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检索条件"任意字段=IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops"

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3D model search and pose estimation from single images using VIP features

3D model search and pose estimation from single images using...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Changchang Wu Friedrich Fraundorfer Jan-Michael Frahm Marc Pollefeys Department of Computer Science UNC Chapel Hill USA Department of Computer Science ETH Zurich Switzerland

This paper describes a method to efficiently search for 3D models in a city-scale database and to compute the camera poses from single query images. The proposed method matches SIFT features (from a single image) to viewpoint invariant patches (VIP) from a 3D model by warping the SIFT features approximately into the orthographic frame of the VIP features. This significantly increases the number of feature correspondences which results in a reliable and robust pose estimation. We also present a 3D model search tool that uses a visual word based search scheme to efficiently retrieve 3D models from large databases using individual query images. Together the 3D model search and the pose estimation represent a highly scalable and efficient city-scale localization system. The performance of the 3D model search and pose estimation is demonstrated on urban image data.

关键词： Image databases Spatial databases Visual databases Cameras computer science Robustness Information retrieval Feature extraction Image retrieval Image matching

来源：评论

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Regularizing optical-flow computation using tensor theory and complex analysis

Regularizing optical-flow computation using tensor theory an...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dan Koppel Chang-Ming Tsai Yuan-Fang Wang Department of Computer Science University of California Santa Barbara USA STI Medical Systems Honolulu HI USA

This paper reports a technique that improves the robustness and accuracy in computing dense optical-flow fields. We propose a global formulation with a regularization term. The regularization expressions are derived based on tensor theory and complex analysis. It is shown that while many regularizers have been proposed (image-driven, flow-driven, homogeneous, inhomogeneous, isotropic, anisotropic), they are all variations of a single base expression nablaunablau T + nablavnablav T . These regularizers, strictly speaking, are valid for uniform 2D translational motion only, because what they do essentially is to penalize changes in a flow field. However, many flow patterns-such as rotation, zoom, and their combinations, induced by a 3D rigid-body *** not constant. The traditional regularizers then incorrectly penalize these legal flow patterns and result in biased estimates. The purpose of this work is then to derive a new suite of regularization expressions that treat all valid flow patterns resulting from a 3D rigid-body motion equally, without unfairly penalizing any of them.

关键词： Optical computing Tensile stress Cameras Robustness Law Legal factors Anisotropic magnetoresistance Layout computer science Geometrical optics

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A new framework for behavior modeling of organs and soft tissue using the Boundary-Element Methods

A new framework for behavior modeling of organs and soft tis...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dan Koppel Shiv Chandrasekaran Yuan-Fang Wang Department of Computer Science University of California USA Sti Medical Systems Honolulu USA Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Department of Computer Science University of California Santa Barbara USA

In this paper, a method for modeling the deformation behavior of organs and soft tissue is presented. The purpose is to predict the global deformation effect that arbitrary, time-varying external perturbations have on an organ. The perturbation might be caused by an instrument (e.g., through the surgeonpsilas grasping and pinching actions), or it might be from organ-organ, organ-body-wall collisions in a bodily cavity. A methodology, employing (1) a surface representation based on the Boundary-Element Method-or BEM, of the deformation equations and (2) recently developed linear-algebra techniques (known as the ldquoHierarchical Semi-Separablerdquo matrix representation-or HSS), is proposed. We demonstrate that the proposed framework achieves an almost linear time complexity of O(n 1.14 ), a significant speed up comparing to the traditional O(n 3 ) schemes employing brute-force linear-algebra solution methods based on Finite-Element Method (FEM) formulations. Furthermore, unlike some previous approach, no restriction is placed on the external perturbation pattern and how it can change over time.

