -K. Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

In this paper, we propose an efficient method to reconstruct surface-from-gradients (SfG). Our method is formulated under the framework of discrete geometry processing. Unlike the existing SfG approaches, we transfer the continuous reconstruction problem into a discrete space and efficiently solve the problem via a sequence of least-square optimization steps. Our discrete formulation brings three advantages: 1) the reconstruction preserves sharp-features, 2) sparse/incomplete set of gradients can be well handled, and 3) domains of computation can have irregular boundaries. Our formulation is direct and easy to implement, and the comparisons with state-of-the-arts show the effectiveness of our method.

关键词： pattern recognition

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Partial Symmetry in Polynomial Systems and its Applications in computer vision 27

Partial Symmetry in Polynomial Systems and its Applications ...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kuang, Yubin Zheng, Yinqiang Astrom, Kalle Lund Univ Ctr Math Sci S-22100 Lund Sweden Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781479951178

Algorithms for solving systems of polynomial equations are key components for solving geometry problems in computer vision. Fast and stable polynomial solvers are essential for numerous applications e.g. minimal problems or finding for all stationary points of certain algebraic errors. Recently, full symmetry in the polynomial systems has been utilized to simplify and speed up state-of-the-art polynomial solvers based on Grobner basis method. In this paper, we further explore partial symmetry (i.e. where the symmetry lies in a subset of the variables) in the polynomial systems. We develop novel numerical schemes to utilize such partial symmetry. We then demonstrate the advantage of our schemes in several computer vision problems. In both synthetic and real experiments, we show that utilizing partial symmetry allow us to obtain faster and more accurate polynomial solvers than the general solvers(1).

关键词： computer vision partial symmetry polynomial equation

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Nonparametric Context Modeling of Local Appearance for Pose- and Expression-Robust Facial Landmark Localization 27

Nonparametric Context Modeling of Local Appearance for Pose-...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Smith, Brandon M. Brandt, Jonathan Lin, Zhe Zhang, Li Univ Wisconsin Madison Madison WI 53706 USA Adobe Res Seattle WA USA

ISBN: (纸本)9781479951178

We propose a data-driven approach to facial landmark localization that models the correlations between each landmark and its surrounding appearance features. At runtime, each feature casts a weighted vote to predict landmark locations, where the weight is precomputed to take into account the feature's discriminative power. the feature voting-based landmark detection is more robust than previous local appearance-based detectors;we combine it with non-parametric shape regularization to build a novel facial landmark localization pipeline that is robust to scale, in-plane rotation, occlusion, expression, and most importantly, extreme head pose. We achieve state-of-the-art performance on two especially challenging in-the-wild datasets populated by faces with extreme head pose and expression.

关键词： Image recognition

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Super Normal Vector for Activity recognition Using Depth Sequences 27

Super Normal Vector for Activity Recognition Using Depth Seq...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yang, Xiaodong Tian, YingLi CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781479951178

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.

关键词： Classification (of information)

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Multilabel Ranking with Inconsistent Rankers 27

Multilabel Ranking with Inconsistent Rankers

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Geng, Xin Luo, Longrun Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479951178

While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where subjective inconsistent rankings from multiple rankers are associated with each instance. the key idea is to learn a latent preference distribution for each instance. the proposed method mainly includes two steps. the first step is to generate a common preference distribution that is most compatible to all the personal rankings. the second step is to learn a mapping from the instances to the preference distributions. the proposed preference distribution learning (PDL) method is applied to the problem of multilabel ranking for natural scene images. Experimental results show that PDL can effectively incorporate the information given by the inconsistent rankers, and perform remarkably better than the compared state-of-the-art multilabel ranking algorithms.

关键词： pattern recognition

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