Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues




Mehrnejad, Marzieh

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Underwater video data are a rich source of information for marine biologists. However, the large amount of recorded video creates a ’big data’ problem, which emphasizes the need for automated detection techniques. This work focuses on the detection of quasi-stationary crabs of various sizes in deep-sea images. Specific issues related to image quality such as low contrast and non-uniform lighting are addressed by the pre-processing step. The segmentation step is based on color, size and shape considerations. Segmentation identifies regions that potentially correspond to crabs. These regions are normalized to be invariant to scale and translation. Feature vectors are formed by the normalized regions, and they are further classified via supervised and non-supervised machine learning techniques. The proposed approach is evaluated experimentally using a video dataset available from Ocean Networks Canada. The thesis provides an in-depth discussion about the performance of the proposed algorithms.



Computer Vision, Classification, Neural Networks, Machine Learning