100
1) Areas e.g fields, lakes.
These are homogeneous features extending over
several or more pixels.
2) Boundaries e.g field boundaries.
These consist of low-level edge elements
where these are defined to be local
discontinuities in image features.
(Pratt (1978)) .
3) Thin lines e.g rivers, forest rides,
roads, hedges.
A line segment is defined by the u-shaped
cross section of image features.
These three types reflect the different
approaches to image segmentation. The so
called region growing strategy starts by
growing an object in an area until a
significant change in the measured features
is noted. Another segmentation strategy aims
to locate object boundaries thereby isolating
the objects.
The overall feature extraction process may
consist of one or more of the following
classes of operations.
1) Preprocessing: This is primarily to remove
the effect of image noise on the following
stages.
2) Feature Extraction: The actual process of
transforming the image to a specific feature
domain.
3) Post Processing: The process by which
detected features are cleaned i.e filtered of
invalid or unwanted results.
Knowledge of the features to be extracted
can be used at any of the three stages. For
example knowledge of the noise statistics
(embodied into a noise model) may be used to
optimally reduce noise in stage 1. In stage 2
a knowledge of the feature type may be used
to reduce the computational cost. World
knowledge can also be used in stage 3 to test
the validity of detected features.
2 EDGE AND LINE FEATURE EXTRACTION
Extensive surveys of the following classes of
techniques can be found in Ballard(1982),
Pratt(1978), Davis(1975), Carlotto(1984) ,
Duda & Hart(1972b).
2.1 Local Operators
Local operators use data from the
neighbourhood of the edge candidate pixel.
There are a wide variety of such local
operators details of which can be found in
Duda & Hart(1972b) and Pratt (1978). These
simple operators often form the basis of
commercial edge detection systems because of
their computational simplicity and their
potential parallel implementation. More
sophisticated statistical operators have
attempted to increase the detection
performance in the presence of noise however
Geiss(1984) found the Marr and
Hildreth (1980) operator unsuccessful at
locating edges in Synthetic Aperture Radar
images and although the technique developed
by Suk and Hong(1984) proved superior to
simple operators it still, performed
unacceptably in radar data.
2.2 Image Modelling
The following set of techniques all attempt
to impart some level of knowledge into the
feature extraction process.
2.2.1 Parametric Modelling
The basis of parametric modelling is to fit
some parametric surface representing an ideal
edge model to a local region of pixels from
which the edge characteristics can then be
derived. Again many researchers have proposed
operators in this category which can be found
in Rosenfeld and Kak(1982), Haralick (1980)
and Chittineni(1983) .
transform
is the F
high pas
produces
produced
transform
Unfortuns
processe:
structure
2.2.2 Statistical Modelling
2.4 Graph
Statistical modelling forms the basis of a
variety of methods which rely on statistical
theory for edge detection and involves the
combination of a noise and image model
allowing calculation of probabilities of edge
existence. Then statistical methods such as
maximum likelihood estimation can be used to
locate probable edges. Chen and
Pavlidis(1980). Rosenfeld(1981).
2.2.3 Template Matching
In this method explicit knowledge of the
desired feature shape is used to produce a
template which is then matched to regions of
the image. The feature will probably exist
where the correlation between template and
image data is greater than a set threshold.
The method is successful for detecting very
specific objects such as tanks and aircraft
in military images where the template may be
a section of an image containing the desired
object.
Simple template operators such as the
Kirsch operator in Pratt(1978) attempt to
generalise the process by detecting line and
edge elements with variously orientated
templates.
Another method related to template matching
by Stockman and Agrawala (1977) is the Hough
Transform technique (Hough (1962)) later
improved by Duda & Hart(1972a). For grey
level images the transform produces peaks and
troughs in Hough Space corresponding to light
and dark straight lines in image space. These
peaks or troughs can then be detected and the
line parameters extracted, using traditional
operators. In general the transform can be
used to extract any feature shape which has a
known parametric form such as the conic
sections (Sloan & Ballard(1980)) and has been
widely used because of its effectiveness in
both clean and noisy images (see
Shapiro(1978)). For radar images the
technique performs successfully at detecting
straight lines that are long relative to the
scene size (Skingley & Rye(1985)). More
recently a modified hough transform, the MUFF
transform (Wallace(1985)) has been developed
which uses a different parametric form for
lines to enable the detection of short lines.
The radon transform shown to be equivalent
to the hough method by Deans (1981) is an
invertible transform and so has been used for
linear feature enhancement as shown by
Murthy(1985) for radar images and for
detection of straight lines in simulated
noisy imagery as illustrated by
Nasrabadi(1984).
2.3 Transform Domain Processing
This class of processing technique comprises
a global two dimensional weighted transform
followed by a spatial frequency filter in the
A graph
consists
between r
arc is a
then seer
path betw'
graph. IJ
known the
solution
demon st rai
solution
(Palay(19
the usefu
for nois
guide the
3 discuss:
Local ope:
are typi
calculate
approach
is thus si
noise. Ot
susceptib:
with edge
effects
sophistic,
in low si
modelling
but param*
disadvantc
modelling
accurate :
they work
known noi;
algorithm
specific i
being lo <
embody mo
fully gi
techniques
and do nc
noise. Gr<
global or
evaluation
to noise,
optimal
Usually tl
processes
of knowle
extensive
exponenti
practical
initial b
provided.
(1984) has
this class
efficiency
We may n
thin line
eharacteri
human visu
criteria.
1) Globa!
