To the Graduate Council: I am submitting herewith a dissertation ...

Chapter 3: A Feature Saliency Descriptor for Registration and Pose Recovery 32The function δ (P)depends on the eigenvalues of S (P)and employs theSurfappropriate thresholds to determine points that belong to two types of salientstructures: surfaces and curves as explained in [Medioni00]. Fig. 3.6 shows the 3Dreconstruction for different pose parameters, starting at the correct parameters at theleft and gradually changing one of the rotation parameters. In the lower sequence areshown the resulting salient points after the tensor voting and feature inference process.Isolated points were removed. The actual variation ofin Table 3.7. We see thatemployed in a pose recovery framework.NSfor this sequence is shownN Scan be used as a point-set saliency measure andThis criterion is not expressed analytically in terms of the datasets and thetransformation parameters and optimization will have to be heuristic. But the notion ofevaluating the saliency of a set of points was the important idea that will be exploitedfor our core problem of general point-sets registration. In the next section we will shiftour focus and present our derivation of an analytically expressed saliency criterion forthe purpose of point-sets and free-form shape registration. While the main emphasiswas first on the global saliency measure of a set of points, the local descriptor turnedout to be more useful in and of its self.3.3 The Local Saliency DescriptorOur purpose at this point is to design a simple local feature descriptor which will bebased on the principles of tensor voting. Being based on a robust feature inferenceframework the new descriptor is expected to have a good performance in the presenceof noise and in the case of sparse and non-uniformly sampled datasets. Thisperformance will be confirmed later in through experimental analysis. In this work weare mainly interested in the basic case of non-oriented clouds of points. To derive theexpression of our descriptor we employ the first pass of Tensor Voting.