Scale Space and Variational Methods in Computer Vision
Third International Conference, SSVM 2011, Ein-Gedi, Israel, May 29 – June 2, 2011, Revised Selected Papers
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Alexander M. Bronstein
Algorithm Analysis and Problem Complexity
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Book and Conference Proceedings
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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...
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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 ...
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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 ...
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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...
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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 ...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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Chapter and Conference Paper
Blind Source Separation Using the Block-Coordinate Relative Newton Method
Presented here is a generalization of the modified relative Newton method, recently proposed in [1] for quasi-maximum likelihood blind source separation. Special structure of the Hessian matrix allows to perfo...
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Chapter and Conference Paper
Optimal Sparse Representations for Blind Deconvolution of Images
The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smoo...
