This chapter introduces the reader to the area of parametric image registration, from a beginner’s point of view. Given a model, an input image and a reference image, the parametric registration task is to find a set of parameters (of the model) that transform the input image into the reference image. This chapter reviews models of the general projective, affine, similarity and Euclidean transf...

The parametric eigenspace method is an object recognition method based on visual learning approach with image coding technique. In this paper, to improve matching efficiency, a novel approach to construct hierarchical dictionary for the parametric eigenspace method is proposed. In the proposed constructing method, learning image set is classified hierarchically, and tree-structured dictionary i...

In many applications of fuzzy set theory, the membership of an object is not defined directly. One of its attributes (like height, age, weight, . . . ) is first mapped on a real number (often by means of a physical instrument) and a parametric function then maps this real number on a membership degree in some fuzzy set (like ‘tall’, ‘old’, ‘heavy’, . . . ). A very common parametric function is ...

This work presents a methodology to derive explicit control laws for hybrid linear systems. The proposed method employs multi-parametric and dynamic programming techniques to dissemble the original MPC formulation into a set of smaller problems, which can be efficiently solved using suitable multi-parametric mixed integer programming algorithms. The solution comprises the envelope of explicit p...

  Data envelopment analysis (DEA) is a non-parametric method for assessing relative efficiency of decision-making units (DMUs). Every single decision-maker with the use of inputs produces outputs. These decision-making units will be defined by the production possibility set. Resource allocation to DMUs is one of the concerns of managers since managers can employ the results of this process to a...

Earthquake time history records are required to perform dynamic nonlinear analyses. In order to provide a suitable set of such records, they are scaled to match a target spectrum as introduced in the well-known design codes. Corresponding scaling factors are taken similar in practice however, optimizing them reduces extra-ordinary economic charge for the seismic design. In the present work a ne...

OBJECTIVE We demonstrate the utility of parametric survival analysis. The analysis of longevity as a function of risk factors such as body mass index (BMI; kg/m(2) ), activity levels, and dietary factors is a mainstay of obesity research. Modeling survival through hazard functions, relative risks, or odds of dying with methods such as Cox proportional hazards or logistic regression are the most...

In the context of testing the speci cation of a nonlinear parametric regression function, we study the power of speci cation tests using the minimax approach. We determine the maximum rate at which a set of smooth local alternatives can approach the parametric model while ensuring consistency of a test uniformly against any alternative in this set. We show that a smooth nonparametric testing pr...

A robust fitting and reconstruction algorithm has to cope with two major problems. First of all it has to be able to deal with noisy input data and outliers. Furthermore it should be capable of handling multiple data set mixtures. The decreasing exponential approach is robust towards outliers and multiple data set mixtures. It is able to fit a parametric model to a given point cloud. As paramet...

We propose a methodology of speech segmentation in which the LSF feature vector matrix of a segment is reconstructed optimally using a set of parametric/non-parametric functions. We have explored approximations using basis functions or polynomials. We have analyzed the performance of these methods w.r.t. phoneme segmentation (on 100 TIMIT sentences) and reconstruction error based on spectral di...