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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961-970 订阅

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Next generation FPGAs and SOCs - How embedded systems can profit

Next generation FPGAs and SOCs - How embedded systems can pr...

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

作者： Eberli, Felix Supercomp Syst AG CH-8005 Zurich Switzerland

ISBN: (纸本)9780769549903

New SOC like the Xilinx Zynq 7045 allow researchers and developers to combine the advantages of writing software for control functionality and having accelerators in the FPGA logic for the number crunching. the dual core Cortex-A9 ARM processor runs with up to 1 GHz and the FPGA has up to 900 DSP slices allowing a performance of up to 1,334 GMACs. SCS is porting a lot of algorithms like SGM stereo [1], Stixel clustering or an optical flow [2] to such devices allowing new cars to see their environment and react appropriately. the new developed SCS Zynq 7045 module will allow accelerated development using this technology.

关键词： digital signal processing chips embedded systems field programmable gate arrays image sequences pattern clustering stereo image processing system-on-chip

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recognition of Symmetry Structure by Use of Gestalt Algebra

Recognition of Symmetry Structure by Use of Gestalt Algebra

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

作者： Michaelsen, Eckart Muench, David Arens, Michael Fraunhofer IOSB D-76275 Ettlingen Germany

ISBN: (纸本)9780769549903

While most approaches to symmetry detection in machine vision try to explain the gray-values or colors of the pixels, Gestalt algebra has no room for such measurement data. the entities (i.e. Gestalten) are only defined with respect to each other. they form a generic hierarchy, and live in a continuous domain without any pixel raster. there is also no constraint forcing them to completely fill an image, or prohibiting overlap. Yet, when used as a tool for symmetry recognition, the algebra must be somehow connected to the given data. In this paper this is done only on the primitive level using the well-known SIFT feature detector. From a set of such SIFT-based Gestalten follows a combinatorial set of higher-order symmetric Gestalten by constructing all possible terms using the operations of the algebra. the Gestalt domain contains a quality or assessment dimension. Taking the best Gestalten with respect to this attribute and clustering them yields the output for this competition participation.

关键词： algebra computer vision feature extraction object detection object recognition

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An approach to pose-based action recognition

An approach to pose-based action recognition

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

作者： Wang, Chunyu Wang, Yizhou Yuille, Alan L. Peking Univ Schl EECS Key Lab Machine Percept MoE Natl Engn Lab Video Technol Beijing 100871 Peoples R China Univ Calif Los Angeles Dept Stat Los Angeles CA USA

ISBN: (纸本)9780769549897

We address action recognition in videos by modeling the spatial-temporal structures of human poses. We start by improving a state of the art method for estimating human joint locations from videos. More precisely, we obtain the K-best estimations output by the existing method and incorporate additional segmentation cues and temporal constraints to select the "best" one. then we group the estimated joints into five body parts (e. g. the left arm) and apply data mining techniques to obtain a representation for the spatial-temporal structures of human actions. this representation captures the spatial configurations of body parts in one frame (by spatial-part-sets) as well as the body part movements(by temporal-part-sets) which are characteristic of human actions. It is interpretable, compact, and also robust to errors on joint estimations. Experimental results first show that our approach is able to localize body joints more accurately than existing methods. Next we show that it outperforms state of the art action recognizers on the UCF sport, the Keck Gesture and the MSR-Action3D datasets.

关键词： data mining gesture recognition image motion analysis image representation image segmentation pose estimation video signal processing

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Exploring Compositional High Order pattern Potentials for Structured Output Learning

Exploring Compositional High Order Pattern Potentials for St...

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

作者： Li, Yujia Tarlow, Daniel Zemel, Richard Univ Toronto Toronto ON M5S 3G4 Canada

ISBN: (纸本)9780769549897

When modeling structured outputs such as image segmentations, prediction can be improved by accurately modeling structure present in the labels. A key challenge is developing tractable models that are able to capture complex high level structure like shape. In this work, we study the learning of a general class of pattern-like high order potential, which we call Compositional High Order pattern Potentials (CHOPPs). We show that CHOPPs include the linear deviation pattern potentials of Rother et al. [] and also Restricted Boltzmann Machines (RBMs);we also establish the near equivalence of these two models. Experimentally, we show that performance is affected significantly by the degree of variability present in the datasets, and we define a quantitative variability measure to aid in studying this. We then improve CHOPPs performance in high variability datasets with two primary contributions: (a) developing a loss-sensitive joint learning procedure, so that internal pattern parameters can be learned in conjunction with other model potentials to minimize expected loss;and (b) learning an image-dependent mapping that encourages or inhibits patterns depending on image features. We also explore varying how multiple patterns are composed, and learning convolutional patterns. Quantitative results on challenging highly variable datasets show that the joint learning and image-dependent high order potentials can improve performance.

关键词： Boltzmann machines feature extraction image segmentation learning (artificial intelligence)

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Supervised Kernel Descriptors for Visual recognition

Supervised Kernel Descriptors for Visual Recognition

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

作者： Wang, Peng Wang, Jingdong Zeng, Gang Xu, Weiwei Zha, Hongbin Li, Shipeng Peking Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Hangzhou Normal Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9780769549897

In visual recognition tasks, the design of low level image feature representation is fundamental. the advent of local patch features from pixel attributes such as SIFT and LBP, has precipitated dramatic progresses. Recently, a kernel view of these features, called kernel descriptors (KDES) [1], generalizes the feature design in an unsupervised fashion and yields impressive results. In this paper, we present a supervised framework to embed the image level label information into the design of patch level kernel descriptors, which we call supervised kernel descriptors (SKDES). Specifically, we adopt the broadly applied bag-of-words (BOW) image classification pipeline and a large margin criterion to learn the lowlevel patch representation, which makes the patch features much more compact and achieve better discriminative ability than KDES. With this method, we achieve competitive results over several public datasets comparing with state-of-the-art methods.

