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Computing layered surface representations: an algorithm for detecting and separating transparent overlays

Computing layered surface representations: an algorithm for ...

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conference on computer Vision and pattern recognition (CVPR)

作者： M. Singh Xiaolei Huang Psychology and Center for Cognitive Science Rutgers University-New Brunswick Piscataway NJ USA Computer and Information Sciences Rutgers University-New Brunswick Piscataway NJ USA

The biological visual system possesses the ability to compute layered surface representations, in which one surface is represented as being viewed through another. This ability is remarkable because, in scenes involving transparency, the link between surface topology and image topology is greatly complicated by the collapse of the photometric contributions of two distinct surfaces onto image intensity. Previous analysis of transparency has focused largely on the role of different kinds of junctions. Although junctions are important, they are not sufficient to predict layered surface structure. We present an algorithm that propagates local junction information by searching for chains of polarity-preserving junctions with consistent 'sidedness', and then propagates the transparency labeling into interior regions. The algorithm outputs a layered representation specifying: (i) the distinct surfaces, (ii) their depth ordering, and (iii) their surface attributes. We demonstrate the results of the algorithm on a number of images - both synthetic and real. We end by considering implications for related domains, such as shading.

关键词： Topology Biology computing Equations Visual system Photometry computer vision Psychology Cognitive science Layout Surface structures

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Surface reconstruction via Helmholtz reciprocity with a single image pair

Surface reconstruction via Helmholtz reciprocity with a sing...

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conference on computer Vision and pattern recognition (CVPR)

作者： P. Tu P.R.S. Mendonca GE Global Research Center Niskayuna NY USA

This paper proposes a method for three-dimensional reconstruction of surfaces that takes advantage of the symmetry resulting from alternating the positions of a camera and a light source. This set up allows for use of the Helmholtz reciprocity principle to recover the shape of smooth surfaces with arbitrary bidirectional reflectance distribution functions without requiring the presence of texture, as well as for exploiting mutual occlusions between images. Different from previous methods, the technique works with as few as one reciprocal pair, and recovers surface depth and orientation simultaneously by finding the global minimum of an error function via dynamic programming. Since the error is a function not just of depth but of surface orientation as well, the reconstruction is subject to tighter geometric constraints. Given a current estimate of surface geometry and intensity measurements in one image, Helmholtz reciprocity is used to predict the pixel intensity values of the second image. The dynamic program finds the reconstruction that minimizes the total difference between the predicted and measured intensity values. This approach allows for the reconstruction of surfaces displaying specularities and regions of high curvature, which is a challenge commonly encountered in the optical inspection of industrial parts. Results with real data show the quality of the reconstruction obtained with the proposed algorithm.

关键词： Surface reconstruction image reconstruction Cameras Light sources Shape Surface texture Bidirectional control Distribution functions Dynamic programming Geometry

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What is the space of camera response functions?

What is the space of camera response functions?

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conference on computer Vision and pattern recognition (CVPR)

作者： M.D. Grossberg S.K. Nayar Department of Computer Science Columbia University New York NY USA

Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image brightness is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. These constraints determine the theoretical space of all possible camera responses. We have collected a diverse database of real-world camera response functions (DoRF). Using this database we show that real-world responses occupy a small part of the theoretical space of all possible responses. We combine the constraints from our theoretical space with the data from DoRF to create a low-parameter Empirical Model of Response (EMoR). This response model allows us to accurately interpolate the complete response function of a camera from a small number of measurements obtained using a standard chart. We also show that the model can be used to accurately estimate the camera response from images of an arbitrary scene taken using different exposures. The DoRF database and the EMoR model can be downloaded at http://***/CAVE.

