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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1351-1360 订阅

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Learning 4D Action Feature Models for Arbitrary View Action recognition

Learning 4D Action Feature Models for Arbitrary View Action ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Pingkun Yan Saad M. Khan Mubarak Shah Computer Vision Laboratory University of Central Florida Orlando FL USA

In this paper we present a novel approach using a 4D (x,y,z,t) action feature model (4D-AFM) for recognizing actions from arbitrary views. the 4D-AFM elegantly encodes shape and motion of actors observed from multiple views. the modeling process starts with reconstructing 3D visual hulls of actors at each time instant. Spatiotemporal action features are then computed in each view by analyzing the differential geometric properties of spatio-temporal volumes (3D STVs) generated by concatenating the actor's silhouette over the course of the action (x,y,t). these features are mapped to the sequence of 3D visual hulls over time (4D) to build the initial 4D-AFM. Actions are recognized based on the scores of matching action features from the input videos to the model points of 4D-AFMs by exploiting pairwise interactions of features. Promising recognition results have been demonstrated on the multi-view IXMAS dataset using both single and multi-view input videos.

关键词： Shape Videos Object recognition Humans computer vision Image reconstruction Spatiotemporal phenomena Costs Cameras Graphical models

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Cell Motion Analysis Without Explicit Tracking

Cell Motion Analysis Without Explicit Tracking

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

作者： Richard Souvenir Jerrod Kraftchick Sangho Lee Mark G. Clemens Min C. Shin Department of Computer Science North Carolina State University Charlotte USA Department of Biology North Carolina State University Charlotte USA

Automated cell tracking using in vivo imagery is difficult, in general, due to the noise inherent in the imaging process, occlusions, varied cell appearance over time, motion of other tissue (distractors), and cells traveling in and out of the image plane. For certain types of cells these problems are exacerbated due to erratic motion patterns. In this paper, we introduce the Radial Flow Transform, which provides motion estimates for objects of interest in a scene without explicitly tracking each object. the transform is robust to misdetected objects, temporally-disjoint motion events, and can represent multiple directions of flow at a single location. We provide operations to convert to and from a vector field representation. this allows for intuitive reasoning about the motion patterns in a scene. We demonstrate results on synthetic data and in vivo microscopy video of a mouse liver.

关键词： Motion analysis Tracking Cells (biology) In vivo Motion estimation Layout Microscopy Biomedical imaging Roads Mice

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Discriminative Modeling by Boosting on Multilevel Aggregates

Discriminative Modeling by Boosting on Multilevel Aggregates

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

作者： Jason J. Corso Computer Science and Engineering Suny at Buffalo Buffalo USA

this paper presents a new approach to discriminative modeling for classification and labeling. Our method, called Boosting on Multilevel Aggregates (BMA), adds a new class of hierarchical, adaptive features into boosting-based discriminative models. Each pixel is linked with a set of aggregate regions in a multilevel coarsening of the image. the coarsening is adaptive, rapid and stable. the multilevel aggregates present additional information rich features on which to boost, such as shape properties, neighborhood context, hierarchical characteristics, and photometric statistics. We implement and test our approach on three two-class problems: classifying documents in office scenes, buildings and horses in natural images. In all three cases, the majority, about 75%, of features selected during boosting are our proposed BMA features rather than patch-based features. this large percentage demonstrates the discriminative power of the multilevel aggregate features over conventional patch-based features. Our quantitative performance measures show the proposed approach gives superior results to the state-of-the-art in all three applications.

关键词： Boosting Aggregates Labeling Pixel Shape Photometry Statistics Testing Layout Horses

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Beyond the Lambertian Assumption: A generative model for Apparent BRDF fields of Faces using Anti-Symmetric Tensor Splines

Beyond the Lambertian Assumption: A generative model for App...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Angelos Barmpoutis Ritwik Kumar Baba C. Vemuri Arunava Banerjee Department of Computer and Information Science and Engineering University of Florida USA

Human faces are neither exactly Lambertian nor entirely convex and hence most models in literature which make the Lambertian assumption, fall short when dealing with specularities and cast shadows. In this paper, we present a novel anti-symmetric tensor spline (a spline for tensor-valued functions) based method for the estimation of the Apparent BRDF (ABRDF) field for human faces that seam-lessly accounts for specularities and cast shadows. Furthermore, unlike other methods, it does not require any 3D information to build the model and can work with as few as 9 images. In order to validate the accuracy of our anti-symmetric tensor spline model, we present a novel approximation of the ABRDF using a continuous mixture of single-lobed spherical functions. We demonstrate the effectiveness of our anti-symmetric tensor-spline model in comparison to other popular models in the literature, by presenting extensive results for face relighting and face recognition using the Extended Yale B database.

