Abstract
A method for building flexible shape models is presented in which a shape is
represented by a set of labelled points. The technique determines the statistics
of the points over a collection of example shapes. The mean positions of the
points give an average shape and a number of modes of variation are determined
describing the main ways in which the example shapes tend to deform
from the average. In this way allowed variation in shape can be included in the
model. The method produces a compact flexible 'Point Distribution Model'
with a small number of linearly independent parameters, which can be used
during image search. We demonstrate the application of the Point Distribution
Model in describing two classes of shapes.
represented by a set of labelled points. The technique determines the statistics
of the points over a collection of example shapes. The mean positions of the
points give an average shape and a number of modes of variation are determined
describing the main ways in which the example shapes tend to deform
from the average. In this way allowed variation in shape can be included in the
model. The method produces a compact flexible 'Point Distribution Model'
with a small number of linearly independent parameters, which can be used
during image search. We demonstrate the application of the Point Distribution
Model in describing two classes of shapes.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the British Machine Vision Conference |
| Editors | David Hogg, Roger Boyle |
| Place of Publication | Leeds |
| Publisher | Springer Nature |
| Pages | 9-18 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 1992 |
| Event | British Machine Vision Conference - University of Leeds , Leeds, United Kingdom Duration: 1 Sept 1992 → … |
Conference
| Conference | British Machine Vision Conference |
|---|---|
| Country/Territory | United Kingdom |
| City | Leeds |
| Period | 1/09/92 → … |