Computer vision cracks the leaf code
Abstract
The botanical value of angiosperm leaf shape and venation (“leaf architecture”) is well known, but the astounding complexity and variation of leaves have thwarted efforts to access this underused resource. This challenge is central for paleobotany because most angiosperm fossils are isolated, unidentified leaves. We here demonstrate that a computer vision algorithm trained on several thousand images of diverse cleared leaves successfully learns leaf-architectural features, then categorizes novel specimens into natural botanical groups above the species level. The system also produces heat maps to display the locations of numerous novel, informative leaf characters in a visually intuitive way. With assistance from computer vision, the systematic and paleobotanical value of leaves is ready to increase significantly.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 07, 2016
- Source ID
- 10.1073/pnas.1524473113
Entities
People
- Peter Wilf
- Scott L. Wing
- Sharat Chikkerur
- Shengping Zhang
- Stefan A. Little
- Thomas Serre
Organizations
- Brown University
- David and Lucile Packard Foundation
- Harbin Institute of Technology
- Microsoft
- National Museum of Natural History
- National Natural Science Foundation of China
- National Science Foundation
- Office of Naval Research
- Pennsylvania State University
- University of Paris-Sud