Recent advances in statistically based ground penetrating radar (GPR) landmine detection have utilized 2-D slices of data to recognize the hyperbolic shapes caused by a sub-surface landmine. The objective in this research is to identify these shapes using methodology found in the field of image processing. Three different recognition methods were considered; (1) instance matching, which aims to recognize occurrences of a specific object; (2) object detection, which aims to find objects belonging to a class of objects; and (3) category recognition, which aims to categorize entire images based upon the contents of each image. This research consists of the adaptation and evaluation of these methods applied to GPR landmine detection. The results from this work illustrate the additional information that these methods provide to the GPR detection system. In addition, this work shows promise for the application of additional methods from the image processing and computer vision fields.
