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Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images
2018
Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Object detection using a boosted cascade of weak classifiers is a principle that has been used in a variety of applications, ranging from pedestrian detection to fruit counting in orchards, and this with a high average precision. In this work we prove that using both the boosted cascade approach suggest by Viola & Jones and the adapted approach based on integral or aggregate channels by Dollár yield promising results on coconut tree detection in aerial images. However with the rise of robust
doi:10.5220/0006571902300241
dblp:conf/visapp/PuttemansBG18
fatcat:qclu4zi4sfbanmvom3pqypujqq