Segments matching: comparison between a neural approach and a classical optimization way

We describe and compare two approaches to achieve segments matching between two images from a sequence, without any knowledge on the viewed object and/or on the motion of the camera between the different images. The first method uses an Hopfield neural network with several local constraints like correlation and distance between segments of consecutive images. The second algorithm is an iterative optimization of a criterion. We model the primitives displacement between the images by an homographic transform. Then we search, with a Levenberg-Marquardt method, the homography matrix giving the best match between the segments of the two images. The two algorithms are validated and compared on sequences of real images.