Modified Discrete Radon Transforms and Their Application to Rotation-Invariant Image Analysis

This paper presents two novel transforms based on the discrete Radon transform. The proposed transforms smartly solve two inherent problems of the Radon transform in rotation estimation in digital images, i.e., direction-dependency and nonhomogeneity, that come from the different numbers of pixels projected on a line for different directions and/or coordinates of a direction. While the first transform considers the sample mean operator on the same sets of pixels for a direction instead of summation in the discrete Radon transform, the second transform uses the mean operator on sets of pixels with the equal number of elements. In order to show the efficiency of the proposed transforms, we apply them on image collections from the Brodatz album for estimating the directional information. Experimental results show a significant increase in correct estimation as well as in the processing time compared to the conventional Radon transform