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An Experimental Comparison of MinCut/MaxFlow Algorithms for Energy Minimization in Vision
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2001
"... After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time compl ..."
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Cited by 870 (47 self)
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After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in lowlevel vision. The combinatorial optimization literature provides many mincut/maxflow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper
Fast Imagebased Object Localization in Natural Scenes
 In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2002
, 2002
"... In many robot applications, autonomous robots must be capable of localizing the objects they are to manipulate. In this paper we address the object localization problem by fitting a parametric curve model to the object contour in the image. The initial prior of the object pose is iteratively refined ..."
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Cited by 12 (2 self)
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In many robot applications, autonomous robots must be capable of localizing the objects they are to manipulate. In this paper we address the object localization problem by fitting a parametric curve model to the object contour in the image. The initial prior of the object pose is iteratively refined to the posterior distribution by optimizing the separation of the object and the background. The local separation criteria are based on local statistics which are iteratively computed from the object and the background region. No prior knowledge on color distributions is needed. Experiments show that the method is capable of localizing objects in a cluttered and textured scene even under strong variations of illumination. The method is able to localize a soccer ball within frame rate.
Graph Based Algorithms for Scene Reconstruction from Two or More Views
, 2004
"... In recent years, graph cuts have emerged as a powerful optimization technique for minimizing energy functions that arise in lowlevel vision problems. Graph cuts avoid the problems of local minima inherent in other approaches (such as gradient descent). The goal of this thesis is to apply graph cuts ..."
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Cited by 7 (1 self)
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In recent years, graph cuts have emerged as a powerful optimization technique for minimizing energy functions that arise in lowlevel vision problems. Graph cuts avoid the problems of local minima inherent in other approaches (such as gradient descent). The goal of this thesis is to apply graph cuts to a classical computer vision problem — scene reconstruction from multiple views, i.e. computing the 3dimensional shape of the scene. This thesis provides a technical result which greatly facilitates the derivation of the scene reconstruction algorithm. Our result should also be useful for developing other energy minimization algorithms based on graph cuts. Previously such algorithms explicitly constructed graphs where a minimum cut also minimizes the appropriate energy. It is natural to ask for what energy functions we can construct such a graph. We answer this question for the class of functions of binary variables that can be written as a sum of terms containing three or fewer variables. We give a simple criterion for functions in this class which is necessary and sufficient, as well as a necessary condition for any function of binary variables. We also give a
The Contracting Curve Density Algorithm and its Application to Modelbased Image Segmentation
 In Proc. Conf. Computer Vision and Pattern Recognition
, 2001
"... In this paper we address the problem of modelbased image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters an initial probability distribution of the model edge in the image is obtained. From the vicinity of the surmised edge loca ..."
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Cited by 7 (4 self)
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In this paper we address the problem of modelbased image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters an initial probability distribution of the model edge in the image is obtained. From the vicinity of the surmised edge local statistics are learned for both sides of the edge. These local statistics provide locally adapted criteria to distinguish the two sides of the edge even in the presence of spatially changing properties such as texture, shading, or color. Based on the local statistics the model parameters are iteratively refined using a MAP estimation. Experiments with RGB images show that the method is capable of achieving high subpixel accuracy and robustness even in the presence of texture, shading, clutter, and partial occlusion. 1.
Segmentation of dynamic ND data sets via graph cuts using markov models
 In Proc. Medical Image Computing and ComputerAssisted Intervention
, 2001
"... Abstract. This paper describes a new segmentation technique for multidimensional dynamic data. One example of such data is a perfusion sequence where a number of 3D MRI volumes shows the dynamics of a contrast agent inside the kidney or heart at enddiastole. We assume that the volumes are registere ..."
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Cited by 4 (0 self)
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Abstract. This paper describes a new segmentation technique for multidimensional dynamic data. One example of such data is a perfusion sequence where a number of 3D MRI volumes shows the dynamics of a contrast agent inside the kidney or heart at enddiastole. We assume that the volumes are registered. If not, we register consecutive volumes via mutual information maximization. The sequence of n registered volumes is regarded as a single volume where each voxel holds an ndimensional vector of intensities, or intensity curve. Our approach is to segment this volume directly based on voxels intensity curves using a generalization of the graph cut techniques in [7, 2]. These techniques use a spatial Markov model to describe correlations between voxels. Our contribution is in introducing a temporal Markov model to describe the desired dynamic properties of segments. Graph cuts obtain a globally optimal segmentation with the best balance between boundary and regional properties among all segmentations satisfying user placed hard constraints. Flexibility, coherent theoretical formulation, and the possibility of a globally optimal solution are attractive features of our method that gracefully handles even low quality data. We demonstrate results for 3D kidney and 2D heart perfusion sequences. 1