Abstract
This paper presents a Hopfield neural network model for matching features invariant to projective transformations. The projective invariance has been embedded into the compatibility constraint for the first time, such that the problem of finding point correspondences can be formulated by minimizing the predefined energy function through a Hopfield network. The neighborhood information of the data can help to reduce the fifth-order constraint to a second-order one, such as points along the silhouettes, or convex hull of a discrete set of points. The proposed method has been tested with a series of real images and performs well.
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