Cooperative Verification Using Radiography Behind an Information Barrier.

Our technique utilizes feature matching in radiographic images of complex items. The SURF (Speeded Up Robust Features) method is used to extract features from the images. FLANN (Fast Learning Artificial Neural Network) is used in the matching process. The feature list becomes the template. The SURF features are somewhat rotation, scale, and translation invariant, which means the reference and target images need not be taken from the exact same position for the source and film, making data collection easier. A significant discovery is that we can discard the position, size, and orientation information of the features and still perform the matching adequately. Without this information, geometry cannot be recovered; we believe it is impossible to reconstruct the image in this case, creating an irreversible transform that creates non-sensitive feature lists, or templates. This method is analogous to using a paper shredder to prevent reconstruction of an original while still being able to match features from the individual shredded pieces.