Fast Extraction of 3D Fourier Moments via Multiple Integral Images: An Application to Antitank Mine Detection in GPR C Scans
Automatic landmine detection is a challenging problem in post conflict areas. This paper demonstrates experiments on antitank mine detection in subsurface 3D images generated by Ground Penetrating Radar. In the algorithmic part, we study piecewise Fourier approximations of low orders which are applied to successive 3D windows of an image. We take coefficients moments of these approximations as features for machine learning. The main contribution of the paper is a technique to calculate the moments fast. We construct a set of suitable integral images, which allows later to calculate each moment in constant time. The technique is akin to, but significantly different from the known idea of Viola and Jones, namely: we calculate Fourier moments rather than simple differential features; secondly, we use multiple integral images, not a single one; thirdly, our integral images are cumulative inner products between input image and suitable trigonometric terms. Conducted tests involve boosted decision trees as detectors.
ICCVG