关键词： Tensile stress Humans Lighting Rendering (computer graphics) Image databases Shape control Information science Face detection Face recognition Graphics

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On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery

On Benchmarking Camera Calibration and Multi-View Stereo for...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： C. Strecha W. von Hansen L. Van Gool P. Fua U. thoennessen CVLab EPFL Lausanne Switzerland FGAN-FOM Ettlingen Germany CVLab ETHZ Zurich Switzerland

In this paper we want to start the discussion on whether image based 3-D modelling techniques can possibly be used to replace LIDAR systems for outdoor 3D data acquisition. Two main issues have to be addressed in this context: (i) camera calibration (internal and external) and (ii) dense multi-view stereo. To investigate both, we have acquired test data from outdoor scenes both with LIDAR and cameras. Using the LIDAR data as reference we estimated the ground-truth for several scenes. Evaluation sets are prepared to evaluate different aspects of 3D model building. these are: (i) pose estimation and multi-view stereo with known internal camera parameters;(ii) camera calibration and multi-view stereo with the raw images as the only input and (iii) multi-view stereo.

关键词： Cameras Calibration Image resolution Laser radar Layout three dimensional displays Data acquisition Rendering (computer graphics) Shape measurement Clouds

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General Constraints for Batch Multiple-Target Tracking Applied to Large-Scale Videomicroscopy

General Constraints for Batch Multiple-Target Tracking Appli...

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

作者： Kevin Smith Alan Carleton Vincent Lepetit Brain Mind Institute École Polytechnique Fédérale de Lausanne (EPFL) Flavour Perception Group Lausanne Switzerland Brain Mind Institute École Polytechnique Fédérale de Lausanne (EPFL) Computer Vision Laboratory Lausanne Switzerland

While there is a large class of Multiple-Target Tracking (MTT) problems for which batch processing is possible and desirable, batch MTT remains relatively unexplored in comparison to sequential approaches. In this paper, we give a principled probabilistic formalization of batch MTT in which we introduce two new, very general constraints that considerably help us in reaching the correct solution. First, we exploit the correlation between the appearance of a target and its motion. Second, entrances and departures of targets are encouraged to occur at the boundaries of the scene. We show how to implement these constraints in a formal and efficient manner. Our approach is applied to challenging 3-D biomedical imaging data where the number of targets is unknown and may vary, and numerous challenging tracking events occur. We demonstrate the ability of our model to simultaneously track the nuclei of over one hundred migrating neuron precursor cells in image stack series collected from a 2-photon microscope.

关键词： Large-scale systems Target tracking Neurons Layout Biomedical imaging Microscopy computer vision Laboratories Image analysis Biomedical computing

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Boosting Ordinal Features for Accurate and Fast Iris recognition

Boosting Ordinal Features for Accurate and Fast Iris Recogni...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Zhaofeng He Zhenan Sun Tieniu Tan Xianchao Qiu Cheng Zhong Wenbo Dong Center for Biometrics and Security Research National Laboratory of Pattern RecognitionNational Laboratory of Pattern Recognition Chinese Academy and Sciences Beijing China

In this paper, we present a novel iris recognition method based on learned ordinal features. Firstly, taking full advantages of the properties of iris textures, a new iris representation method based on regional ordinal measure encoding is presented, which provides an over-complete iris feature set for learning. Secondly, a novel Similarity Oriented Boosting (SOBoost) algorithm is proposed to train an efficient and stable classifier with a small set of features. Compared with Adaboost, SOBoost is advantageous in that it operates on similarity oriented training samples, and therefore provides a better way for boosting strong classifiers. Finally, the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a 'cascade', by which the searching ability of iris recognition towards large-scale deployments is greatly enhanced. Extensive experiments on two challenging iris image databases demonstrate that the proposed method achieves state-of-the-art iris recognition accuracy and speed. In addition, SOBoost outperforms Adaboost (Gentle-Adaboost, JS-Adaboost, etc.) in terms of both accuracy and generalization capability across different iris databases.

