4.2. Models and Estimation Problems
In this section, we examine the basic concepts involved in parameter estimation. We describe the different components of a model and show how to find the adequate model for a given computer vision problem. Estimation is analyzed as a generic problem, and the differences between nonrobust and robust methods are emphasized. We also discuss the role of the optimization criterion in solving an estimation problem.
4.2.1. Elements of a model
The goal of data analysis is to provide, for data spanning a very high-dimensional space, an equivalent low-dimensional representation. A set of measurements consisting of n data vectors yi ∊ Rq can be regarded as a point in Rnq. If the data can be described by a model with only ...
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