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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是151-160 订阅

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Fast Energy Minimization using Learned State Filters

Fast Energy Minimization using Learned State Filters

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Guillaumin, Matthieu Van Gool, Luc Ferrari, Vittorio Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9780769549897

Pairwise discrete energies defined over graphs are ubiquitous in computer vision. Many algorithms have been proposed to minimize such energies, often concentrating on sparse graph topologies or specialized classes of pairwise potentials. However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take. Moreover, recent applications are using more and more computationally expensive pairwise potentials. these factors make it very hard to employ fully connected models. In this paper we propose a novel, generic algorithm to approximately minimize any discrete pairwise energy function. Our method exploits tractable sub-energies to filter the domain of the function. the parameters of the filter are learnt from instances of the same class of energies with good candidate solutions. Compared to existing methods, it efficiently handles fully connected graphs, with many states per node, and arbitrary pairwise potentials, which might be expensive to compute. We demonstrate experimentally on two applications that our algorithm is much more efficient than other generic minimization algorithms such as TRW-S, while returning essentially identical solutions.

关键词： approximation theory computer vision filtering theory graph theory minimisation

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Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression recognition

Capturing Complex Spatio-Temporal Relations among Facial Mus...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Ziheng Wang, Shangfei Ji, Qiang Rensselaer Polytech Inst ECSE Dept Troy NY 12181 USA Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9780769549897

Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited. One contributing factor for this is the limited ability of the current dynamic models in capturing complex spatial and temporal relations. Existing dynamic models can only capture simple local temporal relations among sequential events, or lack the ability for incorporating uncertainties. To overcome these limitations and take full advantage of the spatio-temporal information, we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events. We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition. Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles, as well as its advantage over the existing methods.

关键词： belief networks face recognition image motion analysis muscle

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Hollywood 3D: Recognizing Actions in 3D Natural Scenes

Hollywood 3D: Recognizing Actions in 3D Natural Scenes

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hadfield, Simon Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

ISBN: (纸本)9780769549897

Action recognition in unconstrained situations is a difficult task, suffering from massive intra-class variations. It is made even more challenging when complex 3D actions are projected down to the image plane, losing a great deal of information. the recent emergence of 3D data, both in broadcast content, and commercial depth sensors, provides the possibility to overcome this issue. this paper presents a new dataset, for benchmarking action recognition algorithms in natural environments, while making use of 3D information. the dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data. Our evaluation compares all 4 feature descriptors, using 7 different types of interest point, over a variety of threshold levels, for the Hollywood3D dataset. We make the dataset including stereo video, estimated depth maps and all code required to reproduce the benchmark results, available to the wider community.

关键词： image recognition natural scenes object detection stereo image processing video signal processing

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Category Modeling from just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models

Category Modeling from just a Single Labeling: Use Depth Inf...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Quanshi Song, Xuan Shao, Xiaowei Shibasaki, Ryosuke Zhao, Huijing Univ Tokyo Ctr Spatial Informat Sci Tokyo 1138654 Japan Peking Univ Key Lab Machine Percept Beijing Peoples R China

ISBN: (纸本)9780769549897

An object model base that covers a large number of object categories is of great value for many computer vision tasks. As artifacts are usually designed to have various textures, their structure is the primary distinguishing feature between different categories. thus, how to encode this structural information and how to start the model learning with a minimum of human labeling become two key challenges for the construction of the model base. We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes. However, the incremental model learning may be biased due to the limited human labeling. therefore, we propose a new strategy that uses the depth information in RGBD images to guide the model learning for object detection in ordinary RGB images. In experiments, the proposed method achieves superior performance as good as the supervised methods that require the labeling of all target objects.

关键词： computer aided instruction computer vision feature extraction object detection

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A Fully Automatic Method to Extract the Heart Rate from thermal Video

A Fully Automatic Method to Extract the Heart Rate from Ther...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gault, Travis R. Farag, Aly A. Univ Louisville Louisville KY 40292 USA

ISBN: (纸本)9780769549903

this work deals with non-invasive and non-intrusive measurements of the human facial vasculature from thermal IR to measure cardiovascular vital signs. A robust, fully automatic measurement system is developed to study infrared videos of 32 under three imaging scenarios. Vascular mapping, blood perfusion modeling, and wavelet analysis are used to calculate heart rate from 512 video frames in near real time. Multiple measurement locations are considered to analyze accuracy and sensitivity in a novel approach, revealing that the 60% of vessel segments in the forehead region are capable of producing 85% accuracy of measurement or better.

关键词： biomedical optical imaging cardiovascular system face recognition haemorheology infrared imaging medical image processing video streaming wavelet transforms

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Visual Place recognition with Repetitive Structures

Visual Place Recognition with Repetitive Structures

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Torii, Akihiko Sivic, Josef Pajdla, Tomas Okutomi, Masatoshi Tokyo Tech Tokyo Inst Technol Grad Sch Sci & Engn Dept Mech & Control Engn Tokyo Japan WILLOW Inria Dept Informat Ecole Normale Super ENS INRIA CNRS UMR 8548 Paris France Czech Tech Univ Fac Elect Engn Ctr Machine Percept Dept Cybernet Prague Czech Republic

ISBN: (纸本)9780769549897

Repeated structures such as building facades, fences or road markings often represent a significant challenge for place recognition. Repeated structures are notoriously hard for establishing correspondences using multi-view geometry. Even more importantly, they violate the feature independence assumed in the bag-of-visual-words representation which often leads to over-counting evidence and significant degradation of retrieval performance. In this work we show that repeated structures are not a nuisance but, when appropriately represented, they form an important distinguishing feature for many places. We describe a representation of repeated structures suitable for scalable retrieval. It is based on robust detection of repeated image structures and a simple modification of weights in the bag-of-visual-word model. Place recognition results are shown on datasets of street-level imagery from Pittsburgh and San Francisco demonstrating significant gains in recognition performance compared to the standard bag-of-visual-words baseline and more recently proposed burstiness weighting.

