The importance of using neural networks for image registration has increased since the enhancement in technology responsible for capturing images. Traditional methods rely on manual selection of control points and/or finding a suitable geometric transformation that maps two images. This approach is especially tedious and time consuming for registering multiple images. Further, traditional methods are not able to register images effectively if non-linear transformations are used to convert one image into another. To provide a robust and efficient way of registering images, neural networks provide a powerful alternative. They have proved to be highly reliable especially with medical and satellite imaging; making room for uncertainty and imprecision. This paper highlights the important image registration approaches that make use of neural networks and performs a comparative analysis of these approaches. It also suggests suitable areas in which research can be carried out to improve the efficacy and scalability of the techniques.

Image registration; neural networks; non-linear transformations

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