Multi-scale Space-time Registration of Growing Plants

Abstract

In this paper, we introduce a new method for the spacetime registration of a growing plant that is based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into parts that we call branches. Then these branches are further divided into smaller segments that possess a simple geometric structure. These segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP.We applied our method to various types of plants, including arabidopsis, tomato plant and maize. We established three different metrics for 3D point-wise shape correspondence to test the accuracy, continuity, and cycle consistency of the mapping. We then compared our method with the state-of-the-art. Our results show that our approach achieves better or similar results with a shorter running time.

A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants

“…The intricacies associated with parameter calibration in these segmentation approaches occasionally circumscribe their applicability. In a bid to surmount these challenges, Pan et al [50] and Chebrolu et al [51] innovatively employed spatiotemporal alignment. This strategy facilitated the linkage of plant organs during their growth phases, thereby enabling meticulous phenotypic growth surveillance.…”

PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

“…Meanwhile, their applications are sometimes restricted by the complicated parameter tuning in segmentation. Pan et al [52] and Chebrolu et al [53] used spatiotemporal matching to associate the organs in growth for phenotypic growth tracking.…”