Next: Linear feature modeling:
Up: Isotropic mapping
Previous: Feature extraction
Euclidean isotropy of
Let
represent the collection of objects of interest
present in an image, where each object
, is a collection of
,
where
is a coordinate pair,
is the unit circle,
represents the
orientation of
.
At the lowest level of vision
's, are represented by points on
an edge segment
(where
is obtained by using Burns' edge
detector [15]).
At the next level of perceptual grouping, certain
will be
combined to generate a higher-level structure. Such a structure obtained from
the grouping of
's may be called
for consistency of
notation, although it should be understood that
now represents a
structure at a higher-level than
. (Refer to figure 2.)
We have defined a mapping
, (where
is the
dimensionality of the feature space), to be isotropic if it is invariant
to the action of the Euclidean group:
 |
(2) |
where
is the Euclidean group
- the semi-direct product of the
group of linear isometries and the translation group - such that:
 |
(3) |
The extraction of the feature vector,
, is represented by
.
The action of the Euclidean group on
transforms each
and is given as (refer to figure 3):
 |
(4) |
where
, (
), represents a member of the
translation group of
,
, such that
,
is a rotation by an angle
,
is a reflection
along an axis in
, and
is the reflection
along the
-axis,
.
The action
(by using the identity
), because reflection along an arbitrary axis is equivalent to rotation of
by an angle
to align the axis of reflection with the original
-axis, followed by a reflection in the
-axis, and then rotation by
an angle
.
Figure 2:
,
,
and
combined to form
.
 |
Figure 3:
Action of
on an edge segment,
.
and
represent the end-points of
.
 |
Subsections
Next: Linear feature modeling:
Up: Isotropic mapping
Previous: Feature extraction
Qasim Iqbal
2001-05-06