FEM modelling of bone structures in woodpeckers combined with high speed video of pecking motion etc
headgear? shock absorbing structures? low mass hammering penetrators?
Lizhen Wang1,2, Jason Tak-Man Cheung3, Fang Pu1, Deyu Li1, Ming Zhang2*, Yubo Fan1* 1 Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China, 2 Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 3 Li Ning Sports Science Research Center, Beijing, People's Republic of China Head injury is a leading cause of morbidity and death in both industrialized and developing countries.
"...the woodpecker does not experience any head injury at the high speed of 6-7 m/s with a deceleration of 1000 g when it drums a tree trunk. It is still not known how woodpeckers protect their brain from impact injury...."
A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space