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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61-70 订阅

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Inferring Analogous Attributes 27

Inferring Analogous Attributes

引用

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

作者： Chen, Chao-Yeh Grauman, Kristen Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781479951178

the appearance of an attribute can vary considerably from class to class (e.g., a "fluffy" dog vs. a "fluffy" towel), making standard class-independent attribute models break down. Yet, training object-specific models for each attribute can be impractical, and defeats the purpose of using attributes to bridge category boundaries. We propose a novel form of transfer learning that addresses this dilemma. We develop a tensor factorization approach which, given a sparse set of class-specific attribute classifiers, can infer new ones for object-attribute pairs unobserved during training. For example, even though the system has no labeled images of striped dogs, it can use its knowledge of other attributes and objects to tailor "stripedness" to the dog category. With two large-scale datasets, we demonstrate both the need for category-sensitive attributes as well as our method's successful transfer. Our inferred attribute classifiers perform similarly well to those trained with the luxury of labeled class-specific instances, and much better than those restricted to traditional modes of transfer.

关键词： pattern recognition

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Sign Spotting using Hierarchical Sequential patterns with Temporal Intervals 27

Sign Spotting using Hierarchical Sequential Patterns with Te...

引用

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

作者： Ong, Eng-Jon Koller, Oscar Pugeault, Nicolas Bowden, Richard Univ Surrey CVSSP Guildford GU27XH Surrey England

ISBN: (纸本)9781479951178

this paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patterns that contain temporal intervals called Sequential Interval patterns (SIP). We then propose a novel multi-class classifier that organises different sequential interval patterns in a hierarchical tree structure called a Hierarchical SIP Tree (HSP-Tree). this allows one to exploit any subsequence sharing that exists between different SIPs of different classes. Multiple trees are then combined together into a forest of HSP-Trees resulting in a strong classifier that can be used to spot signs. We then show how the HSP-Forest can be used to spot sequences of signs that occur in an input video. We have evaluated the method on both concatenated sequences of isolated signs and continuous sign sequences. We also show that the proposed method is superior in robustness and accuracy to a state of the art sign recogniser when applied to spotting a sequence of signs.

关键词： Machine Learning Sequential pattern Learning Sign recognition Temporal Intervals

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DeepFace: Closing the Gap to Human-Level Performance in Face Verification 27

DeepFace: Closing the Gap to Human-Level Performance in Face...

引用

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

作者： Taigman, Yaniv Yang, Ming Ranzato, Marc'Aurelio Wolf, Lior Facebook AI Res Menlo Pk CA 94205 USA Tel Aviv Univ IL-69978 Tel Aviv Israel

ISBN: (纸本)9781479951178

In modern face recognition, the conventional pipeline consists of four stages: detect double right arrow align double right arrow represent double right arrow classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. this deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities. the learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

关键词： Face recognition

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Generating object segmentation proposals using global and local search 27

Generating object segmentation proposals using global and lo...

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

作者： Rantalankila, Pekka Kannala, Juho Rahtu, Esa Univ Oulu SF-90100 Oulu Finland

ISBN: (纸本)9781479951178

We present a method for generating object segmentation proposals from groups of superpixels. the goal is to propose accurate segmentations for all objects of an image. the proposed object hypotheses can be used as input to object detection systems and thereby improve efficiency by replacing exhaustive search. the segmentations are generated in a class-independent manner and therefore the computational cost of the approach is independent of the number of object classes. Our approach combines both global and local search in the space of sets of superpixels. the local search is implemented by greedily merging adjacent pairs of superpixels to build a bottom-up segmentation hierarchy. the regions from such a hierarchy directly provide a part of our region proposals. the global search provides the other part by performing a set of graph cut segmentations on a superpixel graph obtained from an intermediate level of the hierarchy. the parameters of the graph cut problems are learnt in such a manner that they provide complementary sets of regions. Experiments with Pascal VOC images show that we reach state-of-the-art with greatly reduced computational cost.

关键词： Object detection Object proposals Object recognition Object segmentation Superpixels segmentation

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Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction 27

Fast and Accurate Image Matching with Cascade Hashing for 3D...

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

作者： Cheng, Jian Leng, Cong Wu, Jiaxiang Cui, Hainan Lu, Hanqing Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Image matching is one of the most challenging stages in 3D reconstruction, which usually occupies half of computational cost and inaccurate matching may lead to failure of reconstruction. therefore, fast and accurate image matching is very crucial for 3D reconstruction. In this paper, we proposed a Cascade Hashing strategy to speed up the image matching. In order to accelerate the image matching, the proposed Cascade Hashing method is designed to be three-layer structure: hashing lookup, hashing remapping, and hashing ranking. Each layer adopts different measures and filtering strategies, which is demonstrated to be less sensitive to noise. Extensive experiments show that image matching can be accelerated by our approach in hundreds times than brute force matching, even achieves ten times or more than Kd-tree based matching while retaining comparable accuracy.

