The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Kurt Hausler - One of the best experts on this subject based on the ideXlab platform.
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a method for automatic spray painting of unknown parts
International Conference on Robotics and Automation, 2002Co-Authors: A Pichler, Henrik John Andersen, Markus Vincze, Ole Madsen, Kurt HauslerAbstract:Today's industrial automation of spray painting is limited to high part volumes and robot trajectories that are programmed by off-line programming and manual teach-in. This paper presents an approach that uses range image data to obtain the geometry of an unknown part and to automatically generate the robot spray painting trajectories. Laser strip range sensors are installed in front of the paint booth to acquire a range image of the part. Utilizing process knowledge (a Geometric library containing constraints specific for the painting application) Geometric Primitives are detected in the range data. From the Geometric Primitives a normal vector field is generated that enables to extract main faces. The main faces are located in a 3D space and the process knowledge related to each Geometric Primitive is utilized to obtain the trajectory for the paint gun. Results of painting a car mirror and steering column are given.
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ICRA - A method for automatic spray painting of unknown parts
Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002Co-Authors: A Pichler, Henrik John Andersen, Markus Vincze, Ole Madsen, Kurt HauslerAbstract:Today's industrial automation of spray painting is limited to high part volumes and robot trajectories that are programmed by off-line programming and manual teach-in. This paper presents an approach that uses range image data to obtain the geometry of an unknown part and to automatically generate the robot spray painting trajectories. Laser strip range sensors are installed in front of the paint booth to acquire a range image of the part. Utilizing process knowledge (a Geometric library containing constraints specific for the painting application) Geometric Primitives are detected in the range data. From the Geometric Primitives a normal vector field is generated that enables to extract main faces. The main faces are located in a 3D space and the process knowledge related to each Geometric Primitive is utilized to obtain the trajectory for the paint gun. Results of painting a car mirror and steering column are given.
Martin D Levine - One of the best experts on this subject based on the ideXlab platform.
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Geometric Primitive extraction using a genetic algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994Co-Authors: Gerhard Roth, Martin D LevineAbstract:Extracting Geometric Primitives from Geometric sensor data is an important problem in model-based vision. A minimal subset is the smallest number of points necessary to define a unique instance of a Geometric Primitive. A genetic algorithm based on a minimal subset representation is used to perform Primitive extraction. It is shown that the genetic approach is an improvement over random search and is capable of extracting more complex Primitives than the Hough transform. >
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Extracting Geometric Primitives
CVGIP: Image Understanding, 1993Co-Authors: Gerhard Roth, Martin D LevineAbstract:Abstract Extracting Geometric Primitives is an important task in model-based computer vision. The Hough transform is the most common method of extracting Geometric Primitives. Recently, methods derived from the field of robust statistics have been used for this purpose. We show that extracting a single Geometric Primitive is equivalent to finding the optimum value of a cost function which has potentially many local minima. Besides providing a unifying way of understanding different Primitive extraction algorithms, this model also shows that for efficient extraction the true global minimum must be found with as few evaluations of the cost function as possible. In order to extract a single Geometric Primitive we choose a number of minimal subsets randomly from the Geometric data. The cost function is evaluated for each of these, and the Primitive defined by the subset with the best value of the cost function is extracted from the Geometric data. To extract multiple Primitives, this process is repeated on the Geometric data that do not belong to the Primitive. The resulting extraction algorithm can be used with a wide variety of Geometric Primitives and Geometric data. It is easily parallelized, and we describe some possible implementations on a variety of parallel architectures. We make a detailed comparison with the Hough transform and show that it has a number of advantages over this classic technique.
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Geometric Primitive extraction using a genetic algorithm
Computer Vision and Pattern Recognition, 1992Co-Authors: Gerhard Roth, Martin D LevineAbstract:A genetic algorithm based on a minimal subset representation of a Geometric Primitive is used to perform Primitive extraction. A genetic algorithm is an optimization method that uses the metaphor of evolution, and a minimal subset is the smallest number of points necessary to define a unique instance of a Geometric Primitive. The approach is capable of extracting more complex Primitives than the Hough transform. While similar to a hierarchical merging algorithm, it does not suffer from the problem of premature commitment. >
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CVPR - Geometric Primitive extraction using a genetic algorithm
Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1Co-Authors: Gerhard Roth, Martin D LevineAbstract:A genetic algorithm based on a minimal subset representation of a Geometric Primitive is used to perform Primitive extraction. A genetic algorithm is an optimization method that uses the metaphor of evolution, and a minimal subset is the smallest number of points necessary to define a unique instance of a Geometric Primitive. The approach is capable of extracting more complex Primitives than the Hough transform. While similar to a hierarchical merging algorithm, it does not suffer from the problem of premature commitment. >
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ICPR (1) - Segmentation of Geometric signals using robust fitting
[1990] Proceedings. 10th International Conference on Pattern Recognition, 1Co-Authors: Gerhard Roth, Martin D LevineAbstract:The problem of segmenting an image provided by a Geometric sensor into Geometric Primitives is addressed by a two-step iterative process. In the first step, the largest connected region bounded by edge pixels is hypothesized as containing a Geometric Primitive. In the second step, the resulting set of pixels is sent to a robust fitter based on the least median of squares algorithm, to verify whether this hypothesis holds. If the fit is successful, then the Geometric Primitive is removed from the data; otherwise, a different hypothesis is obtained by decreasing the edge threshold. The process is repeated until the entire image is segmented. The fact that the robust fitter is tolerant of outliers means that the correct segmentation is produced from many different initial hypotheses. The algorithm is demonstrated on a number of range images which are known to contain particular Geometric Primitives. >
A Pichler - One of the best experts on this subject based on the ideXlab platform.
