Abstract

In this study, a method to automatically segment plant leaves from three-dimensional (3D) images using structure from motion is proposed. First, leaves in the 3D images are roughly segmented using a region-growing method in which near points with distances less than 0.2 cm are assigned to the same group. By repeating this process, the leaves not touching each other can be segmented. Then, each segmented leaf is projected onto two-dimensional (2D) images, and the watershed algorithm is executed. This process successfully segments overlapping leaves.

© 2020 Optical Society of America

Quantification of plant morphology and leaf thickness with optical coherence tomography

Jos de Wit, Sebastian Tonn, Guido Van den Ackerveken, and Jeroen Kalkman
Appl. Opt. 59(33) 10304-10311 (2020)

Automated tree detection from 3D lidar images using image processing and machine learning

Kenta Itakura and Fumiki Hosoi
Appl. Opt. 58(14) 3807-3811 (2019)

Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models

Razieh Pourdarbani, Sajad Sabzi, Mohammad H. Rohban, Ginés García-Mateos, and Juan I. Arribas
Appl. Opt. 60(30) 9560-9569 (2021)

Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans

Zaixing Mao, Atsuya Miki, Song Mei, Ying Dong, Kazuichi Maruyama, Ryo Kawasaki, Shinichi Usui, Kenji Matsushita, Kohji Nishida, and Kinpui Chan
Biomed. Opt. Express 10(11) 5832-5851 (2019)

Automatic 3D color shape measurement system based on a stereo camera

Tzung-Han Lin
Appl. Opt. 59(7) 2086-2096 (2020)