%0 Journal Article
	%A H. B. Darbandi and  M. R. Ito and  J. Little
	%D 2008
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 17, 2008
	%T Clustered Signatures for Modeling and Recognizing 3D Rigid Objects
	%U https://publications.waset.org/pdf/9067
	%V 17
	%X This paper describes a probabilistic method for
three-dimensional object recognition using a shared pool of surface
signatures. This technique uses flatness, orientation, and convexity
signatures that encode the surface of a free-form object into three
discriminative vectors, and then creates a shared pool of data by
clustering the signatures using a distance function. This method
applies the Bayes-s rule for recognition process, and it is extensible
to a large collection of three-dimensional objects.
	%P 1374 - 1382