Date of Award

12-2010

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Computer Engineering

Advisor

Schalkoff, Robert

Committee Member

Ligon , Walter

Committee Member

Birchfield , Stanley

Committee Member

Geist , Robert

Abstract

This work describes a parallelizable optical flow estimator that uses a modified batch version of
the Self Organizing Map (SOM). This gradient-based estimator handles the ill-posedness in motion
estimation via a novel combination of regression and a self organization strategy.
The aperture problem is explicitly modeled using an algebraic framework
that partitions motion estimates obtained from regression into two sets, one (set Hc) with estimates
with high confidence and another (set Hp) with low confidence estimates. The self organization step
uses a uniquely designed pair of training set (Q=Hc) and the initial weights set (W=Hc U Hp).
It is shown that with this specific choice of training and initial weights sets, the interpolation
of flow vectors is achieved primarily due to the regularization property of SOM. Moreover, the
computationally involved step of finding the winner unit in SOM simplifies to indexing into a 2D array
making the algorithm parallelizable and highly scalable. To preserve flow discontinuities at occlusion boundaries,
we have designed anisotropic neighborhood function for SOM that uses a novel OFCE residual-based
distance measure.
A multi-resolution or pyramidal approach is used to estimate large motion. As the algorithm is
scalable, with sufficient number of computing cores (for example on a GPU), the implementation
of the estimator can be made real-time. With the available true motion from Middlebury database,
error metrics are computed.

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