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Automatic Fish Segmentation and Recognition for Trawl-Based Cameras
Abstract
Camera-based fish abundance estimation with the aid of visual analysis techniques has drawn increasing attention. Live fish segmentation and recognition in open aquatic habitats, however, suffers from fast light attenuation, ubiquitous noise and non-lateral views of fish. In this chapter, an automatic live fish segmentation and recognition framework for trawl-based cameras is proposed. To mitigate the illumination issues, double local thresholding method is integrated with histogram backprojection to produce an accurate shape of fish segmentation. For recognition, a hierarchical partial classification is learned so that the coarse-to-fine categorization stops at any level where ambiguity exists. Attributes from important fish anatomical parts are focused to generate discriminative feature descriptors. Experiments on mid-water image sets show that the proposed framework achieves up to 93% of accuracy on live fish recognition based on automatic and robust segmentation results.
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