Tracking of plant organs over spatio-temporal sequence of point cloud data is one of the demanding tasks of agricultural robotics for automated plant monitoring and growth analysis. Due to the complex geometry of plants, it is extremely difficult to identify and track the individual organs in different growth stages of plants. In this paper, we present an approach to perform correspondence estimation of different plant organs over a series of spatio-temporal data. The approach is based on two stages. In the first stage we develop a robust skeleton extraction method from unstructured plant point cloud data by adopting Hausdorff distance metric and modified breadth first search algorithm. The proposed skeletonization method is shown to be performing better than state-of-the-art, especially in handling very thin and delicate branches. We also address an overlooked problem of connecting skeleton points in the form of a graph, and demonstrate that different types of plant phenotype parameters can be obtained in a fully automatic manner from the skeleton graph. In the second stage, we exploit the skeleton graphs in developing an algorithm to perform correspondence estimation among the skeleton nodes using a cosine similarity based approach. We demonstrate the effectiveness of the proposed skeletonization technique in tracking different organs of the plant by finding good quality correspondences. Experiments are performed on three datasets on real and synthetic sequence of spatio-temporal plant point cloud data to demonstrate the effectiveness of the proposed method.

In this paper, we introduce a new method for the space-time 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.

Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is strongly associated with the activity of the prefrontal cortex. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks. Here, we propose a computational model of the prefrontal cortex within the framework of the cell assembly theory. In that framework, ‘a chunk of information’ refers to an associative memory and consists of an ensemble of neurons which activates coherently due to their strong interconnections. Our model consists of a recurrent network of cells whose dynamics results from the interplay between the membrane potential and the theta local field potential.