Generating edge detectors from a training ensemble
David B. Sher, Davin Milun · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
Our objective is to automatically generate an efficient edge detector given an ensemble of training images with known edge maps. This paper shows how to construct linear machines for edge detection from such an ensemble. Linear machines categorize data vectors into N categories by maximizing N - 1 linear functions (convolutions). The detector, that derives from artificial images with step edges, is significantly different from that derived from Canny's criteria. These differences suggest a new theory for edge detectors -- optimal operators that generate a fixed width response to edges. The preliminary suboptimal results from applying our linear machine are already comparable to that of the state of the art in edge detection.