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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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Globally optimal segmentation of interacting surfaces with geometric constraints

Globally optimal segmentation of interacting surfaces with g...

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conference on computer vision and pattern recognition (cvpr)

作者： Kang Li Xiaodong Wu D.Z. Chen M. Sonka Department of Electrical and Computer Engineering University of Iowa Iowa IA USA Department of Computer Science University of Texas-Pan American Edinburgh TX UK Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

Efficient detection of globally optimal surfaces representing object boundaries in volumetric datasets is important and remains challenging in many medical image analysis applications. We have developed an optimal surface detection method that is capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and several geometric constraints defining the surface smoothness and interrelations. The method solves the surface detection problems by transforming them into computing minimum s-t cuts in the derived edge-weighted directed graphs. The proposed algorithm has low-order polynomial complexity and is computationally efficient. The method has been validated on over 100 computer generated volumetric images and 96 CT-scanned datasets of different-sized plexiglas tubes, yielding highly accurate results (mean signed error of the measured inner- and outer-diameters of the plexiglas tubes was 0.21 /spl plusmn/ 3.20%). Our approach can be readily extended to higher dimensional image segmentation.

关键词： Image edge detection Image segmentation Object detection Biomedical imaging Optimal control Cost function Polynomials Image generation computer errors Volume measurement

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Dynamic geodesic snakes for visual tracking

Dynamic geodesic snakes for visual tracking

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conference on computer vision and pattern recognition (cvpr)

作者： M. Niethammer A. Tannenbaum School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Visual tracking using active contours is usually accomplished in a static framework. The active contour tracks the object of interest in a given frame of an image sequence, and then a subsequent prediction step ensures good initial placement for the next frame. This approach is unnatural, the curve evolution gets decoupled from the actual dynamics of the objects to be tracked. True dynamic approaches exist, all being marker particle based, and thus prone to the shortcomings of such particle-based implementations. In particular, topological changes are not handled naturally in this framework. The now "classical" level set approach is tailored for codimension one evolutions. However, dynamic curve evolution is at least of codimension two. We propose a natural, efficient, level set based approach for dynamic curve evolution which removes the artificial separation of segmentation and prediction, while retaining all the desirable properties of level set formulations. This is based on a new energy minimization functional which for the first time puts dynamics into the geodesic active contour framework.

关键词： Level set Active contours Equations Potential energy Geophysics computing Image sequences Convergence Image motion analysis Prediction algorithms Robustness

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Difference sphere: an approach to near light source estimation

Difference sphere: an approach to near light source estimati...

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conference on computer vision and pattern recognition (cvpr)

作者： T. Takai K. Niinuma A. Maki T. Matsuyama Graduate School of Informatics Kyoto University Sakyo Kyoto Japan

We present a novel approach for estimating lighting sources from a single image of a scene that is illuminated by near point light sources, directional light sources and ambient light. We propose to employ a pair of reference spheres as light probes and introduce the difference sphere that we acquire by differencing the intensities of two image regions of the reference spheres. Since the effect by directional light sources and ambient light is eliminated by differencing, the key advantage of considering the difference sphere is that it enables us to estimate near point light sources including their radiance, which has been difficult to achieve in previous efforts where only distant directional light sources were assumed. We also show that analysis of gray level contours on spherical surfaces facilitates separate identification of multiple combined light sources and is well suited to the difference sphere. Once we estimate point light sources with the difference sphere, we update the input image by eliminating their influence and then estimate other remaining light sources, that is, directional light sources and ambient light. We demonstrate the effectiveness of the entire algorithm with experimental results.

关键词： Light sources Layout Electrical capacitance tomography Lighting Probes Shape Laboratories Informatics computer vision computer graphics

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Covariance-driven mosaic formation from sparsely-overlapping image sets with application to retinal image mosaicing

Covariance-driven mosaic formation from sparsely-overlapping...

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conference on computer vision and pattern recognition (cvpr)

作者： Gehua Yang C.V. Stewart Rensselaer Polytechnic Institute Troy NY USA

A new technique is presented for mosaicing sparsely-overlapping image sets, with a target application of assisting the diagnosis and treatment of retinal diseases. The geometric image transformations required to construct the mosaics are estimated by (1) estimating the transformations between as many pairs of images as possible, (2) extracting sets of constraints (correspondences)from the successfully registered image pairs, and (3) using these constraint sets to simultaneously (jointly) estimate the final transformations. Unfortunately, this may not be sufficient to construct seamless mosaics when two images overlap but can not be successfully registered (step 1). This paper presents a new method to generate constraints between such image pairs, and use these constraints to estimate a more consistent set of transformations. For each pair transformation parameter covariance matrices are computed and used to estimate the mapping error covariance matrices for individual features from one image. These features are matched in the second image by minimizing the resulting Mahalanobis distance. The generated correspondences are validated using robust estimation techniques and used to refine the estimates. The steps of covariance computation, matching, and transform estimation are repeated for all relevant image pairs until the final alignment converges. Results are presented and evaluated for several difficult image sets to illustrate the efficacy of the techniques.

关键词： Retina Covariance matrix Degenerative diseases Diabetes Retinopathy computer vision Topology Robustness Image converters Humans

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Visual tracking using learned linear subspaces

Visual tracking using learned linear subspaces

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conference on computer vision and pattern recognition (cvpr)

作者： J. Ho Kuang-Chih Lee Ming-Hsuan Yang D. Kriegman Computer Science & Engineering University of California La Jolla CA USA Computer Science University of Illinois Urbana-Champaign IL USA Honda Research Institute Mountain View CA USA

This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image space. The tracker adaptively updates this subspace while tracking by finding a linear subspace that best approximates the observations made in the previous frames. Instead of the traditional L/sup 2/-reconstruction error norm which leads to subspace estimation using PCA or SVD, we argue that a variant of it, the uniform L/sup 2/-reconstruction error norm, is the right one for tracking. Under this framework we provide a simple and a computationally inexpensive algorithm for finding a subspace whose uniform L/sup 2/-reconstruction error norm for a given collection of data samples is below some threshold, and a simple tracking algorithm is an immediate consequence. We show experimental results on a variety of image sequences of people and man-made objects moving under challenging imaging conditions, which include drastic illumination variation, partial occlusion and extreme pose variation.

