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 operation. 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 binary and gray-scale 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. The proposed method has, furthermore, achieved detection with errors very close to the zero value, when assessing its accuracy using supervised evaluation with synthetic images.

在具有复杂背景的图像中使用积分变换进行行尾检测使得检测成为一项艰巨的任务，需要额外的处理来管理操作。作为一种积分变换，尺度空间氡变换（SSRT）尽管具有很强的粗线检测能力，但也存在这样的缺点。在这项工作中，我们提出了一种解决此问题的方法，无论图像背景内容如何，​​都使用 SSRT 自动检测二值和灰度图像中的粗线性结构。该方法涉及在计算 SSRT 时计算所研究图像的 Hessian 方向，从而在 SSRT 空间中强调线性结构。因此，SSRT 空间中的后续最大值检测是在修改后的变换空间上完成的，该空间没有不需要的部分，因此也没有不相关的峰值，这些峰值通常会淹没表示线的峰值。此外，突出 SSRT 空间中的线性结构，从而有效地检测合成图像和真实图像中不同粗细的线，实验还表明该方法对噪声和复杂背景的鲁棒性。此外，当使用合成图像的监督评估来评估其准确性时，所提出的方法实现了误差非常接近于零值的检测。