An iterative hough procedure for three-dimensional object recognition

摘要

This paper describes an iterative Hough procedure for recognizing images of three-dimensional objects. Straight line segments in the image are matched by finding the parameters of a viewing transformation of a three-dimensional model consisting of line segments. Assuming the scale of the object is known, there are three orientation and two translation parameters to be estimated. Initially a sparse, regular subset of parameters and transformations is evaluated for goodness-of-fit, then the procedure is repeated by successively subdividing the parameter space near current best estimates or peaks.