关键词： Boosting Iris recognition Image databases Spatial databases Machine learning algorithms Large-scale systems Helium Sun Biometrics National security

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Flat Refractive Geometry

Flat Refractive Geometry

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

作者： Tali Treibitz Yoav Y. Schechner Hanumant Singh Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Woods Hole Oceanographic Institution Woods Hole MA USA

While the study of geometry has mainly concentrated on single-viewpoint (SVP) cameras, there is growing attention to more general non-SVP systems. Here we study an important class of systems that inherently have a non-SVP: a perspective camera imaging through an interface into a medium. Such systems are ubiquitous: they are common when looking into water-based environments. the paper analyzes the common flat-interface class of systems. It characterizes the locus of the viewpoints (caustic) of this class, and proves that the SVP model is invalid in it. this may explain geometrical errors encountered in prior studies. Our physics-based model is parameterized by the distance of the lens from the medium interface, beside the focal length. the physical parameters are calibrated by a simple approach that can be based on a single-frame. this directly determines the system geometry. the calibration is then used to compensate for modeled system distortion. Based on this model, geometrical measurements of objects are significantly more accurate, than if based on an SVP model. this is demonstrated in real-world experiments.

关键词： Geometry Cameras Marine animals computer vision Solid modeling Microscopy Robot vision systems Humans Stereo vision Marine technology

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Sequential Particle Swarm Optimization for Visual Tracking

Sequential Particle Swarm Optimization for Visual Tracking

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

作者： Xiaoqin Zhang Weiming Hu Steve Maybank Xi Li Mingliang Zhu National Laboratory of Pattern Recognition Institute of Automation Beijing China School of Computer Science and Information Systems Birkbeck College London UK

Visual tracking usually involves an optimization process for estimating the motion of an object from measured images in a video sequence. In this paper, a new evolutionary approach, PSO (particle swarm optimization), is adopted for visual tracking. Since the tracking process is a dynamic optimization problem which is simultaneously influenced by the object state and the time, we propose a sequential particle swarm optimization framework by incorporating the temporal continuity information into the traditional PSO algorithm. In addition, the parameters in PSO are changed adaptively according to the fitness values of particles and the predicted motion of the tracked object, leading to a favourable performance in tracking applications. Furthermore, we show theoretically that, in a Bayesian inference view, the sequential PSO framework is in essence a multi-layer importance sampling based particle filter. Experimental results demonstrate that, compared with the state-of-the-art particle filter and its variation-the unscented particle filter, the proposed tracking algorithm is more robust and effective, especially when the object has an arbitrary motion or undergoes large appearance changes.

关键词： Particle swarm optimization Particle tracking Particle filters Inference algorithms Motion estimation Motion measurement Video sequences Bayesian methods Nonhomogeneous media Monte Carlo methods

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FusionFlow: Discrete-Continuous Optimization for Optical Flow Estimation

FusionFlow: Discrete-Continuous Optimization for Optical Flo...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Victor Lempitsky Stefan Roth Carsten Rother Microsoft Research Cambridge UK Technical University of Darmstadt Germany

Accurate estimation of optical flow is a challenging task, which often requires addressing difficult energy optimization problems. To solve them, most top-performing methods rely on continuous optimization algorithms. the modeling accuracy of the energy in this case is often traded for its tractability. this is in contrast to the related problem of narrow-baseline stereo matching, where the top-performing methods employ powerful discrete optimization algorithms such as graph cuts and message-passing to optimize highly non-convex energies. In this paper, we demonstrate how similar non-convex energies can be formulated and optimized discretely in the context of optical flow estimation. Starting with a set of candidate solutions that are produced by fast continuous flow estimation algorithms, the proposed method iteratively fuses these candidate solutions by the computation of minimum cuts on graphs. the obtained continuous-valued fusion result is then further improved using local gradient descent. Experimentally, we demonstrate that the proposed energy is an accurate model and that the proposed discrete-continuous optimization scheme not only finds lower energy solutions than traditional discrete or continuous optimization techniques, but also leads to flow estimates that outperform the current state-of-the-art.

关键词： Image motion analysis Optical saturation Statistics Optimization methods State estimation Stereo vision Iterative algorithms Fuses Image sequences computer vision

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