A New Weighted Region-based Hough Transform Algorithm for Robust Line Detection in Poor Quality Images of 2D Lattices of Rectangular Objects

In this work we present a novel kernel-based Hough Transform method for robust line detection in poor quality images of 2D lattices of rectangular objects. First, during a preprocessing stage, the connected regions of the image are determined. Then, a rectangularity score is computed for each region in order to filter out non-rectangular regions. Finally, the proposed method uses a kernel to specify each region’s contribution to the accumulator array based on the following shape descriptors: a) its rectangularity, b) the orientation of the major side of its minimum area bounding rectangle (MBR), and c) the MBR’s geometrical center. The proposed kernel is designed as the product of Gaussians having as footstep shape in Hough space that of a sinusoidal ribbon. Experimental and theoretical analysis on the uncertainties associated with the geometrical center as well as the polar parameters of the MBR’s major axis line equation allows for automatic selection of the parameters used to specify the shape of the kernel’s footstep (e.g. length and width of the ribbon) on the accumulator array. Comparisons performed on images of building facades taken under impaired visual conditions or with low accuracy sensors (e.g. thermal images) between the proposed method and other Hough Transform algorithms, show an improved accuracy of our method in detecting lines and/or linear formations. Finally, the robustness of the proposed method is shown in two other application domains those of, facade image rectification and skew detection and correction in rotated scanned documents.