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Robust and adaptive integration of multiple range images with photometric attributes

Robust and adaptive integration of multiple range images wit...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： R. Sagawa K. Nishino K. Ikeuchi Institute of Industrial Science University of Tokyo Japan

ISBN: (纸本)0769512720

Integration of multiple range images is important to make use of 3D data acquired from stereo systems, laser range finders, etc. We propose a new range image integration method based on volumetric representation. Unlike other volume-based integration methods, we adaptively subdivide voxels depending on the curvature of the surface to be reconstructed, providing efficient representation of the underlying geometry and efficient use of computational resources. In our range image merging framework, additional attributes, e.g., color, laser reflectance power, etc., can be taken into account as well as 3D geometric information. This ability allows us to generate 3D models preserving sharp edges around texture boundaries, thereby providing a good basis for efficient rendering and texture mapping. The overall framework is designed to be robust against noise, taking consensus carefully in both geometry and color, which could be suitable for 3D model reconstruction from noisy stereo images. In this paper, we describe the system, and present several results of applying our framework to real data. We also present some other future applications based on our framework.

关键词： Robustness Photometry Colored noise image reconstruction Surface reconstruction Geometrical optics Computational geometry Merging Laser modes Laser noise

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Tracking and modeling non-rigid objects with rank constraints

Tracking and modeling non-rigid objects with rank constraint...

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conference on computer Vision and pattern recognition (CVPR)

作者： L. Torresani D.B. Yang E.J. Alexander C. Bregler Computer Science Department University of Stanford Stanford CA USA Mechanical Engineering Department University of Stanford Stanford CA USA

ISBN: (纸本)0769512720

This paper presents a novel solution for flow-based tracking and 3D reconstruction of deforming objects in monocular image sequences. A non-rigid 3D object undergoing rotation and deformation can be effectively approximated using a linear combination of 3D basis shapes. This puts a bound on the rank of the tracking matrix. The rank constraint is used to achieve robust and precise low-level optical flow estimation without prior knowledge of the 3D shape of the object. The bound on the rank is also exploited to handle occlusion at the tracking level leading to the possibility of recovering the complete trajectories of occluded/disoccluded points. Following the same low-rank principle, the resulting flow matrix can be factored to get the 3D pose, configuration coefficients, and 3D basis shapes. The flow matrix is factored in an iterative manner, looping between solving for pose, configuration, and basis shapes. The flow-based tracking is applied to several video sequences and provides the input to the 3D non-rigid reconstruction task. Additional results on synthetic data and comparisons to ground truth complete the experiments.

关键词： Shape Tracking Humans Optical noise Deformable models Robustness Video sequences image motion analysis Principal component analysis computer science

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A computer vision system for on-screen item selection by finger pointing

A computer vision system for on-screen item selection by fin...

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conference on computer Vision and pattern recognition (CVPR)

作者： M.S. Lee D. Weinshall E. Cohen-Solal A. Colmenarez D. Lyons Philips Research Briarcliff Manor NY USA

ISBN: (纸本)0769512720

Pointing at planar surfaces such as TV and computer monitors or projection screens can be a useful mode of interaction between humans and machines. To a large extent what seems to hinder the use of vision in such practical applications is the difficulty of the computational task, which is typically defined as 3-D reconstruction from uncalibrated 2-D images of a non-static scene. We describe below two designs where, using one or two cameras, the target of pointing on a flat monitor or screen is identified without 3-D inference, using only image morphing and line intersection. This is accomplished by registering the images with the target plane. When used to identify a pointing target on a surface hidden from the camera (e.g., a computer monitor which supports the camera itself as in most PC configurations), we add aperture(s) coplanar with the target surface in front of the camera(s). We describe experimental results showing a fully automated procedure for pointing target detection with high accuracy. The simplicity of our method and its robustness, as well as the relative accuracy of our results, can make pointing a practical means of human-machine interaction.

关键词： computer vision Fingers Cameras computer displays Surface reconstruction TV Humans Application software Three dimensional displays Layout

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Covariance scaled sampling for monocular 3D body tracking

Covariance scaled sampling for monocular 3D body tracking

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conference on computer Vision and pattern recognition (CVPR)

作者： C. Sminchisescu B. Triggs GRAVIR-INRIA Montbonnot Saint Martin France

ISBN: (纸本)0769512720

We present a method for recovering 3D human body motion from monocular video sequences using robust image matching, joint limits and non-self-intersection constraints, and a new sample-and-refine search strategy guided by rescaled cost-function covariances. Monocular 3D body tracking is challenging: for reliable tracking at least 30 joint parameters need to be estimated, subject to highly nonlinear physical constraints; the problem is chronically ill conditioned as about 1/3 of the d.o.f. (the depth-related ones) are almost unobservable in any given monocular image; and matching an imperfect, highly flexible self-occluding model to cluttered image features is intrinsically hard. To reduce correspondence ambiguities we use a carefully designed robust matching-cost metric that combines robust optical flow, edge energy, and motion boundaries. Even so, the ambiguity, nonlinearity and non-observability make the parameter-space cost surface multi-modal, unpredictable and ill conditioned, so minimizing it is difficult. We discuss the limitations of CONDENSATION-like samplers, and introduce a novel hybrid search algorithm that combines inflated-covariance-scaled sampling and continuous optimization subject to physical constraints. Experiments on some challenging monocular sequences show that robust cost modelling, joint and self-intersection constraints, and informed sampling are all essential for reliable monocular 3D body tracking.