关键词： Target tracking Robustness Principal component analysis computer science Image sequences Lighting Algorithm design and analysis Image reconstruction Shape Reflectivity

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Making one object look like another: controlling appearance using a projector-camera system

Making one object look like another: controlling appearance ...

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conference on computer vision and pattern recognition (cvpr)

作者： M.D. Grossberg H. Peri S.K. Nayar P.N. Belhumeur Department of Computer Science Columbia University NY USA

We present a method for controlling the appearance of an arbitrary 3D object using a projector and a camera. Our goal is to make one object look like another by projecting a carefully determined compensation image onto the object. The determination of the appropriate compensation image requires accounting for spatial variation in the object's reflectance, the effects of environmental lighting, and the spectral responses, spatially varying fall-offs, and non-linear responses in the projector-camera system. Addressing each of these effects, we present a compensation method which calls for the estimation of only a small number of parameters, as part of a novel off-line radiometric calibration. This calibration is accomplished by projecting and acquiring a minimal set of 6 images, irrespective of the object. Results of the calibration are then used on-line to compensate each input image prior to projection. Several experimental results are shown that demonstrate the ability of this method to control the appearance of everyday objects. Our method has direct applications in several areas including smart environments, product design and presentation, adaptive camouflages, interactive education and entertainment.

关键词： Control systems Cameras Calibration Reflectivity computer science Radiometry Lighting computer displays Color Product design

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Multiple kernel tracking with SSD

Multiple kernel tracking with SSD

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conference on computer vision and pattern recognition (cvpr)

作者： G.D. Hager M. Dewan C.V. Stewart Johns Hopkins University Baltimore MD USA Rensselaer Polytechnic Institute Troy NY USA

Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms. This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. here is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.

关键词： Kernel Target tracking Optimization methods Histograms computer vision Robustness Density measurement Force measurement Video sequences Joining processes

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Eye typing off the shelf

Eye typing off the shelf

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conference on computer vision and pattern recognition (cvpr)

作者： D.W. Hansen A. Pece Department of Innovation IT-University of Copenhagen Copenhagen Denmark Heimdall Vision & Department of Computer Science University of Copenhagen Copenhagen Denmark

The goal of this work is using off-the-shelf components for gaze-based interaction, with focus on eye typing. Avoiding the use of dedicated hardware such as IR light emitters makes eye tracking significantly more difficult and requires robust methods capable of handling large changes in image quality. We employ an active-contour method to obtain robust iris tracking. The main strength of the method is that the contour model avoids explicit feature detection: contours are simply assumed to remove statistical dependencies on opposite sides of the contour. The contour model is utilized in an approach combining particle filtering with the EM algorithm. The method is robust against light changes and camera defocusing. For the purpose of determining where the user is looking calibrations is usually needed. The number of calibration points used in different methods varies from a few to several thousands, depending on the prior knowledge used on the setup and equipment We examine basic properties of gaze determination when the geometry of the camera, screen and user is unknown. In particular we present a lower bound on the number of calibration points needed for gaze determination on planar objects, and we examine degenerate configurations. Based on this lower bound we apply a simple calibration procedure, to facilitate button selections for fast on-screen typing.

关键词： Calibration Robustness Cameras Hardware Light emitting diodes Image quality Iris computer vision Filtering algorithms Geometry

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Learning object detection from a small number of examples: the importance of good features

Learning object detection from a small number of examples: t...

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conference on computer vision and pattern recognition (cvpr)

作者： K. Levi Y. Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

Face detection systems have recently achieved high detection rates and real-time performance. However, these methods usually rely on a huge training database (around 5,000 positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multi-viewface detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable state of the art performance using only a few hundred training examples. For profile view faces, local orientation histograms enable learning a system that seems to outperform the state of the art in real-time systems even with a small number of training examples.

关键词： Object detection Face detection Spatial databases Histograms Real time systems Testing Filters computer science Object oriented databases Image edge detection

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Point matching as a classification problem for fast and robust object pose estimation

Point matching as a classification problem for fast and robu...

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conference on computer vision and pattern recognition (cvpr)

作者： V. Lepetit J. Pilet P. Fua Computer Vision Laboratory Swiss Federal Institute of Technology Lausanne Switzerland

We propose a novel approach to point matching under large viewpoint and illumination changes that are suitable for accurate object pose estimation at a much lower computational cost than state-of-the-art methods. Most of these methods rely either on using ad hoc local descriptors or on estimating local affine deformations. By contrast, we treat wide baseline matching of key points as a classification problem, in which each class corresponds to the set of all possible views of such a point. Given one or more images of a target object, we train the system by synthesizing a large number of views of individual key points and by using statistical classification tools to produce a compact description of this view set. At run-time, we rely on this description to decide to which class, if any, an observed feature belongs. This formulation allows us to use a classification method to reduce matching error rates, and to move some of the computational burden from matching to training, which can be performed beforehand. In the context of pose estimation, we present experimental results for both planar and non-planar objects in the presence of occlusions, illumination changes, and cluttered backgrounds. We show that the method is both reliable and suitable for initializing real-time applications.

关键词： Robustness State estimation Lighting Runtime computer vision Computational efficiency Laboratories Error analysis Performance loss Books

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