Hamiltonian Operator

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Ron Kimmel - One of the best experts on this subject based on the ideXlab platform.

  • Hamiltonian Operator for Spectral Shape Analysis
    IEEE transactions on visualization and computer graphics, 2018
    Co-Authors: Yoni Choukroun, Alon Shtern, Alexander M. Bronstein, Ron Kimmel
    Abstract:

    Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami Operator. In this paper, we propose to adapt the classical Hamiltonian Operator from quantum mechanics to the field of shape analysis. To this end, we study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. We present general optimization approaches for solving variational problems involving the basis defined by the Hamiltonian using perturbation theory for its eigenvectors. The suggested Operator is shown to produce better functional spaces to operate with, as demonstrated on different shape analysis tasks.

  • Sparse Approximation of 3D Meshes Using the Spectral Geometry of the Hamiltonian Operator
    Journal of Mathematical Imaging and Vision, 2018
    Co-Authors: Yoni Choukroun, Gautam Pai, Ron Kimmel
    Abstract:

    The discrete Laplace Operator is ubiquitous in spectral shape analysis, since its eigenfunctions are provably optimal in representing smooth functions defined on the surface of the shape. Indeed, subspaces defined by its eigenfunctions have been utilized for shape compression, treating the coordinates as smooth functions defined on the given surface. However, surfaces of shapes in nature often contain geometric structures for which the general smoothness assumption may fail to hold. At the other end, some explicit mesh compression algorithms utilize the order by which vertices that represent the surface are traversed, a property which has been ignored in spectral approaches. Here, we incorporate the order of vertices into an Operator that defines a novel spectral domain. We propose a method for representing 3D meshes using the spectral geometry of the Hamiltonian Operator, integrated within a sparse approximation framework. We adapt the concept of a potential function from quantum physics and incorporate vertex ordering information into the potential, yielding a novel data-dependent Operator. The potential function modifies the spectral geometry of the Laplacian to focus on regions with finer details of the given surface. By sparsely encoding the geometry of the shape using the proposed data-dependent basis, we improve compression performance compared to previous results that use the standard Laplacian basis and spectral graph wavelets.

  • Hamiltonian Operator for spectral shape analysis
    arXiv: Graphics, 2016
    Co-Authors: Yoni Choukroun, Alon Shtern, Alexander M. Bronstein, Ron Kimmel
    Abstract:

    Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami Operator. In this paper, we propose to adapt the classical Hamiltonian Operator from quantum mechanics to the field of shape analysis. To this end we study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. We present a general optimization approach for solving variational problems involving the basis defined by the Hamiltonian using perturbation theory for its eigenvectors. The suggested Operator is shown to produce better functional spaces to operate with, as demonstrated on different shape analysis tasks.

Alatancang - One of the best experts on this subject based on the ideXlab platform.

Alatancang Chen - One of the best experts on this subject based on the ideXlab platform.

Yoni Choukroun - One of the best experts on this subject based on the ideXlab platform.

  • Hamiltonian Operator for Spectral Shape Analysis
    IEEE transactions on visualization and computer graphics, 2018
    Co-Authors: Yoni Choukroun, Alon Shtern, Alexander M. Bronstein, Ron Kimmel
    Abstract:

    Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami Operator. In this paper, we propose to adapt the classical Hamiltonian Operator from quantum mechanics to the field of shape analysis. To this end, we study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. We present general optimization approaches for solving variational problems involving the basis defined by the Hamiltonian using perturbation theory for its eigenvectors. The suggested Operator is shown to produce better functional spaces to operate with, as demonstrated on different shape analysis tasks.

  • Sparse Approximation of 3D Meshes Using the Spectral Geometry of the Hamiltonian Operator
    Journal of Mathematical Imaging and Vision, 2018
    Co-Authors: Yoni Choukroun, Gautam Pai, Ron Kimmel
    Abstract:

    The discrete Laplace Operator is ubiquitous in spectral shape analysis, since its eigenfunctions are provably optimal in representing smooth functions defined on the surface of the shape. Indeed, subspaces defined by its eigenfunctions have been utilized for shape compression, treating the coordinates as smooth functions defined on the given surface. However, surfaces of shapes in nature often contain geometric structures for which the general smoothness assumption may fail to hold. At the other end, some explicit mesh compression algorithms utilize the order by which vertices that represent the surface are traversed, a property which has been ignored in spectral approaches. Here, we incorporate the order of vertices into an Operator that defines a novel spectral domain. We propose a method for representing 3D meshes using the spectral geometry of the Hamiltonian Operator, integrated within a sparse approximation framework. We adapt the concept of a potential function from quantum physics and incorporate vertex ordering information into the potential, yielding a novel data-dependent Operator. The potential function modifies the spectral geometry of the Laplacian to focus on regions with finer details of the given surface. By sparsely encoding the geometry of the shape using the proposed data-dependent basis, we improve compression performance compared to previous results that use the standard Laplacian basis and spectral graph wavelets.

  • Hamiltonian Operator for spectral shape analysis
    arXiv: Graphics, 2016
    Co-Authors: Yoni Choukroun, Alon Shtern, Alexander M. Bronstein, Ron Kimmel
    Abstract:

    Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami Operator. In this paper, we propose to adapt the classical Hamiltonian Operator from quantum mechanics to the field of shape analysis. To this end we study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. We present a general optimization approach for solving variational problems involving the basis defined by the Hamiltonian using perturbation theory for its eigenvectors. The suggested Operator is shown to produce better functional spaces to operate with, as demonstrated on different shape analysis tasks.

M. Gregoratti - One of the best experts on this subject based on the ideXlab platform.