Moving beyond the box: automating the digitisation of insect collections
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Presented at the 2014 TDWG annual conference, Elmia Congress Centre in Jönköping, Sweden, October 27-31, 2014.
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Moving beyond the box: automating the digitisation of insect collections
- 1. Moving beyond the box:Moving beyond the box: automating the digitisation ofautomating the digitisation of insect collectionsinsect collections
- 2. Blagoderov et al (2012) No specimen left behind: industrial scale digitization of natural history collections. ZooKeys 209: 131–146, doi: 10.3897/zookeys.209.3178
- 3. Drawer level imaging is (mostly) a solved problem 1. Place drawer 2. Scan 3. Stitch
- 4. Result: High resolution composite image Drawer level imaging is (mostly) a solved problem • Fast (5 mins per drawer) • High resolution (circa 500MB per image) • No specimen handling
- 5. But, two key problems remain… Synchronisation Label data Keeping the physical & digital copies in sync Capturing data from multiple pinned labels
- 6. • Don’t worry about it, re-image as required • Lock down the drawers • Crop-out each specimen image – Automate the cropping process – Link each specimen to its digital image – Make it easy to collect label data Approaches to the synchronisation problem The only practical solution, but a new rate limiting step
- 7. Annotation software, NHM working with SmartDrive: Initial supporting software • No automated cropping • Manually link images & specimens • Poor UX/UI • Closed source and proprietary • Not cross-platform (Windows only) A good first step to understanding the problems
- 8. Automating specimen segmentation Starting image Auto-segment Mark errors Correct Work with Pieter Holtzhausen and Stéfan van der Walt (Stellenbosch University) Software: Inselect, written in Python
- 9. Original Primary segmentation (contrast based) Segmentation methods Secondary (seed growing)
- 10. Inselect http://naturalhistorymuseum.github.io/inselect/ • Currently pre-release (alpha) • Automatically detects specimens • Creates bounding boxes for cropping and exporting images • Rapid annotation interface • Persistent settings & keyboard shortcuts • Data export in JSON format • Open source & modular • Python based (OpenCV, scikit- image libraries) • Windows, OSX & Linux Automated recognition, cropping and annotation of specimens
- 11. Whole Drawer image Inselect: segmentation of specimen images Auto-segmented images in sidebar Whole Drawer image
- 12. Easy to spot & correct errors Secondary re-segmentation (seed growing) Whole Drawer image
- 13. Whole Drawer image Easy to spot & correct errors Secondary re-segmentation (seed growing)
- 14. Works on slides & pinned insects Whole Drawer image Also testing mineral & fossil samples
- 15. Planned UX / UI Enhancements Whole Drawer image Unit tray recognition Multi- specimen annotation Plug in controlled vocabulary services
- 16. 1D & 2D barcode recognition Whole Drawer image • Recognition and reading of 1D & 2D matrix barcodes from images • Different physical requirements (smallest 6x6mm matrix, readable via handheld scanners) • Testing open source & commercial libraries at different scan resolutions Initial results • Commercial solutions outperform open source • Max. read success 94% • Idiosyncratic results (different results on different OS) • Testing continues…
- 17. The Holy Grail: label imaging & text recognition Whole Drawer image Chauliodes pectinicornis DorsalCaudalFrontal Reconstructed labels
- 18. The Holy Grail: label imaging & text recognition Whole Drawer image Agulla astuta DorsalCaudalLateral Reconstructed labels
- 19. Approaches to label imaging pinned specimens Whole Drawer image Could be incorporated as part of the barcode dispensing process 1. Barcode dispenser & scanner (two sides barcode labels) 2. Freshly pinned barcode label 3. Other collection labels 4. Multiple label imaging cameras 5. Assemble labels from composite images
- 20. Acknowledgements Whole Drawer image Segmentation algorithm & app. development Stefan van der Walt and Pieter Holtzhausen Application development Alice Heaton Barcode recognition & testing Lawrence Hudson Analysis & testing Laurence Livermore, Vladimir Blagoderov and Ben Price Initial specification and funding Vince Smith
Notes de l'éditeur
- Big digitisation programmes. Need high throughput approaches.
- Big digitisation programme Project history: Inselect was originally conceived as an answer to whole-drawer imaging hardware setups, such as Dscan, SatScan and InvertNet, which are in use at different natural history collections around the world. The systems are typically used to industrially digitise insect collections but can also be used to digitise slide collections.
- In early 2014 we started working with research teams in higher education to develop prototypes for automated drawer segmentation. The Inselect prototype was developed in collaboration between the NHM, London and Stellenbosch University, South Africa. The initial collaboration was instigated by Ben Price and the following people: Stéfan van der Walt, Pieter Holtzhausen, Vladimir Blagoderov and Laurence Livermore The first prototype developed by Stéfan and Pieter was Python-based and had the following fucntionality: Downscale high resolution TIFF images for processing to JPEG – easier to run segmentation algorithm Automatically detect specimens Create rectangular bounding boxes around specimens Batch crop specimen images
- One of the rate-limiting steps for these whole drawing imaging systems is annotation and post-processing. Typically collections want to record metadata about individual specimens which involves cropping specimens and entering metadata such as taxonomic name, location in collection etc. Most software currently in use has limitations: No automation – all specimens have to be manually selected by a human operator Poor user interface and user experience – most software has a very basic user interface and provides a poor user experience Closed source & proprietary – making it costly or impossible for museums to collaborate, build upon and share software Not cross-platform – limiting annotation and post-processing to Windows users only
- What I want to do for the remainder of this talk is focus on elements of this programme and pick out some of the key elements which are of broader interest, in terms of …
- Contrast based is the simple approach (quick and simple – less computationally expensive)– When you resegment you use seed growing. (More time consuming but better fidelity. It also uses a human to mark a startting centroid
- The sidebar allows very quick reviewing of segmentation for false positives and incorrectly segmentated objects (e.g. multiple objects together)
- The sidebar allows very quick reviewing of segmentation for false positives and incorrectly segmentated objects (e.g. multiple objects together)
- The sidebar allows very quick reviewing of segmentation for false positives and incorrectly segmentated objects (e.g. multiple objects together)
- The sidebar allows very quick reviewing of segmentation for false positives and incorrectly segmentated objects (e.g. multiple objects together) (Fishfly: Megaloptera)
- The sidebar allows very quick reviewing of segmentation for false positives and incorrectly segmentated objects (e.g. multiple objects together) Lace wing Neuroptera
- The sidebar allows very quick reviewing of segmentation for false positives and incorrectly segmentated objects (e.g. multiple objects together)
- The sidebar allows very quick reviewing of segmentation for false positives and incorrectly segmentated objects (e.g. multiple objects together)