Neural Hypercolumn Architectures for the Preprocessing of Radiographic Weld Images

A general neural hypercolumn architecture is applied to radiographic weld images to locate regions of strong spatial intensity gradients. The hypercolumn output provides information on both the direction and the orientation of local spatial intensity gradients. These outputs can also be used to form an enhanced decimated image which can be processed for feature recognition. Parametric tuning of the architecture is discussed with particular emphasis on the requirements of the application. The performance of this architecture is compared with that of Sobel filters and other edge-detecting convolution masks. The possible representation of these various discrete convolution masks -including hypercolumns - as generalized non-adaptive neurons is also discussed.