关键词： Cameras Layout Brightness Shape measurement Optical imaging Constraint theory image databases computer vision Reflectivity Lighting

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Two Sequential Stages Classifier for Multispectral Data

Two Sequential Stages Classifier for Multispectral Data

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ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Refaat M Mohamed Aly A Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

In this paper, we present an approach for the classification of remote sensing multispectral data, which consists of two sequential stages. The first stage exploits the capabilities of the Support Vector Machines (SVM) approach for density estimation and uses it in a Bayes classification setup. In a typical image, the class of a pixel is highly dependent on the classes of its neighbor pixels. The second stage exploits the dependency of the classes. We incorporate this dependency using stochastic modeling of the context as a Markov Random Field (MRF). The MRF is modeled using Besag model and implemented using the Iterative Conditional Modes (ICM) algorithm. Results show that the stochastic modeling approach enhances the results and provides reasonable smoothness in the classified image.

关键词： Support vector machines Estimation Classification algorithms Pixel Density functionional theory Kernel computer vision

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Deformable object tracking using the boundary element method

Deformable object tracking using the boundary element method

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conference on computer Vision and pattern recognition (CVPR)

作者： M.A. Greminger B.J. Nelson Department of Mechanical Engineering University of Minnesota Minneapolis MN USA Institute of Robotics and Intelligent Systems Swiss Federal Institute of Technology Zurich Switzerland

This paper presents a method to perform 2D (two-dimensional) deformable object tracking using the boundary element method (BEM). BEM, like the finite element method (FEM), is a technique to model an elastic solid. BEM differs from FEM in that only the contour of an object needs to be meshed for BEM, making this method attractive for computer vision problems. For FEM, the interior of the object must be meshed also. In order to track deformable objects, a deformable template is defined that uses BEM to model displacements. The template is registered to the image by applying a force field that deforms the template to match the image. This force field is found using an energy minimization approach. Even though the deformable template uses a linear elastic model, it can be used to track the deformations of objects with nonlinear material properties or in cases where there are large deformations. We demonstrate the performance of this method on objects with linear and nonlinear elastic properties. In addition, it is discussed how this method can be readily extended to 3D (three-dimensional) deformable object tracking.

关键词： Boundary element methods Deformable models Finite element methods computer vision Partitioning algorithms Integral equations Intelligent robots image edge detection Stress Mechanical engineering

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Reliable recovery of piled box-like objects via parabolically deformable superquadrics

Reliable recovery of piled box-like objects via parabolicall...

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International conference on computer Vision (ICCV)

作者： Katsoulas Institute for Pattern Recognition and Image Processing University of Freiburg Germany

Automatic unloading of piled box-like objects is undoubtedly of great importance to the industry. In this contribution a system addressing this problem is described: we employ a laser range finder for data acquisition, and globally deformable superquadrics for object modeling. Our technique is based on a hypothesis generation and refinement scheme. The vertices of the piled objects are extracted and superquadric seeds are aligned at these vertices. The model parameter recovery task is decomposed into two subproblems, each dealing with a subset of the model's parameter set. Both region and boundary based information sources are used for parameter estimation. Compared to a widespread strategy for superquadric recovery, our method shows advantages in terms of robustness and computational efficiency. In addition, our system exhibits versatility with regard to existing industrial systems, since it can effectively deal with both neatly placed and jumbled configurations of objects.

关键词： Parameter estimation Robustness image segmentation Laser modes Deformable models Computational efficiency Service robots Parametric statistics computer vision pattern recognition

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Learning appearance and transparency manifolds of occluded objects in layers

Learning appearance and transparency manifolds of occluded o...

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conference on computer Vision and pattern recognition (CVPR)

作者： B.J. Frey M. Jojic A. Kannan University of Toronto Toronto ONT Canada Redmond WA USA

By mapping a set of input images to points in a low-dimensional manifold or subspace, it is possible to efficiently account for a small number of degrees of freedom. For example, images of a person walking can be mapped to a one-dimensional manifold that measures the phase of the person's gait. However, when the object is moving around the frame and being occluded by other objects, standard manifold modeling techniques (e.g., principal components analysis, factor analysis, locally linear embedding) try to account for global motion and occlusion. We show how factor analysis can be incorporated into a generative model of layered, 2.5-dimensional vision, to jointly locate objects, resolve occlusion ambiguities, and learn models of the appearance manifolds of objects. We demonstrate the algorithm on a video consisting of four occluding objects, two of which are people who are walking, and occlude each other for most of the duration of the video. Whereas standard manifold modeling techniques fail to extract information about the gaits, the layered model successfully extracts a periodic representation of the gait of each person.

