General purpose curvilinear object detection with fuzzy mathematical morphology
Display Omitted We present two general purpose algorithms for curvilinear object detection.The first one is based on the fuzzy morphological hit or miss transform.The second one uses the fuzzy top hat transform.We compare them with other state of the art general purpose methods.We show the strengths and potential of our algorithms across varied research grounded imagery. Curvilinear object detection is the common denominator of several applications. Some illustrative examples are road detection from aerial or satellite images, human airways from volumetric 3D scans or vascular structures in eye fundus images. In this work, we propose two general purpose curvilinear object detectors that may serve as building blocks for application specific systems. To do so, we employ fuzzy mathematical morphology operators due to their robustness with respect to uncertainty and noise, and the trade off they offer between expressive power and computational requirements. The extraction of linear features is based on, respectively, the fuzzy hit or miss transform and the fuzzy top hat transform. They can be customized depending on the width of the objects of interest. We compare these two approaches with other state of the art, general purpose curvilinear object detectors to highlight their strengths and shortcomings. Both detectors succeed at localizing the objects of interest in different greyscale images.
Appl. Soft Comput.