9 Result(s)
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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; ...
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Reference Work Entry In depth
Manifold Intrinsic Similarity
Nonrigid shapes are ubiquitous in nature and are encountered at all levels of life, from macro to nano. The need to model such shapes and understand their behavior arises in many applications in imaging scienc...
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Chapter
Group-Valued Regularization for Motion Segmentation of Articulated Shapes
Motion-based segmentation is an important tool for the analysis of articulated shapes. As such, it plays an important role in mechanical engineering, computer graphics, and computer vision. In this chapter, we...
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Chapter
Stable Semi-local Features for Non-rigid Shapes
Feature-based analysis is becoming a very popular approach for geometric shape analysis. Following the success of this approach in image analysis, there is a growing interest in finding analogous methods in th...
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Reference Work Entry In depth
Manifold Intrinsic Similarity
Non-rigid shapes are ubiquitous in Nature and are encountered at all levels of life, from macro to nano. The need to model such shapes and understand their behavior arises in many applications in imaging scien...
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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...
