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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1421-1430 订阅

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Adaptive 3D face reconstruction from unconstrained photo collections

Adaptive 3D face reconstruction from unconstrained photo col...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Roth, Joseph Tong, Yiying Liu, Xiaoming Department of Computer Science and Engineering Michigan State University United States

ISBN: (纸本)9781467388511

Given a collection of "in-the-wild" face images captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of an individual along with albedo information. Motivated by the success of recent face reconstruction techniques on large photo collections, we extend prior work to adapt to low quality photo collections with fewer images. We achieve this by fitting a 3D Morphable Model to form a personalized template and developing a novel photometric stereo formulation, under a coarse-to-fine scheme. Superior experimental results are reported on synthetic and real-world photo collections.

关键词： Image reconstruction

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A consensus-based framework for distributed Bundle Adjustment

A consensus-based framework for distributed Bundle Adjustmen...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Eriksson, Anders Bastian, John Chin, Tat-Jun Isaksson, Mats School of Electrical Engineering and Computer Science Queensland University of Technology Australia School of Computer Science University of Adelaide Australia Electrical and Computer Engineering Department Colorado State University United States

ISBN: (纸本)9781467388511

In this paper we study large-scale optimization problems in multi-view geometry, in particular the Bundle Adjustment problem. In its conventional formulation, the complexity of existing solvers scale poorly with problem size, hence this component of the Structure-from-Motion pipeline can quickly become a bottle-neck. Here we present a novel formulation for solving bundle adjustment in a truly distributed manner using consensus based optimization methods. Our algorithm is presented with a concise derivation based on proximal splitting, along with a theoretical proof of convergence and brief discussions on complexity and implementation. Experiments on a number of real image datasets convincingly demonstrates the potential of the proposed method by outperforming the conventional bundle adjustment formulation by orders of magnitude.

关键词： computer vision

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Large-pose face alignment via CNN-based dense 3D model fitting

Large-pose face alignment via CNN-based dense 3D model fitti...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Jourabloo, Amin Liu, Xiaoming Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9781467388511

Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e.g, face recognition and 3D face reconstruction. Recently, there have been a few attempts to solve this problem, but still more research is needed to achieve highly accurate results. In this paper, we propose a face alignment method for large-pose face images, by combining the powerful cascaded CNN regressor method and 3DMM. We formulate the face alignment as a 3DMM fitting problem, where the camera projection matrix and 3D shape parameters are estimated by a cascade of CNN-based regressors. The dense 3D shape allows us to design pose-invariant appearance features for effective CNN learning. Extensive experiments are conducted on the challenging databases (AFLW and AFW), with comparison to the state of the art.

关键词： Face recognition

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Learning to assign orientations to feature points

Learning to assign orientations to feature points

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yi, Kwang Moo Verdie, Yannick Fua, Pascal Lepetit, Vincent Switzerland Institute for Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9781467388511

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the tedious and almost impossible task of finding a target orientation to learn, we propose to use Siamese networks which implicitly find the optimal orientations during training. We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. This novel activation performs better for our task. We validate the effectiveness of our method extensively with four existing datasets, including two non-planar datasets, as well as our own dataset. We show that we outperform the state-of-the-art without the need of retraining for each dataset.

关键词： computer vision

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Sublabel-accurate relaxation of nonconvex energies

Sublabel-accurate relaxation of nonconvex energies

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Möllenhoff, Thomas Laude, Emanuel Moeller, Michael Lellmann, Jan Cremers, Daniel TU München Germany University of Lübeck Germany

ISBN: (纸本)9781467388511

We propose a novel spatially continuous framework for convex relaxations based on functional lifting. Our method can be interpreted as a sublabel-accurate solution to multilabel problems. We show that previously proposed functional lifting methods optimize an energy which is linear between two labels and hence require (often infinitely) many labels for a faithful approximation. In contrast, the proposed formulation is based on a piecewise convex approximation and therefore needs far fewer labels - see Fig. 1. In comparison to recent MRF-based approaches, our method is formulated in a spatially continuous setting and shows less grid bias. Moreover, in a local sense, our formulation is the tightest possible convex relaxation. It is easy to implement and allows an efficient primal-dual optimization on GPUs. We show the effectiveness of our approach on several computer vision problems.

