Multi-Scale Fish Segmentation Refinement and Missing Shape Recovery

Image processing and analysis techniques have drawn increasing attention since they enable a non-extractive and non-lethal approach to collecting fisheries data, such as fish size measurement, catch estimation, regulatory compliance, species recognition, and population counting. Measuring fish size accurately requires reliable image segmentation. Major challenges that can easily affect the segmentation include blurring of image areas due to water drops on the camera lens and parts of a fish body being out of the camera view. In this paper, we address each of these issues with an innovative and effective contour-based segmentation and a missing shape recovery method from an arbitrary initial segmentation. The refinement is processed from the coarse level to the fine level. At the coarse level, we align the entire fish contour of the initial segmentation with trained representative contours by using iteratively reweighted least squares (IRLS). At finer levels, we iteratively refine contour segments to represent poorly segmented or missing shape parts. This method addresses the problems listed above and generates promising results with highly robust segmentation performance and length measurement.