Guided Scale Space Radon Transform for linear structures detection
Aicha Baya Goumeidane Djemel Ziou Nafaa Nacereddine
Using integral transforms to the end of lines detection in images with
complex background, makes the detection a hard task needing additional
processing to manage the detection. As an integral transform, the Scale Space
Radon Transform (SSRT) suffers from such drawbacks, even with its great
abilities for thick lines detection. In this work, we propose a method to
address this issue for automatic detection of thick linear structures in gray
scale and binary images using the SSRT, whatever the image background content.
This method involves the calculated Hessian orientations of the investigated
image while computing its SSRT, in such a way that linear structures are
emphasized in the SSRT space. As a consequence, the subsequent maxima detection
in the SSRT space is done on a modified transform space freed from unwanted
parts and, consequently, from irrelevant peaks that usually drown the peaks
representing lines. Besides, highlighting the linear structure in the SSRT
space permitting, thus, to efficiently detect lines of different thickness in
synthetic and real images, the experiments show also the method robustness
against noise and complex background.