Problems requiring a best guess for the value of some parameters based on noisy input data are estimation problems. Alternatively, you might view estimation problems as problems where you try to characterise a dataset using a mathematical construct such as a point or a line. This chapter is intended as a self-contained introduction to the basic concepts in the field of linear estimation. Assuch, it will give you an understanding of the analysis in the following chapters where we focus specifically on motion estimation. The discussion here is quite informal: our aim is to introduce the broad ideas of estimation-more detail is available from avast range of sources [64,122,148,201,239,267,303]. This chapter begins with a brief introduction to probability theory and some key terms before moving onto the basics of estimation, which then allows us to spring into the field of robust estimation. It is assumed you are familiar with basic calculus.

Inspec keywords: motion estimation; probability; calculus; estimation theory

Other keywords: motion estimation; probability theory; robust estimation; linear estimation; calculus; mathematical preliminary

Subjects: Optical, image and video signal processing