Type of Document	Master's Thesis
Author	Paripati, Praveen Kumar
URN	etd-06102012-040611
Title	Polyhedra:representation and recognition
Degree	Master of Science
Department	Computer Science and Applications
Advisory Committee	Advisor Name Title Roach, John W. Committee Chair Bixler, J. Patrick Committee Member Ehrich, Roger W. Committee Member Heath, Lenwood S. Committee Member
Keywords	Polyhedra
Date of Defense	1989-05-15
Availability	restricted
Abstract Computer Aided Design systems intended for three dimensional solid modelling have traditionally used geometric representations incompatible with established representations in computer vision. The utilization of object models built using these systems require a representation conversion before they can be used in automatic sensing systems. Considerable advantages follow from building a combined CAD and sensing system based on a common geometric model. For example, a library of objects can be built up and its models used in vision and touch sensing system integrated into an automated assembly line to 'discriminate between objects and determine- orientation and distance. This thesis studies a representation scheme, the dual spherical representation, useful in geometric modelling and machine recognition. We prove that the representation uniquely represents genus 0 polyhedra. We show by,example that our representation is not a strict dual of the vertex connectivity graph, and hence is not necessarily ambiguous. However, we have not been able to prove that the representation is unambiguous. An augmented dual spherical representation which is unique for general polyhedra is presented. This graph theoretic approach to polyhedra also results in an elegant method for decomposition of polyhedra into combinatorially convex parts. An algorithm implementation details and experimental results for recognition of polyhedra using a large field tactile sensor are given. A theorem relating the edges in the dual spherical representation and the edge under perspective projection is proved. Sensor fusion using visual and tactile sensory inputs is proposed to improve recognition rates.
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