关键词： Sampling methods Robustness Joints Costs image sampling Humans Video sequences image matching Optical design Nonlinear optics

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Geocoded terrestrial mosaics using pose sensors and video registration

Geocoded terrestrial mosaics using pose sensors and video re...

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conference on computer Vision and pattern recognition (CVPR)

作者： S. Hsu Sarnoff Corporation Princeton NJ USA

ISBN: (纸本)0769512720

The paper presents a complete algorithm for building geocoded terrestrial mosaics from aerial video accompanied by GPS/INS readings, without relying on ground survey points or reference imagery to provide geographic control. The 2D mosaic-to-video frame mappings are jointly estimated by bundle adjustment of constraints from pose sensor data and interframe registrations. Multiple-swath video collections are handled by automatically registering spatially adjacent frames across swaths. The proposed approach optimally combines the pose and interframe constraints for geocoding, unlike existing 2D mosaic techniques, while avoiding the complexity of 3D reconstruction. The method was validated on two highly dissimilar operating scenarios, with quantitative evaluation against ground truth supplied by known geocoded reference imagery. One test over hilly terrain found median mosaic continuity and geocoding errors of 1.6 m and 3.1 m, respectively, which is acceptable for many tasks. Characterization of such errors is essential for acceptance of this video mosaic process in critical geospatial applications.

关键词： Cameras Telemetry Global Positioning System Aircraft manufacture Layout Length measurement Position measurement Assembly Automatic control image reconstruction

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Improving the scope of deformable model shape and motion estimation

Improving the scope of deformable model shape and motion est...

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conference on computer Vision and pattern recognition (CVPR)

作者： C. Sminchisescu D. Metaxas S. Dickinson INRIA Rhône-Alpes Montbonnot Saint Martin France University of Pennsylvania Philadelphia USA University of Toronto Toronto Canada

ISBN: (纸本)0769512720

Previous approaches to deformable model shape estimation and tracking have assumed a fixed class of shapes representation (e.g., deformable superquadrics), initialized prior to tracking. Since the shape coverage of the model is fixed, such approaches do not directly accommodate incremental representation discovery during tracking. As a result, model shape coverage is decoupled from tracking, thereby limiting both processes in terms of scope and robustness. We present a novel deformable model framework that accommodates the incremental incorporation during tracking of new geometric primitives (lines, in addition to points) that are not explicitly captured in the initial deformable model but that are moving consistently with its image motion. As these new features are detected via consistency checks, they are added to the model, providing incremental soft constraints on the estimation of its rigid parameters. The consistency checks are based on trilinear relationships between geometric primitives. Consequently, we not only increase both model scope and, ultimately, its higher-level shape coverage, but improve tracking robustness and accuracy, by directly employing the new features in both forward prediction and reconstruction. Our new formulation is a step towards automating model shape estimation and tracking, since it requires significantly reduced initial model hand-crafting. We demonstrate our approach on two separate image-based tracking domains, each involving complex 3D object shape and motion.

关键词： Deformable models Shape Motion estimation Tracking Solid modeling Robustness image reconstruction Power system modeling Educational institutions computer vision

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A high-resolution panoramic camera

A high-resolution panoramic camera

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conference on computer Vision and pattern recognition (CVPR)

作者： Hong Hua N. Ahuja Beckman Institute University of Illinois Urbana-Champaign Urbana IL USA Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL USA

ISBN: (纸本)0769512720

Wide field of view (FOV) and high resolution are two desirable properties in many vision-based applications such as tele-conferencing, surveillance, and robot navigation. In some applications, such as 3D reconstruction and rendering, it is also desired that all viewing directions share a single viewpoint, the entire FOV be imaged simultaneously, in real-time, and the depth of field be large. In this paper, we review such a panoramic camera proposed by Nalwa in 1996 that uses reflections off planar mirrors to achieve the first four of the aforementioned capabilities He uses a single mirror pyramid (SMP) and a number of cameras that point to the individual pyramid faces. Together the cameras yield a visual field having a width of 360 degrees and a height same as that of the individual cameras. We propose a double mirror-pyramid (DALP) design that still achieves a 360-degree FOV horizontally but doubles the vertical FOV. It retains the other three capabilities namely high resolution, a single apparent viewpoint across the entire FOV, and real-time panoramic capture. We specify the visual field mapping from the scene to the sensor realized by, the proposed camera. Finally, an implementation of the proposed DMP design is described and examples of preliminary panoramic images obtained are included.

