Results 1  10
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13
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
, 2001
"... In this paper we show that a classic optical ow technique by Nagel and Enkelmann (1986) can be regarded as an early anisotropic diusion method with a diusion tensor. We introduce three improvements into the model formulation that (i) avoid inconsistencies caused by centering the brightness term and ..."
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Cited by 97 (11 self)
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In this paper we show that a classic optical ow technique by Nagel and Enkelmann (1986) can be regarded as an early anisotropic diusion method with a diusion tensor. We introduce three improvements into the model formulation that (i) avoid inconsistencies caused by centering the brightness term and the smoothness term in dierent images, (ii) use a linear scalespace focusing strategy from coarse to ne scales for avoiding convergence to physically irrelevant local minima, and (iii) create an energy functional that is invariant under linear brightness changes. Applying a gradient descent method to the resulting energy functional leads to a system of diusion{reaction equations. We prove that this system has a unique solution under realistic assumptions on the initial data, and we present an ecient linear implicit numerical scheme in detail. Our method creates ow elds with 100 % density over the entire image domain, it is robust under a large range of parameter variations, and it c...
A Theoretical Framework for Convex Regularizers in PDEBased Computation of Image Motion
, 2000
"... Many differential methods for the recovery of the optic flow field from an image sequence can be expressed in terms of a variational problem where the optic flow minimizes some energy. Typically, these energy functionals consist of two terms: a data term, which requires e.g. that a brightness consta ..."
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Cited by 77 (20 self)
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Many differential methods for the recovery of the optic flow field from an image sequence can be expressed in terms of a variational problem where the optic flow minimizes some energy. Typically, these energy functionals consist of two terms: a data term, which requires e.g. that a brightness constancy assumption holds, and a regularizer that encourages global or piecewise smoothness of the flow field. In this paper we present a systematic classification of rotation invariant convex regularizers by exploring their connection to diffusion filters for multichannel images. This taxonomy provides a unifying framework for datadriven and flowdriven, isotropic and anisotropic, as well as spatial and spatiotemporal regularizers. While some of these techniques are classic methods from the literature, others are derived here for the first time. We prove that all these methods are wellposed: they posses a unique solution that depends in a continuous way on the initial data. An interesting structural relation between isotropic and anisotropic flowdriven regularizers is identified, and a design criterion is proposed for constructing anisotropic flowdriven regularizers in a simple and direct way from isotropic ones. Its use is illustrated by several examples.
Dense Disparity Map Estimation Respecting Image Discontinuities: A PDE and ScaleSpace Based Approach
 JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
, 2000
"... We present an energy based approach to estimate a dense disparity map between two images while preserving its discontinuities resulting from image boundaries. We first derive a simplied expression for the disparity that allows us to easily estimate it from a stereo pair of images using an energy min ..."
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Cited by 60 (8 self)
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We present an energy based approach to estimate a dense disparity map between two images while preserving its discontinuities resulting from image boundaries. We first derive a simplied expression for the disparity that allows us to easily estimate it from a stereo pair of images using an energy minimization approach. We assume that the epipolar geometry is known, and we include this information in the energy model. Discontinuities are preserved by means of a regularization term based on the NagelEnkelmann operator. We investigate the associated EulerLagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method. In order to reduce the risk to be trapped within some irrelevant local minima during the iterations, we use a focusing strategy based on a linear scalespace. We prove the existence and uniqueness of the underlying parabolic partial differential equation. Experimental results on bot...
Estimating Motion in Image Sequences  A tutorial on modeling and computation of 2D motion
 IEEE Signal Processing Magazine
, 1999
"... this paper should be helpful to researchers and practitioners working in the fields of video compression and processing, as well as in computer vision. Although the understanding of issues involved in the computation of motion has significantly increased over the last decade, we are still far from g ..."
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Cited by 38 (0 self)
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this paper should be helpful to researchers and practitioners working in the fields of video compression and processing, as well as in computer vision. Although the understanding of issues involved in the computation of motion has significantly increased over the last decade, we are still far from generic, robust, realtime motion estimation algorithms. The selection of the best motion estimator is still highly dependent on the application. Nevertheless, a broad variety of estimation models, criteria and optimization schemes can be treated in a unified framework presented here, thus allowing a direct comparison and leading to a deeper understanding of the properties of the resulting estimators.
Correspondence Estimation in Image Pairs
, 1999
"... This article provides an overview of current techniques for dense geometric correspondence estimation. We will first formally define geometric correspondence and investigate the different types of image pairs. Then we briefly look at the classic approaches to correspondence estimation, at their feas ..."
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Cited by 19 (3 self)
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This article provides an overview of current techniques for dense geometric correspondence estimation. We will first formally define geometric correspondence and investigate the different types of image pairs. Then we briefly look at the classic approaches to correspondence estimation, at their feasibility and flaws for simultaneous dense estimation. We will focus on the Bayesian approach, which is suited very well for this task and for which several promising algorithms have recently been developed. After having a look at the future of the Bayesian approaches, we conclude with a discussion.
