Introduction

Abstract

The vast majority of computer vision algorithms use some form of optimization, as they intend to find some solution which is “best” according to some criterion. Consequently, the field of optimization is worth studying for everyone being seriously interested in computer vision. In this chapter, some expressions being of widespread use in literature dealing with optimization are clarified first. Furthermore, a classification framework is presented, which intends to categorize optimization methods into the four categories continuous, discrete, combinatorial, and variational, according to the nature of the set from which they select their solution. This categorization helps to obtain an overview of the topic and serves as a basis for the structure of the remaining chapters at the same time. Additionally, some concepts being quite common in optimization and therefore being used in diverse applications are presented. Especially to mention are so-called energy functionals measuring the quality of a particular solution by calculating a quantity called “energy”, graphs, and last but not least Markov Random Fields.