关键词： Cameras Robot vision systems Mirrors Teleconferencing Surveillance Navigation image reconstruction Rendering (computer graphics) Reflection Helium

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Epipolar geometry estimation for non-static scenes by 4D tensor voting

Epipolar geometry estimation for non-static scenes by 4D ten...

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conference on computer Vision and pattern recognition (CVPR)

作者： Wai-Shun Tong Chi-Keung Tang G. Medioni Hong Kong University of Science and Technology Hong Kong China University of Southern California Los Angeles CA USA

ISBN: (纸本)0769512720

In the presence of false matches and moving objects, image registration is challenging, as outlier rejection, matching and registration become interdependent. We present an efficient and robust method, 4D tensor voting to estimate epipolar geometries for non-static scenes, and identify matching points due to salient and independent motions. Unlike other optimization techniques, data communication in 4D tensor voting does not involve any iterative search. Thus, initialization, local optimum, convergence, and dimensionality of parameter space are not problematic. Like the 8D counterpart, the only assumption we make is the pinhole camera model. Two advancements are made in this work. First, we reduce the dimensionality, and the 4D joint image space is an isotropic and orthogonal one, validating the general assumptions of tensor voting. This improvement is evidenced by the facts that only two passes are needed, and that 4D tensor voting can tolerate an even larger noise/signal ratio (up to a ratio of five). Second, instead of discarding motion pixels as outliers, we successively extract the epipolar geometries contributed by the static background and by the matching points due to salient motions. Only two frames are needed, and no simplifying assumption (such as affine camera model or homographic model between images) is made. Our 4D algorithm consists of two stages: local continuity constraint propagation to remove outliers, and global consistency checking to localize a 4D topological point cone. Results on challenging datasets are presented.

关键词： Geometry Layout Tensile stress Voting Cameras image registration Robustness Motion estimation Data communication Convergence

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Abnormal cell detection using the Choquet integral

Abnormal cell detection using the Choquet integral

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conference of the North American Fuzzy Information processing.society - NAFIPS

作者： R. Stanley J. Keller C.W. Caldwell P. Gader Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA Department of Computer Engineering and Computer Science University of Missouri Columbia Columbia MO USA Department of Pathology and Anatomical Sciences University of Missouri Columbia Columbia MO USA Department of Computer Engineering and Computer Science University of Missouri Columbia Columbia MO USA

Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes 1-22 and the X chromosome for females. Many genetic abnormalities are directly linked to structural and/or numerical aberrations of chromosomes within metaphase spreads. Cells with the Philadelphia chromosome contain an abnormal chromosome for class 9 and for class 22, leaving a single normal chromosome for each class. A data-driven homologue matching technique is applied to recognizing normal chromosomes from classes 9 and 22. Homologue matching integrates neural networks, dynamic programming and the Choquet integral for chromosome recognition. The inability to locate matching homologous pairs for classes 9 and 22 provides an indication that the cell is abnormal, potentially containing the Philadelphia chromosome. Applying this technique to 50 normal and to 48 abnormal cells containing the Philadelphia chromosome yields 100.0% correct abnormal cell detection with a 24.0% false positive rate.

关键词： Biological cells Cells (biology) Genetics Neural networks Humans Dynamic programming image analysis Pathology pattern matching Fuses

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3D biplanar reconstruction of scoliotic vertebrae using statistical models

3D biplanar reconstruction of scoliotic vertebrae using stat...

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conference on computer Vision and pattern recognition (CVPR)

作者： S. Benameur M. Mignotte S. Parent H. Labelle W. Skalli J.A. De Guise Laboratoire de recherche en imagerie et orthopédie CRCHUM Hôpital Notre Dame Montreal Canada Laboratoire d'imagerie en scoliose 3D Centre de recherche Hôpital Sainte Justine Montreal Canada Ecole de Technologie Supérieure Montreal Canada Laboratoire de vision et modélisation géométrique DIRO Université de Montréal Montréal Canada Laboratoire de biomécanique Ecole Nationale Supérieure dArts et Métiers Paris France

ISBN: (纸本)0769512720

This paper presents a new 3D reconstruction method of the scoliotic vertebrae of a spine, using two conventional radiographic views (postero-anterior and lateral), and global prior knowledge on the geometrical structure of each vertebra. This geometrical knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in the Karhunen-Loeve expansion of the pathological deformations observed on a representative scoliotic vertebra population. The proposed reconstruction method consists in fitting the projections of this deformable template with the segmented contours of the corresponding vertebra on the two radiographic views. The 3D reconstruction problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. The reconstruction of the spine is then made vertebra by vertebra. This 3D reconstruction method has been successfully tested on several biplanar radiographic images, yielding very promising results.

关键词： Spine image reconstruction Radiography Reconstruction algorithms Pathology Cost function Deformable models Orthopedic surgery Solid modeling Art

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