Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens
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arXiv
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2025
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| author | East, Alyson Campolongo, Elizabeth G. Meyers, Luke Rayeed, S M Stevens, Samuel Zarubiieva, Iuliia Fluck, Isadora E. Girón, Jennifer C. Jousse, Maximiliane Lowe, Scott Perry, Kayla I Betancourt, Isabelle Charney, Noah Donoso, Evan Fox, Nathan Landsbergen, Kim J. Nepovinnykh, Ekaterina Ramirez, Michelle Singh, Parkash Thapa-Magar, Khum Thompson, Matthew Waite, Evan Berger-Wolf, Tanya Lapp, Hilmar Mabee, Paula Stewart, Charles Taylor, Graham Record, Sydne |
| author_facet | East, Alyson Campolongo, Elizabeth G. Meyers, Luke Rayeed, S M Stevens, Samuel Zarubiieva, Iuliia Fluck, Isadora E. Girón, Jennifer C. Jousse, Maximiliane Lowe, Scott Perry, Kayla I Betancourt, Isabelle Charney, Noah Donoso, Evan Fox, Nathan Landsbergen, Kim J. Nepovinnykh, Ekaterina Ramirez, Michelle Singh, Parkash Thapa-Magar, Khum Thompson, Matthew Waite, Evan Berger-Wolf, Tanya Lapp, Hilmar Mabee, Paula Stewart, Charles Taylor, Graham Record, Sydne |
| contents | 1) Biological collections house millions of specimens with digital images increasingly available through open-access platforms. However, most imaging protocols were developed for human interpretation without considering automated analysis requirements. As computer vision applications revolutionize taxonomic identification and trait extraction, a critical gap exists between current digitization practices and computational analysis needs. This review provides the first comprehensive practical framework for optimizing biological specimen imaging for computer vision applications. 2) Through interdisciplinary collaboration between taxonomists, collection managers, ecologists, and computer scientists, we synthesized evidence-based recommendations addressing fundamental computer vision concepts and practical imaging considerations. We provide immediately actionable implementation guidance while identifying critical areas requiring community standards development. 3) Our framework encompasses ten interconnected considerations for optimizing image capture for computer vision-powered taxonomic identification and trait extraction. We translate these into practical implementation checklists, equipment selection guidelines, and a roadmap for community standards development including filename conventions, pixel density requirements, and cross-institutional protocols. 4)By bridging biological and computational disciplines, this approach unlocks automated analysis potential for millions of existing specimens and guides future digitization efforts toward unprecedented analytical capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens East, Alyson Campolongo, Elizabeth G. Meyers, Luke Rayeed, S M Stevens, Samuel Zarubiieva, Iuliia Fluck, Isadora E. Girón, Jennifer C. Jousse, Maximiliane Lowe, Scott Perry, Kayla I Betancourt, Isabelle Charney, Noah Donoso, Evan Fox, Nathan Landsbergen, Kim J. Nepovinnykh, Ekaterina Ramirez, Michelle Singh, Parkash Thapa-Magar, Khum Thompson, Matthew Waite, Evan Berger-Wolf, Tanya Lapp, Hilmar Mabee, Paula Stewart, Charles Taylor, Graham Record, Sydne Computer Vision and Pattern Recognition Image and Video Processing Quantitative Methods 1) Biological collections house millions of specimens with digital images increasingly available through open-access platforms. However, most imaging protocols were developed for human interpretation without considering automated analysis requirements. As computer vision applications revolutionize taxonomic identification and trait extraction, a critical gap exists between current digitization practices and computational analysis needs. This review provides the first comprehensive practical framework for optimizing biological specimen imaging for computer vision applications. 2) Through interdisciplinary collaboration between taxonomists, collection managers, ecologists, and computer scientists, we synthesized evidence-based recommendations addressing fundamental computer vision concepts and practical imaging considerations. We provide immediately actionable implementation guidance while identifying critical areas requiring community standards development. 3) Our framework encompasses ten interconnected considerations for optimizing image capture for computer vision-powered taxonomic identification and trait extraction. We translate these into practical implementation checklists, equipment selection guidelines, and a roadmap for community standards development including filename conventions, pixel density requirements, and cross-institutional protocols. 4)By bridging biological and computational disciplines, this approach unlocks automated analysis potential for millions of existing specimens and guides future digitization efforts toward unprecedented analytical capabilities. |
| title | Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens |
| topic | Computer Vision and Pattern Recognition Image and Video Processing Quantitative Methods |
| url | https://arxiv.org/abs/2505.17317 |