title: An edge-detection approach to investigating pigeon navigation.
abstract: This study brings together work in pattern recognition and animal behaviour. By applying algorithms in pattern recognition, we examined how visual landscape information influences pigeons' homing behaviour. We used an automated procedure (Canny edge detector) to extract edges from an aerial image of the experimental terrain. Analysis of pigeons' homing routes recorded using global positioning system (GPS) trackers showed that the chosen homing paths, as well as changes in the birds' navigational states, tended to coincide with these edges. This study demonstrates that some edge-containing land features attract homing pigeons and trigger changes in their navigational states.
authors.affiliation: Machine Learning Research Group, University of Oxford, UK. kamkeung@robots.ox.ac.uk
authors.forename: Kam-Keung
authors.initials: KK
authors.lastname: Lau
authors.forename: Stephen
authors.initials: S
authors.lastname: Roberts
authors.forename: Dora
authors.initials: D
authors.lastname: Biro
authors.forename: Robin
authors.initials: R
authors.lastname: Freeman
authors.forename: Jessica
authors.initials: J
authors.lastname: Meade
authors.forename: Tim
authors.initials: T
authors.lastname: Guilford
mesh_terms: D000465:Algorithms; D000818:Animals; D010856:Columbidae / Q000502:physiology*; D005426:Flight, Animal; D006702:Homing Behavior; D007091:Image Processing, Computer-Assisted / Q000379:methods; D009949:Orientation; D010364:Pattern Recognition, Visual