ROOT IDENTIFICATION WITH MULTIPLE INSTANCE LEARNING ACCEPTED TO MACHINE VISION AND APPLICATIONS!

Congratulations to our labmates Guohao Yu, Alina Zare and Hudanyun Sheng, as well as collaborators, Roser Matamala, Joel Reyes-Cabrera, Felix Fritschi and Thomas Juenger! Their paper, “Root Identification in Minirhizotron Imagery with Multiple Instance Learning”, was recently accepted to Machine Vision and Applications!

Their paper explores the use of multiple instance learning to segment minirhizotron images of plant roots.  Contrary to traditional methods which require precisely-labeled groundtruth, their work utilizes weak annotations, saving both time and burden.  Check out the pre-print here!