Robust line fitting in a noisy image by the method of moments

A highly robust estimator for regression models

“…The data points are taken from Fig. 3 of Qjidaa and Radouane (1999). It was claimed that 30% of the data points are inliers generated from the equation: N(0, 1), and 70% are outliers (b 1 = b 0 = 1).…”

“…A robust line-fitting method is given in (Frigui et al, 1998) detection in a range image where the background model needs to be estimated with outliers stemming from targets and noise. A new statistical clustering method based on Legendre moment theory and maximum entropy principle for line fitting in a noisy image is formulated in (Qjidaa and Radouane, 1999). The algorithms proposed in Bruckstein, 1992, 2000) are also based on the Hough transform and can be used to solve the problem of efficient linefitting in the presence of errors in both coordinates.…”

Fast and low-complexity method for exact computation of 3D Legendre moments

“…Legendre orthogonal moments can be used to represent an image with the minimum amount of information redundancy (Teh and Chin, 1988). Based on these attractive properties, Legendre moments are used in many applications such as pattern recognition (Chong et al, 2004, Luo andLin, 2007), face recognition (Haddadnia et al, 2001), line fitting (Qjidaa and Radouane, 1999), texture analysis (Bharathi and Ganesan, 2008), template matching (Omachi andOmachi, 2007, Hosny, 2010b), palm-print verification (Pang et al, 2003), occupant classification system for automotive airbag suppression (Farmer and Jain, 2003), comparison of two-dimensional polyacrylamide gel electrophoresis maps images (Marengoa et al, 2005), retrieval and classification (Yadav et al, 2008), and tool wear monitoring (Barreiro et al, 2008). It is well known that, the direct computation of Legendre moments is time consuming process and the computational complexity increased by increasing the moment order.…”

Blocking artifact removal using partial overlapping based on exact Legendre moments computation

“…They can be used to represent an image with the minimum amount of information redundancy and a high capability for image representation [1]. Based on these attractive proprieties, Legendre moments have been widely used in many research fields, such as pattern recognition [9,10], face recognition [11], image indexing [12], line fitting [13], and image processing [14][15][16].…”