关键词： image classification image recognition image representation learning (artificial intelligence)

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Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines

Weakly Supervised Learning of Mid-Level Features with Beta-B...

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

作者： Mittelman, Roni Lee, Honglak Kuipers, Benjamin Savarese, Silvio Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

ISBN: (纸本)9780769549897

the use of semantic attributes in computer vision problems has been gaining increased popularity in recent years. Attributes provide an intermediate feature representation in between low-level features and the class categories, leading to improved learning on novel categories from few examples. However, a major caveat is that learning semantic attributes is a laborious task, requiring a significant amount of time and human intervention to provide labels. In order to address this issue, we propose a weakly supervised approach to learn mid-level features, where only class-level supervision is provided during training. We develop a novel extension of the restricted Boltzmann machine (RBM) by incorporating a Beta-Bernoulli process factor potential for hidden units. Unlike the standard RBM, our model uses the class labels to promote category-dependent sharing of learned features, which tends to improve the generalization performance. By using semantic attributes for which annotations are available, we show that we can find correspondences between the learned mid-level features and the labeled attributes. therefore, the mid-level features have distinct semantic characterization which is similar to that given by the semantic attributes, even though their labeling was not provided during training. Our experimental results on object recognition tasks show significant performance gains, outperforming existing methods which rely on manually labeled semantic attributes.

关键词： Boltzmann machines computer vision learning (artificial intelligence) object recognition

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Kernel Null Space Methods for Novelty Detection

Kernel Null Space Methods for Novelty Detection

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

作者： Bodesheim, Paul Freytag, Alexander Rodner, Erik Kemmler, Michael Denzler, Joachim Univ Jena Comp Vis Grp D-07743 Jena Germany

ISBN: (纸本)9780769549897

Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold. We present how to apply a null space method for novelty detection, which maps all training samples of one class to a single point. Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model. In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance. this subspace is called the null space of the training data. To decide about novelty of a test sample, our null space approach allows for solely relying on a distance measure instead of performing density estimation directly. therefore, we derive a simple yet powerful method for multi-class novelty detection, an important problem not studied sufficiently so far. Our novelty detection approach is assessed in comprehensive multi-class experiments using the publicly available datasets Caltech-256 and ImageNet. the analysis reveals that our null space approach is perfectly suited for multi-class novelty detection since it outperforms all other methods.

关键词： learning (artificial intelligence) object recognition

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Non-Rigid Structure from Motion with Diffusion Maps Prior

Non-Rigid Structure from Motion with Diffusion Maps Prior

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

作者： Tao, Lili Matuszewski, Bogdan J. Univ Cent Lancashire Appl Digital Signal & Image Proc Res Ctr Preston PR1 2HE Lancs England

ISBN: (纸本)9780769549897

In this paper, a novel approach based on a non-linear manifold learning technique is proposed to recover 3D non-rigid structures from 2D image sequences captured by a single camera. Most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. these techniques perform well when the deformations are relatively small or simple, but fail when more complex deformations need to be recovered. the non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. A specific type of shape variations might be governed by only a small number of parameters, therefore can be well-represented in a low dimensional manifold. We learn a nonlinear shape prior using diffusion maps method. the key contribution in this paper is the introduction of the shape prior that constrain the reconstructed shapes to lie in the learned manifold. the proposed methodology has been validated quantitatively and qualitatively on 2D points sequences projected from the 3D motion capture data and real 2D video sequences. the comparisons of the proposed manifold based method against several state-of-the-art techniques are shown on different types of deformable objects.

关键词： computer graphics image sequences shape recognition video signal processing

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Gauging Association patterns of Chromosome Territories via Chromatic Median

Gauging Association Patterns of Chromosome Territories via C...

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

作者： Ding, Hu Stojkovic, Branislav Berezney, Ronald Xu, Jinhui SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA SUNY Buffalo Dept Biol Sci Buffalo NY 14260 USA

ISBN: (纸本)9780769549897

Computing accurate and robust organizational patterns of chromosome territories inside the cell nucleus is critical for understanding several fundamental genomic processes, such as co-regulation of gene activation, gene silencing, X chromosome inactivation, and abnormal chromosome rearrangement in cancer cells. the usage of advanced fluorescence labeling and image processing techniques has enabled researchers to investigate interactions of chromosome territories at large spatial resolution. the resulting high volume of generated data demands for high-throughput and automated image analysis methods. In this paper, we introduce a novel algorithmic tool for investigating association patterns of chromosome territories in a population of cells. Our method takes as input a set of graphs, one for each cell, containing information about spatial interaction of chromosome territories, and yields a single graph that contains essential information for the whole population and stands as its structural representative. We formulate this combinatorial problem as a semi-definite programming and present novel techniques to efficiently solve it. We validate our approach on both artificial and real biological data;the experimental results suggest that our approach yields a near-optimal solution, and can handle large-size datasets, which are significant improvements over existing techniques.

关键词： cellular biophysics graph theory medical image processing organisational aspects pattern classification

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Selective Transfer Machine for Personalized Facial Action Unit Detection

Selective Transfer Machine for Personalized Facial Action Un...

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

作者： Chu, Wen-Sheng De la Torre, Fernando Cohn, Jeffery F. Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. they neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. the alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2]. STM outperformed generic classifiers in all.

关键词： emotion recognition face recognition image classification learning (artificial intelligence)

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