We present a unified computational framework to generate desiriptions in terms of regions, curves, and labelled junctions, from sparse, noisy, binary data in 2-D. Each input site can be a point, a point with an associated tangent direction, a point with an associated tangent vecotr. or any combination of the above. The methodology is grounded on two elements: tensor calculus for reprsentation, and non-linear viting for communication. Each input site communicates its information (a tensor) to its neighborhood through a redefined (tensor) field, and therefore casts a (tensor) vote. Each iste collects all the votes cast at tis location and econdes them into a new tensor. A local, paralel rountine then simultaneously deects junctions, curves and region boudaries. The proposed approach is non-iterative, and the only free parameter is the size of the neighborhood, related to the scale. We illustrate the approach with results on a variety of images, then outline futher applications.
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