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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是351-360 订阅

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Relaxed Matching Kernels For Robust Image Comparison

Relaxed Matching Kernels For Robust Image Comparison

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Andrea Vedaldi Stefano Soatto University of California Los Angeles USA

the popular bag-of-features representation for object recognition collects signatures of local image patches and discards spatial information. Some have recently attempted to at least partially overcome this limitation, for instance by "spatial pyramids" and "proximity" kernels. We introduce the general formalism of "relaxed matching kernels" (RMKs) that includes such approaches as special cases, allow us to derive useful general properties of these kernels, and to introduce new ones. As an example, we introduce a kernel based on matching graphs of features and one based on matching information-compressed features. We show that all RMKs are competitive and outperform in several cases recently published state-of-the-art results on standard datasets. However, we also show that a proper implementation of a baseline bag-of-features algorithm can be extremely competitive, and outperform the other methods in some cases.

关键词： Kernel Robustness Quantization Dictionaries Object recognition Statistics Histograms Standards publication Image recognition Image reconstruction

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A Recursive Filter For Linear Systems on Riemannian Manifolds

A Recursive Filter For Linear Systems on Riemannian Manifold...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Ambrish Tyagi James W. Davis Department of Computer Science and Engineering Ohio State Uinversity Columbus OH USA

We present an online, recursive filtering technique to model linear dynamical systems that operate on the state space of symmetric positive definite matrices (tensors) that lie on a Riemannian manifold. the proposed approach describes a predict-and-update computational paradigm, similar to a vector Kalman filter, to estimate the optimal tensor state. We adapt the original Kalman filtering algorithm to appropriately propagate the state over time and assimilate observations, while conforming to the geometry of the manifold. We validate our algorithm with synthetic data experiments and demonstrate its application to visual object tracking using covariance features.

关键词： Nonlinear filters Linear systems Tensile stress State-space methods Symmetric matrices Vectors State estimation Kalman filters Filtering algorithms Geometry

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Measuring camera translation by the dominant apical angle

Measuring camera translation by the dominant apical angle

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Akihiko Torii Michal Havlena Tomas Pajdla Bastian Leibe Czech Technical University Prague Czech Republic ETH Zürich Zurich Switzerland

this paper provides a technique for measuring camera translation relatively w.r.t. the scene from two images. We demonstrate that the amount of the translation can be reliably measured for general as well as planar scenes by the most frequent apical angle, the angle under which the camera centers are seen from the perspective of the reconstructed scene points. Simulated experiments show that the dominant apical angle is a linear function of the length of the true camera translation. In a real experiment, we demonstrate that by skipping image pairs with too small motion, we can reliably initialize structure from motion, compute accurate camera trajectory in order to rectify images and use the ground plane constraint in recognition of pedestrians in a hand-held video sequence.

关键词： Cameras Layout Image reconstruction Motion detection Computational modeling Video sequences Object recognition Motion measurement Handheld computers Image recognition

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Privacy Preserving Crowd Monitoring: Counting People without People Models or Tracking

Privacy Preserving Crowd Monitoring: Counting People without...

引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Antoni B. Chan Zhang-Sheng John Liang Nuno Vasconcelos Electrical and Computer Engineering Department University of California San Diego USA

We present a privacy-preserving system for estimating the size of inhomogeneous crowds, composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking. First, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic textures motion model. Second, a set of simple holistic features is extracted from each segmented region, and the correspondence between features and the number of people per segment is learned with Gaussian Process regression. We validate both the crowd segmentation algorithm, and the crowd counting system, on a large pedestrian dataset (2000 frames of video, containing 49,885 total pedestrian instances). Finally, we present results of the system running on a full hour of video.

关键词： Privacy Layout computer vision computerized monitoring Gaussian processes Event detection Object detection Robustness Object segmentation Feature extraction

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Modeling and Generating Complex Motion Blur for Real-time Tracking

Modeling and Generating Complex Motion Blur for Real-time Tr...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Christopher Mei Ian Reid Department of Engineering Science University of Oxford UK

this article addresses the problem of real-time visual tracking in presence of complex motion blur. Previous authors have observed that efficient tracking can be obtained by matching blurred images instead of applying the computationally expensive task of deblurring [11]. the study was however limited to translational blur. In this work, we analyse the problem of tracking in presence of spatially variant motion blur generated by a planar template. We detail how to model the blur formation and parallelise the blur generation, enabling a real-time GPU implementation. through the estimation of the camera exposure time, we discuss how tracking initialisation can be improved. Our algorithm is tested on challenging real data with complex motion blur where simple models fail. the benefit of blur estimation is shown for structure and motion.