关键词： geographic information systems image recognition image retrieval object detection

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Learning and calibrating per-location classifiers for visual place recognition

Learning and calibrating per-location classifiers for visual...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gronat, Petr Obozinski, Guillaume Sivic, Josef Pajdla, Tomas INRIA France Czech Technical University in Prague Czech Republic Center for Machine Perception Faculty of Electrical Engineering France WILLOW Project Laboratoire d'Informatique de l' Ecole Normale Superieure ENS/INRIA/CNRS UMR 8548. France Universit Paris-Est LIGM (UMR CNRS 8049) Center for Visual Computing 77455 Marne-la-Valle France

ISBN: (纸本)9780769549897

the aim of this work is to localize a query photograph by finding other images depicting the same place in a large geotagged image database. this is a challenging task due to changes in viewpoint, imaging conditions and the large size of the image database. the contribution of this work is two-fold. First, we cast the place recognition problem as a classification task and use the available geotags to train a classifier for each location in the database in a similar manner to per-exemplar SVMs in object recognition. Second, as only few positive training examples are available for each location, we propose a new approach to calibrate all the per-location SVM classifiers using only the negative examples. the calibration we propose relies on a significance measure essentially equivalent to the p-values classically used in statistical hypothesis testing. Experiments are performed on a database of 25,000 geotagged street view images of Pittsburgh and demonstrate improved place recognition accuracy of the proposed approach over the previous work.

关键词： geophysical image processing image classification learning (artificial intelligence) object recognition statistical testing support vector machines

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Modeling Mutual Visibility Relationship in Pedestrian Detection

Modeling Mutual Visibility Relationship in Pedestrian Detect...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ouyang, Wanli Zeng, Xingyu Wang, Xiaogang Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pat Rec Beijing Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

Detecting pedestrians in cluttered scenes is a challenging problem in computer vision. the difficulty is added when several pedestrians overlap in images and occlude each other. We observe, however, that the occlusion/visibility statuses of overlapping pedestrians provide useful mutual relationship for visibility estimation - the visibility estimation of one pedestrian facilitates the visibility estimation of another. In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians. the visibility relationship among pedestrians is learned from the deep model for recognizing co-existing pedestrians. Experimental results show that the mutual visibility deep model effectively improves the pedestrian detection results. Compared with existing image-based pedestrian detection approaches, our approach has the lowest average miss rate on the Caltech-Train dataset, the Caltech-Test dataset and the Eth dataset. Including mutual visibility leads to 4%- 8% improvements on multiple benchmark datasets.

关键词： computer vision object detection pedestrians visual databases

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Multi-Task Sparse Learning with Beta Process Prior for Action recognition

Multi-Task Sparse Learning with Beta Process Prior for Actio...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yuan, Chunfeng Hu, Weiming Tian, Guodong Yang, Shuang Wang, Haoran CASIA Natl Lab Pattern Recognit Beijing Peoples R China Southeast Univ Sch Automat Beijing Peoples R China

ISBN: (纸本)9780769549897

In this paper we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible. Learning the sparse representation under each feature modality is considered as a single task in MTSL. Since the tasks are generated from multiple features associated with the same visual input, they are not independent but inter-related. We introduce a Beta process(BP) prior to the hierarchical MTSL model, which efficiently learns a compact dictionary and infers the sparse structure shared across all the tasks. the MTSL model enforces the robustness in coefficient estimation compared with performing each task independently. Besides, the sparseness is achieved via the Beta process formulation rather than the computationally expensive l(1) norm penalty. In terms of non-informative gamma hyper-priors, the sparsity level is totally decided by the data. Finally, the learning problem is solved by Gibbs sampling inference which estimates the full posterior on the model parameters. Experimental results on the Kth and UCF sports datasets demonstrate the effectiveness of the proposed MTSL approach for action recognition.

关键词： computer vision feature extraction gesture recognition image representation inference mechanisms learning (artificial intelligence)

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Face recognition in Movie Trailers via Mean Sequence Sparse Representation-based Classification

Face Recognition in Movie Trailers via Mean Sequence Sparse ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ortiz, Enrique G. Wright, Alan Shah, Mubarak Univ Cent Florida Ctr Comp Vis Res Orlando FL 32816 USA

ISBN: (纸本)9780769549897

this paper presents an end-to-end video face recognition system, addressing the difficult problem of identifying a video face track using a large dictionary of still face images of a few hundred people, while rejecting unknown individuals. A straightforward application of the popular l(1)-1 minimization for face recognition on a frame-by-frame basis is prohibitively expensive, so we propose a novel algorithm Mean Sequence SRC (MSSRC) that performs video face recognition using a joint optimization leveraging all of the available video data and the knowledge that the face track frames belong to the same individual. By adding a strict temporal constraint to the l(1)-minimization that forces individual frames in a face track to all reconstruct a single identity, we show the optimization reduces to a single minimization over the mean of the face track. We also introduce a new Movie Trailer Face Dataset collected from 101 movie trailers on YouTube. Finally, we show that our method matches or outperforms the state-of-the-art on three existing datasets (YouTube Celebrities, YouTube Faces, and Buffy) and our unconstrained Movie Trailer Face Dataset. More importantly, our method excels at rejecting unknown identities by at least 8% in average precision.

关键词： entertainment face recognition image classification image reconstruction image representation image sequences minimisation social networking (online)

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