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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 1D receptive fields that detect local surface shape and motion parameters within cross sections, we show how the recovered shape and motion model parameters are sufficient to produce local estimates of time to collision. In general, filter pairs (receptive fields) can be synthesized to perform or detect specific image deformations. At the heart of the method is the use of a matrix to represent image deformation correspondence between individual pixels of two views of a surface. The image correspondence matrix can be decomposed using singular value decomposition to yield a pair of corresponding receptive fields that detect image changes due to the deformation of interest.