Resolution analysis for Gradient Direction Matching of object model edges to overhead images☆
Section snippets
Background and introduction
This paper addresses the problem of broad area search for specific objects or families of related objects in sets of overhead images (aerial or satellite) with broad area coverage. This is a problem of major importance to human analysts and photo-interpreters in government and military services responsible for quickly analyzing enormous volumes of overhead imagery. Computer assistance is required because human analysts are only able to manually search large volumes of overhead imagery at a
Gradient direction fields
GDM requires a field of gradient directions for image pixels and gradient directions for object edges projected onto the image. Gradient directions are estimated differently in each case.
Gradient Direction Matching
For an 8-bit image u with w columns of pixels (the width) and h rows of pixels (the height), the wxh array θ of pixel gradient directions θ(c, r) can be estimated using (1). A corresponding wxh binary array of thresholded pixel gradient magnitudes a(c, r) can be generated by setting a(c, r) = 1 only when the gradient magnitude A(c, r) in (1) is greater than some minimum perception threshold A0 on 8-bit gray-value contrast (e.g., A0 = 8).
Next, suppose the physical model of some object to be detected
GDM performance at variable matching resolutions
This Section addresses two fundamental issues related to GDM efficiency and statistical performance, namely (1) how small the object model projections can be, and (2) how many object rotations need to be matched. An understanding of these issues allows image spatial resolution and the number of object rotations to be chosen prior to model matching so as to maximize the pixel throughput rate while maintaining good matching performance.
Suppose N rotations of an object are to be matched to an
Summary and conclusions
A novel Gradient Direction Matching (GDM) algorithm that matches physical models of objects to overhead images with broad area coverage has been presented. It was designed to handle variations in scene clutter and image acquisition conditions. As a hybrid algorithm, GDM incorporates philosophies consistent with both edge-based matching and image-based matching. It matches orientations of object model edges on the one hand to image pixel gradient directions (as opposed to image edges, which are
Acknowledgements
The authors thank George Weinert for implementing the image thumbnail-based query interface and running scripted experiments. Thanks also to Chuck Grant for developing and implementing the algorithms that project object model edges onto overhead images.
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This work was performed under the auspices of the U.S. Department of Energy by the University of California, Lawrence Livermore National Laboratory under Contract No. W-7405-Eng-48 (UCRL-JRNL-217038).