Robust Region-based Line Detection from Poor Quality Images of Aligned 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. Following a preprocessing stage that specifies the connected regions of the image, the proposed method uses a kernel to specify each region's voting strength 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. 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 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, façade image rectification and skew detection and correction in rotated scanned documents.

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Robust Region-based Line Detection from Poor Quality Images of Aligned Rectangular Objects

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Robust Line Detection in Images of Building Facades using Region-based Weighted Hough Transform

Robust Line Detection in Images of Building Facades using Region-based Weighted Hough Transform

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

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