Generalization of Brady-Yong Algorithm for Fast Hough Transform to Arbitrary Image Size
This is an incremental improvement for image processing and tomography applications, enabling more flexible use of the Hough transform.
The paper tackles the limitation of fast Hough transform algorithms, which typically require power-of-two image sizes, by proposing a new algorithm that works for arbitrary sizes while maintaining optimal computational complexity and achieving higher accuracy.
Nowadays, the Hough (discrete Radon) transform (HT/DRT) has proved to be an extremely powerful and widespread tool harnessed in a number of application areas, ranging from general image processing to X-ray computed tomography. Efficient utilization of the HT to solve applied problems demands its acceleration and increased accuracy. Along with this, most fast algorithms for computing the HT, especially the pioneering Brady-Yong algorithm, operate on power-of-two size input images and are not adapted for arbitrary size images. This paper presents a new algorithm for calculating the HT for images of arbitrary size. It generalizes the Brady-Yong algorithm from which it inherits the optimal computational complexity. Moreover, the algorithm allows to compute the HT with considerably higher accuracy compared to the existing algorithm. Herewith, the paper provides a theoretical analysis of the computational complexity and accuracy of the proposed algorithm. The conclusions of the performed experiments conform with the theoretical results.