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a method for automatic spray painting of unknown parts
International Conference on Robotics and Automation, 2002Co-Authors: A Pichler, Henrik John Andersen, Markus Vincze, Ole Madsen, Kurt HauslerAbstract:Today's industrial automation of spray painting is limited to high part volumes and robot trajectories that are programmed by off-line programming and manual teach-in. This paper presents an approach that uses range image data to obtain the geometry of an unknown part and to automatically generate the robot spray painting trajectories. Laser strip range sensors are installed in front of the paint booth to acquire a range image of the part. Utilizing process knowledge (a Geometric library containing constraints specific for the painting application) Geometric Primitives are detected in the range data. From the Geometric Primitives a normal vector field is generated that enables to extract main faces. The main faces are located in a 3D space and the process knowledge related to each Geometric Primitive is utilized to obtain the trajectory for the paint gun. Results of painting a car mirror and steering column are given.
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ICRA - A method for automatic spray painting of unknown parts
Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002Co-Authors: A Pichler, Henrik John Andersen, Markus Vincze, Ole Madsen, Kurt HauslerAbstract:Today's industrial automation of spray painting is limited to high part volumes and robot trajectories that are programmed by off-line programming and manual teach-in. This paper presents an approach that uses range image data to obtain the geometry of an unknown part and to automatically generate the robot spray painting trajectories. Laser strip range sensors are installed in front of the paint booth to acquire a range image of the part. Utilizing process knowledge (a Geometric library containing constraints specific for the painting application) Geometric Primitives are detected in the range data. From the Geometric Primitives a normal vector field is generated that enables to extract main faces. The main faces are located in a 3D space and the process knowledge related to each Geometric Primitive is utilized to obtain the trajectory for the paint gun. Results of painting a car mirror and steering column are given.
Gerhard Roth - One of the best experts on this subject based on the ideXlab platform.
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Geometric Primitive extraction using a genetic algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994Co-Authors: Gerhard Roth, Martin D LevineAbstract:Extracting Geometric Primitives from Geometric sensor data is an important problem in model-based vision. A minimal subset is the smallest number of points necessary to define a unique instance of a Geometric Primitive. A genetic algorithm based on a minimal subset representation is used to perform Primitive extraction. It is shown that the genetic approach is an improvement over random search and is capable of extracting more complex Primitives than the Hough transform. >
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Extracting Geometric Primitives
CVGIP: Image Understanding, 1993Co-Authors: Gerhard Roth, Martin D LevineAbstract:Abstract Extracting Geometric Primitives is an important task in model-based computer vision. The Hough transform is the most common method of extracting Geometric Primitives. Recently, methods derived from the field of robust statistics have been used for this purpose. We show that extracting a single Geometric Primitive is equivalent to finding the optimum value of a cost function which has potentially many local minima. Besides providing a unifying way of understanding different Primitive extraction algorithms, this model also shows that for efficient extraction the true global minimum must be found with as few evaluations of the cost function as possible. In order to extract a single Geometric Primitive we choose a number of minimal subsets randomly from the Geometric data. The cost function is evaluated for each of these, and the Primitive defined by the subset with the best value of the cost function is extracted from the Geometric data. To extract multiple Primitives, this process is repeated on the Geometric data that do not belong to the Primitive. The resulting extraction algorithm can be used with a wide variety of Geometric Primitives and Geometric data. It is easily parallelized, and we describe some possible implementations on a variety of parallel architectures. We make a detailed comparison with the Hough transform and show that it has a number of advantages over this classic technique.
