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  1. No Access

    Chapter and Conference Paper

    Sparse Models for Intrinsic Shape Correspondence

    We present a novel sparse modeling approach to non-rigid shape matching using only the ability to detect repeatable regions. As the input to our algorithm, we are given only two sets of regions in two shapes; ...

    Jonathan Pokrass, Alexander M. Bronstein in Perspectives in Shape Analysis (2016)

  2. No Access

    Chapter and Conference Paper

    Equi-affine Invariant Geometries of Articulated Objects

    We introduce an (equi-)affine invariant geometric structure by which surfaces that go through squeeze and shear transformations can still be properly analyzed. The definition of an affine invariant metric enab...

    Dan Raviv, Alexander M. Bronstein in Outdoor and Large-Scale Real-World Scene A… (2012)

  3. Chapter and Conference Paper

    Stable Spectral Mesh Filtering

    The rapid development of 3D acquisition technology has brought with itself the need to perform standard signal processing operations such as filters on 3D data. It has been shown that the eigenfunctions of the...

    Artiom Kovnatsky, Michael M. Bronstein in Computer Vision – ECCV 2012. Workshops and… (2012)

  4. No Access

    Chapter and Conference Paper

    A Correspondence-Less Approach to Matching of Deformable Shapes

    Finding a match between partially available deformable shapes is a challenging problem with numerous applications. The problem is usually approached by computing local descriptors on a pair of shapes and then ...

    Jonathan Pokrass, Alexander M. Bronstein in Scale Space and Variational Methods in Com… (2012)

  5. No Access

    Chapter and Conference Paper

    Photometric Heat Kernel Signatures

    In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local heat kernel signature shape descriptors. Our construction is based on the ...

    Artiom Kovnatsky, Michael M. Bronstein in Scale Space and Variational Methods in Com… (2012)

  6. No Access

    Chapter and Conference Paper

    Deformable Shape Retrieval by Learning Diffusion Kernels

    In classical signal processing, it is common to analyze and process signals in the frequency domain, by representing the signal in the Fourier basis, and filtering it by applying a transfer function on the Fou...

    Yonathan Aflalo, Alexander M. Bronstein in Scale Space and Variational Methods in Com… (2012)

  7. Chapter and Conference Paper

    Putting the Pieces Together: Regularized Multi-part Shape Matching

    Multi-part shape matching is an important class of problems, arising in many fields such as computational archaeology, biology, geometry processing, computer graphics and vision. In this paper, we address the ...

    Or Litany, Alexander M. Bronstein in Computer Vision – ECCV 2012. Workshops and… (2012)

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    Chapter and Conference Paper

    Shape Palindromes: Analysis of Intrinsic Symmetries in 2D Articulated Shapes

    Analysis of intrinsic symmetries of non-rigid and articulated shapes is an important problem in pattern recognition with numerous applications ranging from medicine to computational aesthetics. Considering art...

    Amit Hooda, Michael M. Bronstein in Scale Space and Variational Methods in Com… (2012)

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    Chapter and Conference Paper

    Discrete Minimum Distortion Correspondence Problems for Non-rigid Shape Matching

    Similarity and correspondence are two fundamental archetype problems in shape analysis, encountered in numerous application in computer vision and pattern recognition. Many methods for shape similarity and cor...

    Chaohui Wang, Michael M. Bronstein in Scale Space and Variational Methods in Com… (2012)

  10. No Access

    Chapter and Conference Paper

    Group-Valued Regularization Framework for Motion Segmentation of Dynamic Non-rigid Shapes

    Understanding of articulated shape motion plays an important role in many applications in the mechanical engineering, movie industry, graphics, and vision communities. In this paper, we study motion-based segm...

    Guy Rosman, Michael M. Bronstein in Scale Space and Variational Methods in Com… (2012)

  11. Chapter and Conference Paper

    Spatially-Sensitive Affine-Invariant Image Descriptors

    Invariant image descriptors play an important role in many computer vision and pattern recognition problems such as image search and retrieval. A dominant paradigm today is that of “bags of features”, a repres...

    Alexander M. Bronstein, Michael M. Bronstein in Computer Vision – ECCV 2010 (2010)

  12. Chapter and Conference Paper

    Regularized Partial Matching of Rigid Shapes

    Matching of rigid shapes is an important problem in numerous applications across the boundary of computer vision, pattern recognition and computer graphics communities. A particularly challenging setting of th...

    Alexander M. Bronstein, Michael M. Bronstein in Computer Vision – ECCV 2008 (2008)

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    Chapter and Conference Paper

    Paretian Similarity for Partial Comparison of Non-rigid Objects

    In this paper, we address the problem of partial comparison of non-rigid objects. We introduce a new class of set-valued distances, related to the concept of Pareto optimality in economics. Such distances allo...

    Alexander M. Bronstein, Michael M. Bronstein in Scale Space and Variational Methods in Com… (2007)

  14. No Access

    Chapter and Conference Paper

    Matching Two-Dimensional Articulated Shapes Using Generalized Multidimensional Scaling

    We present a theoretical and computational framework for matching of two-dimensional articulated shapes. Assuming that articulations can be modeled as near-isometries, we show an axiomatic construction of an a...

    Alexander M. Bronstein, Michael M. Bronstein in Articulated Motion and Deformable Objects (2006)

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    Chapter and Conference Paper

    On Separation of Semitransparent Dynamic Images from Static Background

    Presented here is the problem of recovering a dynamic image superimposed on a static background. Such a problem is ill-posed and may arise e.g. in imaging through semireflective media, in separation of an illu...

    Alexander M. Bronstein, Michael M. Bronstein in Independent Component Analysis and Blind S… (2006)

  16. Chapter and Conference Paper

    Robust Expression-Invariant Face Recognition from Partially Missing Data

    Recent studies on three-dimensional face recognition proposed to model facial expressions as isometries of the facial surface. Based on this model, expression-invariant signatures of the face were constructed ...

    Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel in Computer Vision – ECCV 2006 (2006)

  17. No Access

    Chapter and Conference Paper

    Facetoface: An Isometric Model for Facial Animation

    A geometric framework for finding intrinsic correspondence between animated 3D faces is presented. We model facial expressions as isometries of the facial surface and find the correspondence between two faces ...

    Alexander M. Bronstein, Michael M. Bronstein in Articulated Motion and Deformable Objects (2006)

  18. No Access

    Chapter and Conference Paper

    Isometric Embedding of Facial Surfaces into \(\mathbb{S}^{\rm 3}\)

    The problem of isometry-invariant representation and comparison of surfaces is of cardinal importance in pattern recognition applications dealing with deformable objects. Particularly, in three-dimensional fac...

    Alexander M. Bronstein, Michael M. Bronstein in Scale Space and PDE Methods in Computer Vi… (2005)

  19. No Access

    Chapter and Conference Paper

    QML Blind Deconvolution: Asymptotic Analysis

    Blind deconvolution is considered as a problem of quasi maximum likelihood (QML) estimation of the restoration kernel. Simple closed-form expressions for the asymptotic estimation error are derived. The asympt...

    Alexander M. Bronstein, Michael M. Bronstein in Independent Component Analysis and Blind S… (2004)

  20. No Access

    Chapter and Conference Paper

    Blind Deconvolution Using the Relative Newton Method

    We propose a relative optimization framework for quasi maximum likelihood blind deconvolution and the relative Newton method as its particular instance. Special Hessian structure allows its fast approximate co...

    Alexander M. Bronstein, Michael M. Bronstein in Independent Component Analysis and Blind S… (2004)

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