3-D object recognition using adaptive scale MEGI

We propose a method for recognition of a 3-D object using a multi scale description of the object and adaptive matching. MEGI model is a description model to represent arbitrary shapes. However, many MEGI elements are necessary to represent uneven or carved surfaces with accuracy, so it is difficult to use them for recognition. As a solution, we make a tree which corresponds to the multi scale description of the object. While tracing the tree from the root which corresponds the coarsest representation to leaves, a matching algorithm presented in this paper assigns a different scale to each part of the object adaptively and estimates the matching score effectively.

[1]  Hiroshi Matsuo,et al.  3-D object recognition using MEGI model from range data , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[2]  Nicholas I. Fisher,et al.  Correlation coefficients for random variables on a unit sphere or hypersphere , 1986 .

[3]  L. A. Li︠u︡sternik.  Convex figures and polyhedra , 1966 .

[4]  Luc Cournoyer,et al.  The NRCC three-dimensional image data files , 1988 .

[5]  Berthold K. P. Horn.  Extended Gaussian images , 1984, Proceedings of the IEEE.

[6]  James J. Little.  Determining Object Attitude from Extended Gaussian Images , 1985, IJCAI.

[7]  Mutsuo Sano,et al.  Three-dimensional object recognition using spherical correlation , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.