Fully automatic road recognition remains an elusive goal in spite of many years of research. Most practical systems today use tedious manual tracing for the entry of data from satellite and aerial images to geographical data bases. The paper presents a semi-automatic method for the entry of such data. First ribbons of high contrast are found by analyzing gray scale surface principal curvatures. Then, pixels belonging to such ribbons are fitted by conic splines, and then a graph is constructed whose nodes are end points of the arcs fitted by the splines. The key new idea is to assign edges between all nodes and label them with a cost function based on physical constraints on roads. Once a pair of end points is chosen, a shortest path algorithm is used to determine the road between them. Thus a global optimization is performed over all possible candidates. >
['Jianying Hu', 'Bill Sakoda', 'Theo Pavlidis']
Interactive road finding for aerial images