Automatic Target Recognition by Matching Oriented Edge Pixels
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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. Threedimensional objects are modeled by a set of twodimensional (2D) views of the object. Translation, rotation, and scaling of the views are allowed to approximate full threedimensional (3D) 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. INTRODUCTION 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 2D models to the image, 3D objects can be recognized by representing each object as a set of 2D 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, sixdimensional (6 D), transformation space. This representation provides a number of benefits. Edges are robust to changes in sensing conditions, and edgebased 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 [13]–[15]. for more please visit ::> http://cs.cornell.edu/~dph/papers/HOTIP97.pdf http://citeseerx.ist.psu.edu/viewdoc/dow...1&type=pdf 



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