关键词： Legged locomotion Videos image motion analysis pattern recognition Educational institutions Principal component analysis Motion analysis Data mining pattern analysis Tracking

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Direct 3D-rotation estimation from spherical images via a generalized shift theorem

Direct 3D-rotation estimation from spherical images via a ge...

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conference on computer Vision and pattern recognition (CVPR)

作者： A. Makadia K. Daniilidis GRASP Laboratory University of Pennsylvania Philadelphia PA USA

Omnidirectional images arising from 3D-motion of a camera contain persistent structures over a large variation of motions because of their large field of view. This persistence made appearance-based methods attractive for robot localization given reference views. Assuming that central omnidirectional images can be mapped to the sphere, the question is what are the underlying mappings of the sphere that can reflect a rotational camera motion. Given such a mapping, we propose a systematic way for finding invariance and the mapping parameters themselves based on the generalization of the Fourier transform. Using results from representation theory, we can generalize the Fourier transform to any homogeneous space with a transitively acting group. Such a case is the sphere with rotation as the acting group. The spherical harmonics of an image pair are related to each other through a shift theorem involving the irreducible representation of the rotation group. We show how to extract Euler angles using this theorem. We study the effect of the number of spherical harmonic coefficients as well as the effect of violation of appearance persistence in real imagery.

关键词： Cameras Fourier transforms Motion estimation Robot vision systems Optical sensors Layout Laboratories Robot localization image motion analysis Spatiotemporal phenomena

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Estimating surface characteristics using physical reflectance models

Estimating surface characteristics using physical reflectanc...

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conference on computer Vision and pattern recognition (CVPR)

作者： H. Ragheb E.R. Hancock Department of Computer Science University of York York UK University of Bu-Ali Sina Hamedan Iran

We make use of the Beckmann-Kirchhoff and Davies scattering models to estimate surface properties for both dielectric and metallic surfaces based on reflectance measurements. In the case of metallic surfaces, we consider two refinements of the Davies theory, which apply under different restrictions concerning the reflectance geometry. The first of these is due to Bennett and Porteus (1961) and applies for normal incidence and reflectance. The second is due to Torrance (1964) and applies when the incidence radiation is off normal. We then suggest three classes of materials for which the appropriate approximations may be used to estimate the surface roughness, the correlation length and the surface slope. Finally, we use the surface slope estimates to fit the Beckmann-Kirchhoff model to reflectance data. In contrast to previous methods which work at long wavelengths and use special purpose instrumentation, our methods can be performed using visible light and a digital camera.

关键词： Reflectivity Rough surfaces Surface roughness Optical surface waves Surface texture Surface fitting computer science Geometry image analysis computer vision

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Automated feature-based range registration of urban scenes of large scale

Automated feature-based range registration of urban scenes o...

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conference on computer Vision and pattern recognition (CVPR)

作者： I. Stamos M. Leordeanu Department of Computer Science Hunter CollegeCity College and Graduate Center City University of New York NY USA Department of Computer Science City University of New York NY NY

We are building a system that can automatically acquire 3D range scans and 2D images to build geometrically and photometrically correct 3D models of urban environments. A major bottleneck in the process is the automated registration of a large number of geometrically complex 3D range scans in a common frame of reference. In this paper we provide a method for the accurate and efficient registration of a large number of complex range scans. The method utilizes range segmentation and feature extraction algorithms. Our algorithm automatically computes pairwise registrations between individual scans, builds a topological graph, and places the scans in the same frame of reference. We present results for building large scale 3D models of historic sites and urban structures.

关键词： Layout Large-scale systems Solid modeling Photometry image reconstruction Laser modes Data mining computer science Educational institutions Feature extraction

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