Fast topology estimation for image mosaicing using adaptive information thresholding

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Over the past decade, several image mosaicing methods have been proposed in robotic mapping and remote sensing applications. Owing to rapid developments in obtaining optical data from areas beyond human reach, there is a high demand from different science fields for creating large-area image mosaics, often using images as the only source of information. One of the most important steps in the mosaicing process is motion estimation between overlapping images to obtain the topology, i.e., the spatial relationships between images. In this paper, we propose a generic framework for feature-based image mosaicing capable of obtaining the topology with a reduced number of matching attempts and of getting the best possible trajectory estimation. Innovative aspects include the use of a fast image similarity criterion combined with a Minimum Spanning Tree (MST) solution, to obtain a tentative topology and information theory principles to decide when to update trajectory estimation. Unlike previous approaches for large-area mosaicing, our framework is able to naturally deal with the cases where time-consecutive images cannot be matched successfully, such as completely unordered sets. This characteristic also makes our approach robust to sensor failure. The performance of the method is illustrated with experimental results obtained from different challenging underwater image sequences ​
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