关键词： Biological tissues Deformable models Instruments Surges Finite element methods Surgery computer science Biological materials Integral equations History

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A dual-layer estimator architecture for long-term localization

A dual-layer estimator architecture for long-term localizati...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Anastasios I. Mourikis Stergios I. Roumeliotis Department of Computer Science and Engineering University of Minnesota USA

In this paper, we present a localization algorithm for estimating the 3D position and orientation (pose) of a moving vehicle based on visual and inertial measurements. The main advantage of the proposed method is that it provides precise pose estimates at low computational cost. This is achieved by introducing a two-layer estimation architecture that processes measurements based on their information content. Inertial measurements and feature tracks between consecutive images are processed locally in the first layer (multi-state-constraint Kalman filter) providing estimates for the motion of the vehicle at a high rate. The second layer comprises a bundle adjustment iterative estimator that operates intermittently so as to (i) reduce the effect of the linearization errors, and (ii) update the state estimates every time an area is re-visited and features are re-detected (loop closure). Through this process reliable state estimates are available continuously, while the estimation errors remain bounded during long-term operation. The performance of the developed system is demonstrated in large-scale experiments, involving a vehicle localizing within an urban area.

关键词： Vehicles State estimation computer architecture Position measurement Computational efficiency Motion measurement Tracking Motion estimation Error correction Estimation error

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Incremental estimation without specifying a-priori covariance matrices for the novel parameters

Incremental estimation without specifying a-priori covarianc...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Christian Beder Richard Steffen Computer Science Department Kiel University Germany Department of Photogrammetry Bond University Germany

We will present a novel incremental algorithm for the task of online least-squares estimation. Our approach aims at combining the accuracy of least-squares estimation and the fast computation of recursive estimation techniques like the Kalman filter. Analyzing the structure of least-squares estimation we devise a novel incremental algorithm, which is able to introduce new unknown parameters and observations into an estimation simultaneously and is equivalent to the optimal overall estimation in case of linear models. It constitutes a direct generalization of the well-known Kalman filter allowing to augment the state vector inside the update step. In contrast to classical recursive estimation techniques no artificial initial covariance for the new unknown parameters is required here. We will show, how this new algorithm allows more flexible parameter estimation schemes especially in the case of scene and motion reconstruction from image sequences. Since optimality is not guaranteed in the non-linear case we will also compare our incremental estimation scheme to the optimal bundle adjustment on a real image sequence. It will be shown that competitive results are achievable using the proposed technique.

关键词： Covariance matrix Recursive estimation Parameter estimation Image sequences Equations Algorithm design and analysis Image reconstruction Motion estimation computer science Vectors

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Non-ideal iris segmentation using graph cuts

Non-ideal iris segmentation using graph cuts

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shrinivas J. Pundlik Damon L. Woodard Stanley T. Birchfield Electrical and Computer Engineering Department Clemson University Clemson SC USA School of Computing Image and Video Analysis Laboratory Clemson SC USA

A non-ideal iris segmentation approach using graph cuts is presented. Unlike many existing algorithms for iris localization which extensively utilize eye geometry, the proposed approach is predominantly based on image intensities. In a step-wise procedure, first eyelashes are segmented from the input images using image texture, then the iris is segmented using grayscale information, followed by a post-processing step that utilizes eye geometry to refine the results. A preprocessing step removes specular reflections in the iris, and image gradients in a pixel neighborhood are used to compute texture. The image is modeled as a Markov random field, and a graph cut based energy minimization algorithm [2] is used to separate textured and untextured regions for eyelash segmentation, as well as to segment the pupil, iris, and background using pixel intensity values. The algorithm is automatic, unsupervised, and efficient at producing smooth segmentation regions on many non-ideal iris images. A comparison of the estimated iris region parameters with the ground truth data is provided.

关键词： Iris Image segmentation Eyelashes Pixel Image texture Gray-scale Information geometry Reflection Markov random fields Minimization methods

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Multi-fiber reconstruction from DW-MRI using a continuous mixture of von Mises-Fisher distributions

Multi-fiber reconstruction from DW-MRI using a continuous mi...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ritwik Kumar Angelos Barmpoutis Baba C. Vemuri Paul R. Carney Thomas H. Mareci Department of Computer and Information Science and Engineering University of Florida USA Department of Pediatrics University of Florida USA Department of Biochemistry and Molecular Biology University of Florida USA

In this paper we propose a method for reconstructing the Diffusion Weighted Magnetic Resonance (DW-MR) signal at each lattice point using a novel continuous mixture of von Mises-Fisher distribution functions. Unlike most existing methods, neither does this model assume a fixed functional form for the MR signal attenuation (e.g. 2nd or 4th order tensor) nor does it arbitrarily fix important mixture parameters like the number of components. We show that this continuous mixture has a closed form expression and leads to a linear system which can be easily solved. Through extensive experimentation with synthetic data we show that this technique outperforms various other state-of-the-art techniques in resolving fiber crossings. Finally, we demonstrate the effectiveness of this method using real DW-MRI data from rat brain and optic chiasm.