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Geometric Primitive extraction using a genetic algorithm
Computer Vision and Pattern Recognition, 1992Co-Authors: Gerhard Roth, Martin D LevineAbstract:A genetic algorithm based on a minimal subset representation of a Geometric Primitive is used to perform Primitive extraction. A genetic algorithm is an optimization method that uses the metaphor of evolution, and a minimal subset is the smallest number of points necessary to define a unique instance of a Geometric Primitive. The approach is capable of extracting more complex Primitives than the Hough transform. While similar to a hierarchical merging algorithm, it does not suffer from the problem of premature commitment. >
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CVPR - Geometric Primitive extraction using a genetic algorithm
Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1Co-Authors: Gerhard Roth, Martin D LevineAbstract:A genetic algorithm based on a minimal subset representation of a Geometric Primitive is used to perform Primitive extraction. A genetic algorithm is an optimization method that uses the metaphor of evolution, and a minimal subset is the smallest number of points necessary to define a unique instance of a Geometric Primitive. The approach is capable of extracting more complex Primitives than the Hough transform. While similar to a hierarchical merging algorithm, it does not suffer from the problem of premature commitment. >
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ICPR (1) - Segmentation of Geometric signals using robust fitting
[1990] Proceedings. 10th International Conference on Pattern Recognition, 1Co-Authors: Gerhard Roth, Martin D LevineAbstract:The problem of segmenting an image provided by a Geometric sensor into Geometric Primitives is addressed by a two-step iterative process. In the first step, the largest connected region bounded by edge pixels is hypothesized as containing a Geometric Primitive. In the second step, the resulting set of pixels is sent to a robust fitter based on the least median of squares algorithm, to verify whether this hypothesis holds. If the fit is successful, then the Geometric Primitive is removed from the data; otherwise, a different hypothesis is obtained by decreasing the edge threshold. The process is repeated until the entire image is segmented. The fact that the robust fitter is tolerant of outliers means that the correct segmentation is produced from many different initial hypotheses. The algorithm is demonstrated on a number of range images which are known to contain particular Geometric Primitives. >
Mamano Nil - One of the best experts on this subject based on the ideXlab platform.
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Stable-matching Voronoi diagrams: Combinatorial complexity and algorithms
'Carleton University', 2020Co-Authors: Barequet Gill, Eppstein David, Goodrich Michael, Mamano NilAbstract:We study algorithms and combinatorial complexity bounds for stable-matching Voronoi diagrams, where a set, $S$, of $n$ point sites in the plane determines a stable matching between the points in $\mathbb{R}^2$ and the sites in $S$ such that (i) the points prefer sites closer to them and sites prefer points closer to them, and (ii) each site has a quota or ``appetite'' indicating the area of the set of points that can be matched to it. Thus, a stable-matching Voronoi diagram is a solution to the well-known post office problem with the added (realistic) constraint that each post office has a limit on the size of its jurisdiction. Previous work on the stable-matching Voronoi diagram did not analyze its combinatorial or algorithmic complexity, instead only providing existence and uniqueness proofs. In this paper, we show that a stable-matching Voronoi diagram of $n$ point sites has $O(n^{2+\varepsilon})$ faces and edges, for any $\varepsilon>0$, and show that this bound is almost tight by giving a family of diagrams with $\Theta(n^2)$ faces and edges. We also provide a discrete algorithm for constructing it in $O(n^3+n^2f(n))$ time in the real-RAM model of computation, where $f(n)$ is the runtime of a Geometric Primitive (which we identify) that can be performed in the real-RAM model or can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation. We show, however, how to compute the Geometric Primitive exactly for polygonal convex distance functions
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Stable-Matching Voronoi Diagrams: Combinatorial Complexity and Algorithms
2020Co-Authors: Barequet Gill, Eppstein David, Goodrich, Michael T., Mamano NilAbstract:We study algorithms and combinatorial complexity bounds for \emph{stable-matching Voronoi diagrams}, where a set, $S$, of $n$ point sites in the plane determines a stable matching between the points in $\mathbb{R}^2$ and the sites in $S$ such that (i) the points prefer sites closer to them and sites prefer points closer to them, and (ii) each site has a quota or "appetite" indicating the area of the set of points that can be matched to it. Thus, a stable-matching Voronoi diagram is a solution to the well-known post office problem with the added (realistic) constraint that each post office has a limit on the size of its jurisdiction. Previous work on the stable-matching Voronoi diagram provided existence and uniqueness proofs, but did not analyze its combinatorial or algorithmic complexity. In this paper, we show that a stable-matching Voronoi diagram of $n$ point sites has $O(n^{2+\varepsilon})$ faces and edges, for any $\varepsilon>0$, and show that this bound is almost tight by giving a family of diagrams with $\Theta(n^2)$ faces and edges. We also provide a discrete algorithm for constructing it in $O(n^3\log n+n^2f(n))$ time in the real-RAM model of computation, where $f(n)$ is the runtime of a Geometric Primitive (which we define) that can be approximated numerically, but cannot, in general, be performed exactly in an algebraic model of computation. We show, however, how to compute the Geometric Primitive exactly for polygonal convex distance functions.Comment: 34 pages, 21 figures, This is a full version of an extended abstract presented in ICALP'18. To appear in JoCG. v2: upgraded version for JoC