A Low-Complexity Mosaicing Algorithm for Stock Assessment of Seabed-Burrowing Species

This paper proposes an algorithm for mosaicing videos generated during stock assessment of seabed-burrowing species. In these surveys, video transects of the seabed are captured and the population is estimated by counting the number of burrows in the video. The mosaicing algorithm is designed to process a large amount of video data and summarize the relevant features for the survey in a single image. Hence, the algorithm is designed to be computationally inexpensive while maintaining a high degree of robustness. We adopt a registration algorithm that employs a simple translational motion model and generates a mapping to the mosaic coordinate system using a concatenation of frame-by-frame homographies. A temporal smoothness prior is used in a maximum a posteriori homography estimation algorithm to reduce noise in the motion parameters in images with small amounts of texture detail. A multiband blending scheme renders the mosaic and is optimized for the application requirements. Tests on a large data set show that the algorithm is robust enough to allow the use of mosaics as a medium for burrow counting. This will increase the verifiability of the stock assessments as well as generate a ground truth data set for the learning of an automated burrow counting algorithm.

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