Let F(x, a) be a real polynomial in two sets of variables, x and a, that is linear with respect to one of the variable sets, say a. In this paper, we deal with two of the main steps of the Hough transform framework for the pattern recognition technique to detect loci in images. More precisely, we present an algorithmic process, based on the Moore–Penrose pseudo-inverse, to provide a region of analysis in the parameter space. In addition, we state an upper bound for the sampling distance of the discretization of the parameter space region.
Moore–Penrose approach in the Hough transform framework
Beltrametti M. C.;Torrente M.
2020-01-01
Abstract
Let F(x, a) be a real polynomial in two sets of variables, x and a, that is linear with respect to one of the variable sets, say a. In this paper, we deal with two of the main steps of the Hough transform framework for the pattern recognition technique to detect loci in images. More precisely, we present an algorithmic process, based on the Moore–Penrose pseudo-inverse, to provide a region of analysis in the parameter space. In addition, we state an upper bound for the sampling distance of the discretization of the parameter space region.
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