%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