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.

翻译：利用积分变换检测复杂背景图像中的线条，使检测成为一项需要额外处理来管理的艰巨任务。作为积分变换的一种，尺度空间Radon变换（SSRT）尽管在检测粗线条方面具有强大能力，但仍存在此类缺陷。本文提出一种方法，通过SSRT自动检测灰度图像和二值图像中的粗线性结构，无论图像背景内容如何。该方法在计算SSRT的同时，利用被检测图像的Hessian方向，使得线性结构在SSRT空间中得到增强。因此，后续在SSRT空间中的最大值检测是在一个消除无关部分、进而消除通常掩盖线条峰值的无关峰值的修正变换空间上进行的。此外，在SSRT空间中突出线性结构能够有效检测合成图像和真实图像中不同粗细的线条，实验还表明该方法对噪声和复杂背景具有鲁棒性。