## Search

Now showing items 1-10 of 27

#### Recognition and matching in the presence of deformation and lighting change

(2008-11-21)

Natural images of objects and scenes show a fascinating amount of
variability due to different factors like lighting and viewpoint change,
occlusion, articulation and non-rigid deformation. There are certain cases
like recognition of specular objects and images with arbitrary deformations
where existing techniques do not perform well. For image deformation, we
propose a method for faster keypoint matching with histogram descriptors and a
completely deformation invariant representation. We also propose a method for
improving specular object recognition.
Histograms are a powerful statistical representation for keypoint matching
and content based image retrieval. The <EM>earth mover's distance</EM> (EMD) is an
important perceptually meaningful metric for comparing histograms, but it
suffers from high (O(n<SUP>3</SUP> log n)) computational complexity. We
propose a novel

**linear time**algorithm for approximating EMD with the weighted L<SUB>1</SUB> norm of the wavelet transform of the difference histogram. We prove that the resulting wavelet EMD metric is equivalent to EMD. We experimentally show that wavelet EMD is a good approximation to EMD, has similar performance, but requires much less computation. We also give a fast algorithm for the best partial EMD match between two histograms. Images of non-planar object can undergo a large non-linear deformation due to a viewpoint change. Complex deformations occur in images of non-rigid objects, for example, in medical image sequences. We propose using the <EM>contour tree</EM> as a novel framework invariant to arbitrary deformations for representing and comparing images. It represents all the deformation invariant information in an image. Lighting changes greatly affect the appearance of <EM>specular</EM> objects and make recognition difficult much more than for Lambertian objects. In model based recognition of specular objects, an important constraint is that the estimated lighting should be non-negative everywhere. We propose a new method to enforce this constraint and explore its usefulness in specular object recognition, using the spherical harmonic representation of lighting. The new method is faster as well as more accurate than previous methods. Experiments on both synthetic and real data indicate that the constraint can improve recognition of specular objects by better separating the correct and incorrect models....#### A Variational Shape Optimization Framework for Image Segmentation

(2006-11-15)

Image segmentation is one of the fundamental problems in image processing. The goal is to partition a given image into regions that are uniform with respect to some image features and possibly to extract the region boundaries. ...

#### Pricing Volatility Derivatives Using Space Scaled Levy Processes

(2008-09-02)

The VIX index measures the one-month risk-neutral forward volatility of the S&P500 (SPX) index. While Lévy processes such as the CGMY process can price options on the underlying stock or index, they implicitly assume a ...

#### Fast Solvers for Models of Fluid Flow with Spectral Elements

(2008-09-02)

We introduce a preconditioning technique based on Domain Decomposition and the Fast Diagonalization Method that can be applied to tensor product based discretizations of the steady convection-diffusion and the linearized ...

#### Algorithms for Solving Linear and Polynomial Systems of Equations over Finite Fields with Applications to Cryptanalysis

(2007-06-07)

This dissertation contains algorithms for solving linear and polynomial systems
of equations over GF(2). The objective is to provide fast and exact tools for algebraic
cryptanalysis and other applications. Accordingly, it ...

#### Homotopy Optimization Methods and Protein Structure Prediction

(2005-07-22)

The focus of this dissertation is a new method for solving unconstrained minimization problems---<i>homotopy optimization using perturbations and
ensembles</i> (HOPE). HOPE is a homotopy optimization method ...

#### Dimensionality reduction for hyperspectral data

(2008-05-09)

This thesis is about dimensionality reduction for hyperspectral data. Special emphasis is given to dimensionality reduction techniques known as kernel eigenmap methods and manifold learning algorithms. Kernel eigenmap ...

#### Entropy Stable Approximations of Nonlinear Conservation Laws and Related Fluid Equations

(2007-08-01)

We present a systematic study of novel entropy stable approximations for a variety of nonlinear conservation laws, from the scalar Burgers equation to one dimensional Navier-Stokes and two
dimensional shallow water ...

#### Definable families of finite Vapnik Chervonenkis dimension

(2008-04-25)

Vapnik Chervonenkis dimension is a basic combinatorial notion with applications in machine
learning, stability theory, and statistics. We explore what effect model
theoretic structure has on the VC dimension of formulas, ...

#### Network State Estimation Via Passive Traffic Monitoring

(2005-04-20)

We propose to study computer network traffic as a dynamical system, with the intent of determining how predictable the traffic is over short time scales. We will use passive measurements from high capacity links, so that ...