关键词： computer vision

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Predicting when saliency maps are accurate and eye fixations consistent

Predicting when saliency maps are accurate and eye fixations...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Volokitin, Anna Gygli, Michael Boix, Xavier Computer Vision Laboratory ETH Zurich Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore CBMM Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781467388511

Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps. Recently, the success of Deep Convolutional Neural Networks (DCNNs) for object recognition has opened a new avenue for computational models of visual attention due to the tight link between visual attention and object recognition. In this paper, we show that using features from DCNNs for object recognition we can make predictions that enrich the information provided by saliency models. Namely, we can estimate the reliability of a saliency model from the raw image, which serves as a meta-saliency measure that may be used to select the best saliency algorithm for an image. Analogously, the consistency of the eye fixations among subjects, i.e. the agreement between the eye fixation locations of different subjects, can also be predicted and used by a designer to assess whether subjects reach a consensus about salient image locations.

关键词： Behavioral research

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Sparse coding for third-order Super-symmetric tensor descriptors with application to texture recognition

Sparse coding for third-order Super-symmetric tensor descrip...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Koniusz, Piotr Cherian, Anoop Canberra Research Laboratory Australia ARC Centre of Excellence for Robotic Vision Australian National University Canberra Australia

ISBN: (纸本)9781467388511

Super-symmetric tensors - a higher-order extension of scatter matrices - are becoming increasingly popular in machine learning and computer vision for modeling data statistics, co-occurrences, or even as visual descriptors. They were shown recently to outperform second-order approaches [18], however, the size of these tensors are exponential in the data dimensionality, which is a significant concern. In this paper, we study third-order supersymmetric tensor descriptors in the context of dictionary learning and sparse coding. For this purpose, we propose a novel non-linear third-order texture descriptor. Our goal is to approximate these tensors as sparse conic combinations of atoms from a learned dictionary. Apart from the significant benefits to tensor compression that this framework offers, our experiments demonstrate that the sparse coefficients produced by this scheme lead to better aggregation of high-dimensional data and showcase superior performance on two common computer vision tasks compared to the state of the art.

关键词： Tensors

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Situation recognition: Visual semantic role labeling for image understanding

Situation recognition: Visual semantic role labeling for ima...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yatskar, Mark Zettlemoyer, Luke Farhadi, Ali Computer Science and Engineering University of Washington SeattleWA United States SeattleWA United States

ISBN: (纸本)9781467388511

This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (e.g., the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a large space of possible situations and collect a large-scale dataset containing over 500 activities, 1,700 roles, 11,000 objects, 125,000 images, and 200,000 unique situations. We also introduce structured prediction baselines and show that, in activity-centric images, situation-driven prediction of objects and activities outperforms independent object and activity recognition.

关键词： computer vision

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Subspace clustering with priors via sparse quadratically constrained quadratic programming

Subspace clustering with priors via sparse quadratically con...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Cheng, Yongfang Wang, Yin Sznaier, Mario Camps, Octavia Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9781467388511

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a convex semi-definite optimization problem subject to an additional rank constrain that involves only a very small number of variables. This is established by first reducing the problem to a quadratically constrained quadratic problem and then using its special structure to find conditions guaranteeing that a suitably built convex relaxation is indeed exact. When combined with the standard nuclear norm relaxation for rank, the results above lead to computationally efficient algorithms with optimality guarantees. A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise, co-ocurrences, and percentage of outliers. These results are illustrated with several examples.

关键词： pattern recognition

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Predicting motivations of actions by leveraging text

Predicting motivations of actions by leveraging text

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Vondrick, Carl Oktay, Deniz Pirsiavash, Hamed Torralba, Antonio Massachusetts Institute of Technology United States University of Maryland Baltimore County United States

ISBN: (纸本)9781467388511

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.

关键词： computer vision

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