Simultaneous detection of lines and edges using compound Gabor filters

Lines and edges play an important role in pattern recognition because they mark object surfaces and boundaries. In spite of many attempts to construct optimal detectors, e.g. an edge detector, it appears that all known algorithms have problems at locations where lines and edges are very close and/or intersect. Furthermore, there are still very few schemes which can detect and classify lines and edges (events) simultaneously. In view of the fact that the human visual system does not seem to suffer from any problems, our aim is to develop an event detection scheme that makes use of biologically-motivated operators and, therefore, to overcome the problems known from the literature. In this scheme we apply 2D complex Gabor filters and exploit the local phase information. In order to cope with events having a different curvature, we develop an adaptive scheme that is based on compound Gabor filter kernels with different orientation bandwidths.