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- W4385697923 abstract "Automated photo-identification of blue whale individuals has received little attention over the years compared to humpback and right whales, most likely because of the difficulty of the task: blue whales are identified using the low-contrast pigmentation of their skin (light and dark shades of grey displayed in patches of varying sizes). This paper explores the use of three local feature matching techniques (SIFT, L2-Net and LoFTR) for blue whale photo-identification and compares their performance to that of a baseline method (Pose Invariant Embeddings, or PIE) which has been used for humpback whale identification. We conduct experiments using a rich dataset made of 3129 images of 807 unique individuals acquired over a 40-year period. Results show a large difference in performance between all methods, with LoFTR outperforming the other three by a large margin. Measured top-1 and top-10 accuracies (76.7% and 80.3% respectively) suggest that LoFTR is suitable for the robust matching of images of blue whales." @default.
- W4385697923 created "2023-08-10" @default.
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- W4385697923 date "2023-01-01" @default.
- W4385697923 modified "2023-09-23" @default.
- W4385697923 title "Automated Blue Whale Photo-Identification Using Local Feature Matching" @default.
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- W4385697923 doi "https://doi.org/10.1007/978-3-031-37731-0_34" @default.
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