Laplacian-Gaussian Sub-Correlation Analysis for Scale Space Imaging

Abstract

Two universal imaging rules, self-similarity and perspective projection, are combined for detecting almost horizontal space in complex scene. By extracting scale shift due to perspective projection through Laplacian-Gaussian image analysis, the expansion of maneuvering area is estimated in terms of the invariant measure associated with a fractal attractor. Estimated maneuvering area is shown to be visualized on the scene image without explicit geometry of the roadway. The representation scheme was verified through experimental studies.