5.7. Conclusion and Future Work
We have presented the current state of the tensor voting framework, which is a product of a number of years of research performed mostly at the University of Southern California. In this section, we present the contributions of the framework to perceptual organization and computer vision problems, as well as the axes of our ongoing and future research.
The tensor voting framework provides a general methodology that can be applied to a large range of problems as long as they can be posed as the inference of salient structures in a metric space of any dimension. The benefits from our representation and voting schemes are that no models need to be known a priori, nor do the data have to fit a parametric model. In ...
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