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Chapter 3: A Feature Saliency Descriptor for Registration and Pose Recovery 37The expression of the accumulated votes at a given site is similar to (3.9), while thelocal saliency measure is given by the determinant of the tensor T i(square of thevolume Viof the bounding box of the uncertainty ellipsoid):21 2 3)2iD = detT= ( λ λ λ = V(3.13)ii3.4 A 3D Registration Criterion Derived from Joint VotingThe development of the feature saliency descriptor presented in the previous sectionwas primarily geared toward the task of 3D point-sets registration. The emphasis onthe analytic expression of the local saliency descriptor was motivated by our goal toaddress the registration task in the framework of standard gradient-based optimizationtechniques. Our first alignment criterion is based on the computation of globalsaliency for the joint datasets to be registered. The method relies on the fact that at theunregistered position the point-sets will normally have little interaction, due to thelocal nature of saliency inference. On the other hand, at the aligned position structurescommon to the registered point-sets will overlap resulting in a local increase in thenumber of votes, and hence of the measure of feature saliency as defined above. Thisis illustrated in Fig. 3.9. If we are to register the datasets P1, P2,..., PN, and ifP( Θ ) = P1 ∪ P2... ∪ P N, with Θ being the transformation parameter vector, we candefine, for a given pose Θ , the criterion that measures the total saliency of theresulting structure as:∑C ( Θ)= S(X )(3.14)X∈P(Θ)
Chapter 3: A Feature Saliency Descriptor for Registration and Pose Recovery 38Eigen-system ellipses(a)(b)Fig. 3.9. Given the local nature of Tensor Voting the two datasets will have little interaction atthe unregistered position (a). When the two patterns overlap (b) local saliency (proportional touncertainty ellipse size) in the intersection will increase while remaining the same in the otherareas.
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Page 5 and 6: AbstractThe focus of this research
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Page 13 and 14: Chapter 1: Introduction 2The proble
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Chapter 6: Results 92Z-variance = 2
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Bibliography 144BIBLIOGRAPHY[Anders
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Bibliography 146[Chiba98] N. Chiba
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Bibliography 148[Fruh01] C. Früh,
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Bibliography 150[Jebara99] T. Jebar
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Bibliography 152IEEE International
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Bibliography 154[Schmid00] C. Schmi
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Bibliography 156[Wollny02] G. Wolln
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