Feature-based Image Matching for Identifying Individual K\=ak\=a
| Autor: | O'Sullivan, Fintan, Escott, Kirita-Rose, Shaw, Rachael C., Lensen, Andrew |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | This report investigates an unsupervised, feature-based image matching pipeline for the novel application of identifying individual k\=ak\=a. Applied with a similarity network for clustering, this addresses a weakness of current supervised approaches to identifying individual birds which struggle to handle the introduction of new individuals to the population. Our approach uses object localisation to locate k\=ak\=a within images and then extracts local features that are invariant to rotation and scale. These features are matched between images with nearest neighbour matching techniques and mismatch removal to produce a similarity score for image match comparison. The results show that matches obtained via the image matching pipeline achieve high accuracy of true matches. We conclude that feature-based image matching could be used with a similarity network to provide a viable alternative to existing supervised approaches. Comment: 42 pages, honour's report from Victoria University of Wellington |
| Databáze: | arXiv |
| Externí odkaz: |
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