Projection-based high accuracy measurement of straight line edges

Abstract

We present a novel projection-based high accuracy algorithm to determine the parameters of a straight edge in a noisy image. Our algorithm is equivalent to applying a set of very long directional operators over a range of finely quantized angles. This is known to improve both signal-to-noise ratio and localization in the measurement of straight line edges, but was rarely used in practice because of the expense and coefficient restrictions of the operators. The algorithm is implemented by taking a set of projections of the original grey level image, filtering the projections, and analyzing the peaks in projection space to estimate line offset and angle. It also includes a procedure to analyze the peaks in projection space which provides an accuracy better than the quantization in offset and angle parameters. The algorithm was tested on synthetic and a large number of real images and offers very high (subpixel) offset and angular accuracy. The advantages of the algorithm over traditional approaches are improved signal-to-noise ratio and localization accuracy (due to the effective use of very long directional edge operators), no need for expensive edge operators and related hardware, and no need for sophisticated thresholding of the gradient image for finding edges. It is robust in the presence of many types of texture, patterned and bias noise, light intensity, and focus change, and is ideal for use in industrial machine vision, where a large number of parts with straight edges are processed. © 1988 Springer-Verlag.