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Coral reefs are vital for marine ecosystem and fishing industry. Automatic classification of corals is essential for the preservation and study of coral reefs. However, significant intra-class variations and inter-class similarity among coral genera, as well as the challenges of underwater illumination present a great hindrance for the automatic classification. We propose an end-to-end trainable Deep Fusion Net for the classification of corals from two types of images. The network takes two simultaneous inputs of reflectance and fluorescence images. It is composed of three branches: Reflectance, Fluorescence and Integration. The branches are first trained individually and then fused together. Finally, the Deep Fusion Net is trained end-to-end for the classification of different coral genera and other non-coral classes. Experiments on the challenging Eliat Fluorescence Coral dataset show that the Deep Fusion net achieves superior classification accuracy compared to other methods.
|Title of host publication||2019 Digital Image Computing|
|Subtitle of host publication||Techniques and Applications, DICTA 2019|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Publication status||Published - Dec 2019|
|Event||2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019 - Perth, Australia|
Duration: 2 Dec 2019 → 4 Dec 2019
|Name||2019 Digital Image Computing: Techniques and Applications, DICTA 2019|
|Conference||2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019|
|Period||2/12/19 → 4/12/19|
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1/01/15 → 31/12/21
1/01/15 → 31/12/20