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15 - Statistical models of shape and appearance

from Part V - Image analysis tools

Published online by Cambridge University Press:  05 November 2014

Aly A. Farag
Aly A. Farag
Affiliation:
University of Louisville, Kentucky
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Summary

Introduction

In this chapter, statistical models, derived from the shape and texture of an object in an image or volume, are studied. Statistical shape and appearance models can capture patterns of variability in shape and gray-level appearance. They form the basis of two of the most powerful tools for object analysis – active shape models (ASM) and active appearance models (AAM) – and are very popular in the computer vision and biomedical imaging analysis literature. This chapter reviews the basic foundation of these two statistical models and provides illustrative examples of their effectiveness in object modeling. The chapter builds upon various ideas studied in previous chapters.

Statistical shape models

The shape of an object can be represented by a set of n points, which can be in any dimension (i.e., 2D or 3D). Adopting Kendall’s definition [15.1][15.2], shape is formally defined as:

DEFINITION 15.1 A shape embodies all the geometrical information that remains when location, scale, and rotational effects are filtered out from an object. ■

Type
Chapter
Information
Biomedical Image Analysis
Statistical and Variational Methods
 , pp. 417 - 457
Publisher: Cambridge University Press
Print publication year: 2014

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