Automatic Target Recognition by Matching Oriented Edge Pixels
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20-10-2010, 11:05 AM

Automatic Target Recognition by
Matching Oriented Edge Pixels

Clark F. Olson and Daniel P. Huttenlocher

Abstract—This paper describes techniques to perform efficient
and accurate target recognition in difficult domains. In order to
accurately model small, irregularly shaped targets, the target objects
and images are represented by their edge maps, with a local
orientation associated with each edge pixel. Three-dimensional
objects are modeled by a set of two-dimensional (2-D) views of
the object. Translation, rotation, and scaling of the views are
allowed to approximate full three-dimensional (3-D) motion of the
object. A version of the Hausdorff measure that incorporates both
location and orientation information is used to determine which
positions of each object model are reported as possible target
locations. These positions are determined efficiently through the
examination of a hierarchical cell decomposition of the transformation
space. This allows large volumes of the space to be
pruned quickly. Additional techniques are used to decrease the
computation time required by the method when matching is
performed against a catalog of object models. The probability
that this measure will yield a false alarm and efficient methods
for estimating this probability at run time are considered in detail.
This information can be used to maintain a low false alarm rate or
to rank competing hypotheses based on their likelihood of being
a false alarm. Finally, results of the system recognizing objects in
infrared and intensity images are given.

THIS PAPER considers methods to perform automatic target
recognition by representing target models and images
as sets of oriented edge pixels and performing matching in
this domain. While the use of edge maps implies matching
2-D models to the image, 3-D objects can be recognized by
representing each object as a set of 2-D views of the object.
Explicitly modeling translation, rotation in the plane, and
scaling of the object (i.e. similarity transformations), combined
with considering the appearance of an object from the possible
viewing directions, approximates the full, six-dimensional (6-
D), transformation space.
This representation provides a number of benefits. Edges
are robust to changes in sensing conditions, and edge-based
techniques can be used with many imaging modalities. The
use of the complete edge map to model targets rather than approximating
the target shape as straight edge segments allows
small, irregularly shaped targets to be modeled accurately.
Furthermore, matching techniques have been developed foredge maps that can handle occlusion, image noise, and clutter
and that can search the space of possible object positions
efficiently through the use of intelligent search strategies that
are able to rule out much of the search space with little work.
One problem that edge matching techniques can have is that
images with considerable clutter can lead to a significant rate
of false alarms. This problem can be reduced by considering
not only the location of each edge pixel but, in addition,
their orientations when performing matching. Our analysis
and experiments indicate that this greatly reduces the rate at
which false alarms are found. An additional benefit of this
information is that it helps to prune the search space and thus
leads to improved running times.
We must have some decision process that determines which
positions of each object model are output as hypothetical
target locations. To this end, Section II describes a modified
Hausdorff measure that uses both the location and orientation
of the model and image pixels in determining how well a
target model matches the image at each position. Section III
then describes an efficient search strategy for determining the
image locations that satisfy this modified Hausdorff measure
and are thus hypothetical target locations. Pruning techniques
that are implemented using a hierarchical cell decomposition
of the transformation space allow a large search space to be
examined quickly without missing any hypotheses that satisfy
the matching measure. Additional techniques to reduce the
search time when multiple target models are considered in the
same image are also discussed.
In Section IV, the probability that a false alarm will be
found when using the new matching measure is discussed,
and a method to estimate this probability efficiently at run
time is given. This analysis allows the use of an adaptive
algorithm, where the matching threshold is set such that the
probability of a false alarm is low. In very complex imagery,
where the probability of a false alarm cannot be reduced to
a small value without the risk of missing objects that we
wish to find, this estimate can be used to rank the competing
hypotheses based on their likelihood of being a false alarm.
Section V demonstrates the use of these techniques in infrared
and intensity imagery. The accuracy with which we estimate
the probability of a false alarm is tested, and the performance
of these techniques is compared against a similar system that
does not use orientation information. Finally, a summary of
the paper is given.
Due to the volume of research that has been performed
on automatic target recognition, this paper discusses only
the previous research that is directly relevant to the ideasdescribed here. The interested reader can find overviews of
automatic target recognition from a variety of perspectives in
[2], [3], [6], [9], and [22]. Alternative methods of using object
edges or silhouettes to perform automatic target recognition
have been previously examined, for example, in [7], [20], and
[21]. Portions of this work have been previously reported in

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