关键词： Image matching

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Addressing System-Level Optimization with OpenVX Graphs

Addressing System-Level Optimization with OpenVX Graphs

引用

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

作者： Rainey, Erik Villarreal, Jesse Dedeoglu, Goksel Pulli, Kari Lepley, thierry Brill, Frank Texas Instruments Inc Dallas TX 75265 USA LLC PercepTon Dallas TX 75265 USA NVIDIA Santa Clara CA 95050 USA

ISBN: (纸本)9781479943098

During the performance optimization of a computer vision system, developers frequently run into platform-level inefficiencies and bottlenecks that can not be addressed by traditional methods. OpenVX is designed to address such system-level issues by means of a graph-based computation model. this approach differs from the traditional acceleration of one-off functions, and exposes optimization possibilities that might not be available or obvious with traditional computer vision libraries such as OpenCV.

关键词： OpenVX computer vision embedded systems embedded vision mobile vision optimization performance optimization

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Discriminative Ferns Ensemble for Hand Pose recognition 27

Discriminative Ferns Ensemble for Hand Pose Recognition

引用

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

作者： Krupka, Eyal Vinnikov, Alon Klein, Ben Bar Hillel, Aharon Freedman, Daniel Stachniak, Simon Microsoft Res Haifa Israel Microsoft Console Dev R&D Redmond WA USA

ISBN: (纸本)9781479951178

We present the Discriminative Ferns Ensemble (DFE) classifier for efficient visual object recognition. the classifier architecture is designed to optimize both classification speed and accuracy when a large training set is available. Speed is obtained using simple binary features and direct indexing into a set of tables, and accuracy by using a large capacity model and careful discriminative optimization. the proposed framework is applied to the problem of hand pose recognition in depth and infra-red images, using a very large training set. Both the accuracy and the classification time obtained are considerably superior to relevant competing methods, allowing one to reach accuracy targets with run times orders of magnitude faster than the competition. We show empirically that using DFE, we can significantly reduce classification time by increasing training sample size for a fixed target accuracy. Finally a DFE result is shown for the MNIST dataset, showing the method's merit extends beyond depth images.

关键词： Visual object recognition classification hand pose recognition recognition is depth images

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Turning Mobile Phones into 3D Scanners 27

Turning Mobile Phones into 3D Scanners

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

作者： Kolev, Kahn Tanskanen, Petri Speciale, Pablo Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781479951178

In this paper, we propose an efficient and accurate scheme for the integration of multiple stereo-based depth measurements. For each provided depth map a confidence-based weight is assigned to each depth estimate by evaluating local geometry orientation, underlying camera setting and photometric evidence. Subsequently, all hypotheses are fused together into a compact and consistent 3D model. thereby, visibility conflicts are identified and resolved, and fitting measurements are averaged with regard to their confidence scores. the individual stages of the proposed approach are validated by comparing it to two alternative techniques which rely on a conceptually different fusion scheme and a different confidence inference, respectively. Pursuing live 3D reconstruction on mobile devices as a primary goal, we demonstrate that the developed method can easily be integrated into a system for monocular interactive 3D modeling by substantially improving its accuracy while adding a negligible overhead to its performance and retaining its interactive potential.

关键词： three dimensional computer graphics

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A Stream Algebra for computer vision Pipelines

A Stream Algebra for Computer Vision Pipelines

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

作者： Helala, Mohamed A. Pu, Ken Q. Qureshi, Faisal Z. Univ Ontario Inst Technol Fac Sci Oshawa ON Canada

ISBN: (纸本)9781479943098

Recent interest in developing online computer vision algorithms is spurred in part by a growth of applications capable of generating large volumes of images and videos. these applications are rich sources of images and video streams. Online vision algorithms for managing, processing and analyzing these streams need to rely upon streaming concepts, such as pipelines, to ensure timely and incremental processing of data. this paper is a first attempt at defining a formal stream algebra that provides a mathematical description of vision pipelines and describes the distributed manipulation of image and video streams. We also show how our algebra can effectively describe the vision pipelines of two state of the art techniques.

关键词： Image and Video Streams Stream Algebra computer vision Pipelines Stream Operators

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When 3D Reconstruction Meets Ubiquitous RGB-D Images 27th

When 3D Reconstruction Meets Ubiquitous RGB-D Images

引用

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

作者： Zhang, Quanshi Song, Xuan Shao, Xiaowei Zhao, Huijing Shibasaki, Ryosuke Univ Tokyo Tokyo 1138654 Japan Peking Univ Beijing Peoples R China

ISBN: (纸本)9781479951178

3D reconstruction from a single image is a classical problem in computer vision. However, it still poses great challenges for the reconstruction of daily-use objects with irregular(1) shapes. In this paper, we propose to learn 3D reconstruction knowledge from informally captured(2) RGB-D images, which will probably be ubiquitously used in daily life. the learning of 3D reconstruction is defined as a category modeling problem, in which a model for each category is trained to encode category-specific knowledge for 3D reconstruction. the category model estimates the pixel-level 3D structure of an object from its 2D appearance, by taking into account considerable variations in rotation, 3D structure, and texture. Learning 3D reconstruction from ubiquitous RGB-D images creates a new set of challenges. Experimental results have demonstrated the effectiveness of the proposed approach.

关键词： 3D reconstruction RGB-D images knowledge base ubiquitous learning web images Knowledge Bases images three-dimensional displays computer vision

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