This paper aims to develop new and fast algorithms for recovering a sparse vector from a small number of measurements, which is a fundamental problem in the field of compressive sensing (CS). Currently, CS favors incoherent systems, in which any two measurements are as little correlated as possible. In reality, however, many problems are coherent, and conventional methods such as L1 minimizatio...

The doubling constant of a metric space (X; d) is the smallest value such that every ball in X can be covered by balls of half the radius. The doubling dimension of X is then defined as dim(X) = log2 . A metric (or sequence of metrics) is called doubling precisely when its doubling dimension is bounded. This is a robust class of metric spaces which contains many families of metrics that occur i...

The proposed quad-dominant mesh adaptation algorithm is based on simplicial optimization. It is driven by an anisotropic Riemannian metric and uses specialized local operators formulated in terms of an L∞ instead of the usual L2 distance. Furthermore, the physically-based vertex relocation operator includes an alignment force to explicitly minimize the angular deviation of selected edges from t...

Linear subspace has many important applications in computer vision, such as structure from motion, motion estimation, layer extraction, object recognition, and object tracking. Singular Value Decomposition (SVD) algorithm is a standard technique to compute the subspace from the input data. The SVD algorithm, however, is sensitive to outliers as it uses L2 norm metric, and it can not handle miss...

Cognitive radio (CR) systems make efficient use of the frequency spectrum by opportunistically exploiting unoccupied or under–utilized frequency bands. However, the frequency bands used by CR systems are expected to suffer from various forms of noise and interference with non–Gaussian distributions, such as narrowband and co–channel interference caused by the primary user and other CRs, respect...

In this article we study Sobolev metrics of order one on diffeomorphism groups on the real line. We prove that the space Diff1(R) equipped with the homogenous Sobolev metric of order one is a flat space in the sense of Riemannian geometry, as it is isometric to an open subset of a mapping space equipped with the flat L2-metric. Here Diff1(R) denotes the extension of the group of all either comp...

© 2 0 0 1 m a s s a c h u s e t t s i n s t i t u t e o f t e c h n o l o g y, c a m b r i d g e , m a 0 2 1 3 9 u s a — w w w. a i. m i t. e d u m a s s a c h u s e t t s i n s t i t u t e o f t e c h n o l o g y — a r t i f i c i a l i n t e l l i g e n c e l a b o r a t o r y 1 ABSTRACT The image comparison operation – assessing how well one image matches another – forms a critical component...

Let M be a projective manifold, p : MG −→ M a regular covering over M with a free abelian transformation group G. We describe holomorphic functions on MG of an exponential growth with respect to the distance defined by a metric pulled back from M . As a corollary we obtain for such functions Cartwright and Liouville type theorems. Our approach brings together L2 cohomology technique for holomor...

Significant number of parameterization methods has been proposed to perform good quality of texturing 3D models. However, most methods are hard to be extended for handling the texture mapping with constraints. In this paper, we develop a new algorithm to achieve the matching of the features between the model and texture image. To minimize the distortion artifacts from the matching algorithm, a ...

Latin Hypercube Sampling (LHS) and Jittered Sampling (JS) both achieve better convergence than standard Monte Carlo Sampling (MCS) by using stratification to obtain a more uniform selection of samples, although LHS and JS use different stratification strategies. The “Koksma-Hlawka-like inequality” bounds the error in a computed mean in terms of the sample design’s discrepancy, which is a common...