Recent Development in Tensor Voting

Chi-Keung Tang


Abstract

In this 20 min presentation, I'll talk about what we've progressed from the 2D and 3D Tensor Voting. Surface curvature gives a unique, viewpoint independent description for local shape. In our previous versions, we did not estimate or use this information explicitly for the shape inference process. Here, through the use of homogeneous coordinates and curvature votes, we develop a robust, voting-based method that both estimates and incorporates curvature information into the current voting framework. In contrast to most previous approaches, No partial derivatives or other second order derivatives are computed, which are very extremely unstable. On the other hand, the tensor voting formalism has also been extended into higher dimensions. We have also applied the 8D version in robust epipolar geometry estimation that infers the hyperplane containing all the correct matches given a set of noisy point correspondences from two unregistered images, even in the presence of moving objects. Despite its simiarity to Hough Transform, by use of adequate data structures and locality, we can show that the space and time complexity of our method are independent of the dimensionality. Regardless of the dimensionality, our experiments show the different versions of our methods still work and degrades very gracefully in the presence of large amount of outlier noise (for example, only 1 out of 4 data points is good, i.e. the majority of the data is noise.)

On-line references

Integrated Surface Inference from Sparse Data Sets


Maintained by Alexandre R.J. FRANÇOIS