关键词： Tensile stress Signal resolution Attenuation Diffusion tensor imaging Solid modeling Distributed computing Distribution functions Linear systems Fourier transforms Magnetic field measurement

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CTREC: C-arm tracking and reconstruction using elliptic curves

CTREC: C-arm tracking and reconstruction using elliptic curv...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Gouthami Chintalapani Ameet K. Jain David H. Burkhardt Jerry L. Prince Gabor Fichtinger Johns Hopkins University USA Philips Research North America USA Haverford College USA Queen's University Canada

C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional information, attributable to the difficulty in obtaining the pose of X-ray images in 3D space. We propose a unified mathematical framework to address the issues of intra-operative pose estimation, correspondence and reconstruction, using simple elliptic curves. In contrast to other fiducial-based tracking methods, our method uses a single ellipse to constrain 5 out of 6 degrees of freedom of C-arm pose, along with randomly distributed unknown points in the imaging volume (either naturally present or induced by randomly placed beads or other markers in the image space) from two images/views to completely recover the Carm pose. Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0.51. and 0.12. STD. The method appears to be sufficiently accurate and appealing for many clinical applications, since it uses a simple elliptic fiducial coupled with patient information and has very minimal interference with the workspace.

关键词： Elliptic curves Image reconstruction Calibration Optical imaging Nonlinear distortion computer vision Optical distortion Image segmentation North America Educational institutions

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Geometric modeling of rigid and non-rigid 3D shapes using the global geodesic function

Geometric modeling of rigid and non-rigid 3D shapes using th...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Djamila Aouada David W. Dreisigmeyer Hamid Krim Electrical and Computer Engineering Department North Carolina State University Raleigh NC USA Los Alamos National Laboratory Los Alamos NM USA

In this paper, we present a novel intrinsic geometric representation of 3D objects. We add the proposed modeling of objects to their topological graphs to ensure a full and compact description necessary for shape-based retrieval, recognition and analysis of 3D models. In our approach, we address the challenges due to pose variability, computational complexity and noisy data by intrinsically and simply describing a 3D object by a global geodesic function. We exploit the geometric features contained in the dense set of iso-levels of this function. Using Whitney easy embedding theorem, we embed the manifold of the geodesic iso-levels in Ropf 3 and obtain a single space curve as our geometry descriptor. 3D shape comparison is then reduced to comparing the resulting modeling curves. To quantify the dissimilarities between them we simply compute an L 2 distance between classical Euclidian invariants applied to space curves. The experimental results show that in addition to being straightforward and easy to compute, our modeling technique achieves a high level of discrimination, and appears to be robust to both noise and decimation.

关键词： Solid modeling Shape

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Learning a scene contextual model for tracking and abnormality detection

Learning a scene contextual model for tracking and abnormali...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Benjamin Yao Liang Wang Song-chun Zhu Department of Statistics University of California Los Angeles USA Lotus Hill Institute of Computer Vision and Information Science Ezhou China

In this paper we present a novel framework for learning contextual motion model involving multiple objects in far-field surveillance video and apply the learned model to improving the performance of objects tracking and abnormal event detection. We represent trajectory of multiple objects by a 3D graph G in x,y,t, which is augmented by a number of spatio-temporal relations (links) between moving and static objects in the scene (e.g. relation between crosswalk, pedestrian and car). An inhomogeneous Markov model p is defined over G, whose parameters are estimated by MLE method and relations are pursued by a minimax entropy principle (as in texture modeling) [16] so that we can synthesize entirely new video sequences that reproduce the observed statistics from training video. With the learned model, we define the abnormality of a subgraph given its neighborhood by log-likelihood ratio test, which is estimated by importance sampling. The learned model is applied to tracking and abnormal event detection. Our experiments show that the learned model improve tracking performance and detect sophisticated abnormal events like traffic rule violation.

关键词： Layout Context modeling Event detection Surveillance Tracking Parameter estimation Maximum likelihood estimation Minimax techniques Entropy Video sequences

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