The generalized Hough transform is an appropriate technique for pattern recognition problems involving incomplete information. Previous Hough-based approaches to the problem of 3-D object orientation determination from 2-D data have been limited in application to objects with straight line or vertex leatures generating possible correspondences between standard and transformed object views. This is because use of more general feature sets such as object contour points results in an unreasonable computational effort to solve for the transformation parameters each solution of the underdetermined equations generates a multiplicity of entries for the multidimensional arrays used to implement the transformation parameter solution space. Other methods which are not based on Hough table resolution but which exploit tensor invariance of imaged object features have proven useful for object identity and pose determination but these have required complete object feature data. A curve parameterization which exhibits tensor invariance under the affine transformation relating standard and transformed object views has been employed by the authors to generate local feature data for objects with partially extractable planar features. The transformation parameters are obtained from the data by solving a completely specified set oflinear equations greatly reducing the computational complexity of the Hough approach. The solutions can then be subjected to flexible constraints consistent with rigid 3-D motion orthographic projection and geometric invariance. Each tentative match generates a single entry in the Hough parameter space. The sparsely filled solution

Date Published: 1 November 1990
PDF: 8 pages
Proc. SPIE 1349, Applications of Digital Image Processing XIII, (1 November 1990); doi: 10.1117/12.23561

Published in SPIE Proceedings Vol. 1349:
Applications of Digital Image Processing XIII
Andrew G. Tescher, Editor(s)