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

Chapter 2: Related Work 20P 1P 2P 3Fig. 2.1. Tensor voting in the case of non-oriented points. The first pass: the votes arerepresented by the sticks directed along the lines joining the sites (and encoding tangents tocurve elements), and the lengths of the sticks decrease for farther voting sites. The ellipsoidreflects the orientation uncertainty of the tangent at the site. It is also the geometric expressionof the resulting tensor sum.Since we are mainly working with non-oriented points the technique that will bedescribed later in this dissertation is based on this first pass algorithm. Tensor votingfor the extraction of features in noisy datasets was shown to be a robust, fast, andintuitive method. It is also one of the few techniques that can handle sparse point-setswhich contain large gaps of missing data. The method has a remarkable generality thatinitiated applications to many areas of computer vision. Applications include solvingfor epipolar geometry estimation using 8-Dimensional Tensor Voting [Tang01], therecovery of motion layers [Tong04][Nicolescu03], the tracking of segmented objectsin image sequences [Kornprobst00], image repairing [Jia03], and color and texturesegmentation [Jia04]. The tensor-voting framework was also extended to handlepolarity information in addition to direction [Tong01].