Abstract
A novel procedure is presented to construct image-domain filters (receptive fields) that directly recover local motion and shape parameters. These receptive fields are derived from training on image deformations that best discriminate between different shape and motion parameters. Beginning with the construction of 1-D receptive fields that detect local surface shape and motion parameters within cross sections, we show how the recovered model parameters are sufficient to produce local estimates of optical flow, focus of expansion, and time to collision. The theory is supported by a series of experiments on well-known image sequences for which ground truth is available. Comparisons against published results are quite competitive, which we believe to be significant given the local, feed-forward nature of the resulting algorithms.
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