关键词： Tracking Kernel Cameras Charge-coupled image sensors Motion estimation computer vision Layout Lighting Cost function Digital images

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Nonlinear Image Representation Using Divisive Normalization

Nonlinear Image Representation Using Divisive Normalization

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Siwei Lyu Eero P. Simoncelli Howard Hughes Medical Institute and Center for Neuroscience New York University USA

In this paper, we describe a nonlinear image representation based on divisive normalization that is designed to match the statistical properties of photographic images, as well as the perceptual sensitivity of biological visual systems. We decompose an image using a multi-scale oriented representation, and use Student's t as a model of the dependencies within local clusters of coefficients. We then show that normalization of each coefficient by the square root of a linear combination of the amplitudes of the coefficients in the cluster reduces statistical dependencies. We further show that the resulting divisive normalization transform is invertible and provide an efficient iterative inversion algorithm. Finally, we probe the statistical and perceptual advantages of this image representation by examining its robustness to added noise, and using it to enhance image contrast.

关键词： Image representation Wavelet coefficients Iterative algorithms Probes Noise robustness Image processing computer vision Application software Biomedical imaging Neuroscience

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Clustering and Dimensionality Reduction on Riemannian Manifolds

Clustering and Dimensionality Reduction on Riemannian Manifo...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Alvina Goh Rene Vidal Center for Imaging Science Johns Hopkins University Baltimore MD USA

We propose a novel algorithm for clustering data sampled from multiple submanifolds of a Riemannian manifold. First, we learn a representation of the data using generalizations of local nonlinear dimensionality reduction algorithms from Euclidean to Riemannian spaces. Such generalizations exploit geometric properties of the Riemannian space, particularly its Riemannian metric. then, assuming that the data points from different groups are separated, we show that the null space of a matrix built from the local representation gives the segmentation of the data. Our method is computationally simple and performs automatic segmentation without requiring user initialization. We present results on 2-D motion segmentation and diffusion tensor imaging segmentation.

关键词： Image segmentation Clustering algorithms computer vision Euclidean distance Principal component analysis Motion segmentation Diffusion tensor imaging Geophysics computing Null space Extraterrestrial measurements

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Subspace segmentation with outliers: a Grassmannian approach to the maximum consensus subspace

Subspace segmentation with outliers: a Grassmannian approach...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Nuno Pinho da Silva Joao Paulo Costeira ISR-Instituto Superior Técnico Lisboa Portugal

Segmenting arbitrary unions of linear subspaces is an important tool for computer vision tasks such as motion and image segmentation, SfM or object recognition. We segment subspaces by searching for the orthogonal complement of the subspace supported by the majority of the observations, i.e., the maximum consensus subspace. It is formulated as a grassmannian optimization problem: a smooth, constrained but nonconvex program is immersed into the Grassmann manifold, resulting in a low dimensional and unconstrained program solved with an efficient optimization algorithm. Nonconvexity implies that global optimality depends on the initialization. However, by finding the maximum consensus subspace, outlier rejection becomes an inherent property of the method. Besides robustness, it does not rely on prior global detection procedures (e.g., rank of data matrices), which is the case of most current works. We test our algorithm in both synthetic and real data, where no outlier was ever classified as inlier.

关键词： Image segmentation computer vision Constraint optimization Object recognition Robustness Testing Null space MODIS Vectors Optimization methods

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Extrinsic and Depth Calibration of ToF-cameras

Extrinsic and Depth Calibration of ToF-cameras

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Stefan Fuchs Gerd Hirzinger Institute of Robotics and Mechatronics DLR Germany

Recently, ToF-cameras have attracted attention because of their ability to generate a full 21/2D depth image at video frame rates. thus, ToF-cameras are suitable for real-time 3D tasks such as tracking, visual servoing or object pose estimation. the usability of such systems mainly depends on an accurate camera calibration. In this work a calibration process for ToF-cameras with respect to the intrinsic parameters, the depth measurement distortion and the pose of the camera relative to a robot's endeffector is described. the calibration process is not only based on the monochromatic images of the camera but also uses its depth values that are generated from a chequer-board pattern. the robustness and precision of the presented method is assessed applying it to randomly selected shots and comparing the calibrated measurements to a ground truth obtained from a laser scanner.

关键词： Calibration Cameras Robot vision systems Optical distortion Optical sensors Distortion measurement Time measurement Object detection Image processing Layout

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Modeling the structure of multivariate manifolds: Shape Maps

Modeling the structure of multivariate manifolds: Shape Maps

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Georg Langs Nikos Paragios Laboratoire MAS Ecole Centrale de Paris Equipe GALEN INRIA Saclay-Ile-de-France Chatenay-Malabry France

We propose a shape population metric that reflects the interdependencies between points observed in a set of examples. It provides a notion of topology for shape and appearance models that represents the behavior of individual observations in a metric space, in which distances between points correspond to their joint modeling properties. A Markov chain is learnt using the description lengths of models that describe sub sets of the entire data. the according diffusion map or shape map provides for the metric that reflects the behavior of the training population. With this metric functional clustering, deformation- or motion segmentation, sparse sampling and the treatment of outliers can be dealt with in a unified and transparent manner. We report experimental results on synthetic and real world data and compare the framework with existing specialized approaches.

关键词： Shape Topology Brain modeling Deformable models Buildings Motion segmentation computer vision Biomedical imaging Image segmentation Extraterrestrial measurements

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