Basic Visual Capabilities
, 1993
"... tive Vision and especially Prof. Ruzena Bajcsy, Henrik Christianssen, Prof. Jim Crowley, Prof. Randal Nelson and Prof. Giulio Sandini were most useful in the development of my ideas. The help of Kourosh Pahlavan and Prof. JanOlof Eklundh in gathering image data with the KTHhead is highly appreciat ..."
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Cited by 6 (1 self)
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tive Vision and especially Prof. Ruzena Bajcsy, Henrik Christianssen, Prof. Jim Crowley, Prof. Randal Nelson and Prof. Giulio Sandini were most useful in the development of my ideas. The help of Kourosh Pahlavan and Prof. JanOlof Eklundh in gathering image data with the KTHhead is highly appreciated. Especially I would like to thank my family, Willibald and Dietlinde, Barbara, Elke, Wolfgang and Magdalena for their love and support throughout the years. This work would not have been possible without the generous support of the Osterreichisches Bundesministerium fur Wissenschaft und Forschung, the Osterreichische Bundekammer der Gewerblichen Wirtschaft and the Directorate of Robotics and Machine Intelligence of the National Science Foundation. i Contents 1 Introduction 1 1.1 Classical computer vision : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 The state of the art : : : : : : : : : : : : : : : : : : : : : : : : : : :
Modeling Image Analysis Problems Using Markov Random Fields
, 2000
"... this article are addressed mainly from the computational viewpoint. The primary concerns are how to dene an objective function for the optimal solution for an image analysis problem and how to nd the optimal solution. The reason for dening the solution in an optimization sense is due to various unce ..."
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Cited by 6 (1 self)
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this article are addressed mainly from the computational viewpoint. The primary concerns are how to dene an objective function for the optimal solution for an image analysis problem and how to nd the optimal solution. The reason for dening the solution in an optimization sense is due to various uncertainties in imaging processes. It may be dicult to nd the perfect solution, so we usually look for an optimal one in the sense that an objective, into which constraints are encoded, is optimized
Selective Image Diffusion: Application To Disparity Estimation
 Proc. Int’l Conf. Image Processing
, 1998
"... Inverse problems encountered in image processing and computer vision are often illposed. Whether set in a Bayesian or energybased context, such problems require prior assumptions expressed through an a priori probability or a regularization term, respectively. In some cases, the prior term exhibit ..."
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Cited by 6 (1 self)
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Inverse problems encountered in image processing and computer vision are often illposed. Whether set in a Bayesian or energybased context, such problems require prior assumptions expressed through an a priori probability or a regularization term, respectively. In some cases, the prior term exhibits partial dependence on the observations (e.g., images) that is often ignored to simplify modeling and computations. We briefly review methods that take this dependence into account and we propose a new formulation of the prior term that blends some other simple approaches. Similarly to others, we apply a linear transformation to the prior term but, in addition, we require that the eigenvalues of the transformation have specific properties. These properties are chosen so that diffusion is allowed only along the direction perpendicular to local image gradient. If the gradient magnitude is small, isotropic diffusion is performed. We apply this formulation to stereoscopic disparity estimation a...
Codesign by Parallel Prototyping: OpticalFlow Detection Case Study
 In High Performance Architectures for RealTime Image Processing
, 1998
"... Introduction Traditional development of embedded systems separates hardware and software into independent design streams. Alternatively, codesign involves the interaction between the two streams, possibly once selection of the main hardware engine is complete [1 ] . Unfortunately, if the solutio ..."
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Cited by 3 (3 self)
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Introduction Traditional development of embedded systems separates hardware and software into independent design streams. Alternatively, codesign involves the interaction between the two streams, possibly once selection of the main hardware engine is complete [1 ] . Unfortunately, if the solution involves highperformance multiprocessor architectures codesign requires considerable prior experience of architectural alternatives, topologies and programming environments. In fact, the large number of degrees of freedom in an openended codesign problem is equivalent to an NPcomplete problem [ 2 ] . Recently, system development based on a restriction of choice to pipelined processor farming (PPF) has proved successful for the domain of continuousflow realtime vision systems [ 3 ] . In the systems of interest, data rather than control exigencies frequently dominate. Realtime constraints are soft, conditioned by various throu
On Optimal Control Methods in Computer Vision and Image Processing
, 1994
"... In this paper, we discuss the employment of methods from optimal control for problems in computer vision and image processing. The underlying principle will be that of dynamic programming and the associated HamiltonJacobi equation which allows a uni ed approach to tackle a number of dierent iss ..."
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Cited by 2 (1 self)
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In this paper, we discuss the employment of methods from optimal control for problems in computer vision and image processing. The underlying principle will be that of dynamic programming and the associated HamiltonJacobi equation which allows a uni ed approach to tackle a number of dierent issues in vision. In particular, we will consider problems concerning shape theory, morphology, optical ow, nonlinear scalespaces, and shapefromshading.