This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.

Objective: The dual-task paradigm is a promising procedure for estimating cognitive status, and may also be collaterally used to reduce cognitive decline and prevent dementia. In this research, we use the Mini-Mental State Exam (MMSE) to assess cognitive status in the elderly as a reference, and investigate a potential of using machine learning for early detecting cognitive impairment in elderly. Methods: Although many studies have suggested that dual-task performance, in which participants perform a cognitive task while walking, is associated with cognition, they only considered the correlation between cognitive parameters and simple gait feature, such as gait speed, through statistical analysis. We instead use a Kinect sensor to capture participants’ whole-body movements and extract a rich gait feature that has the ability to exhibit different tendencies of movements between healthy and cognitive-impaired elderlies. Results: In our experiments, a classifier based on the dual-task gait feature achieved higher performance than the one based on single-task feature; the performance of the rich gait feature was better than that of a simple one; and an optimal detection performance was achieved with a MMSE cut-off score of 25. Conclusion: We positively validated that the proposed method could early detect elderly with lower MMSE scores based on dual-task gait feature with a promising performance. Significance: Our approach can support an early and automated diagnosis of cognitive impairment.

This paper describes a method for inferring three-dimensional (3D) plant branch structures that are hidden under leaves from multi-view observations. Unlike previous geometric approaches that heavily rely on the visibility of the branches or use parametric branching models, our method makes statistical inferences of branch structures in a probabilistic framework. By inferring the probability of branch existence using a Bayesian extension of image-to-image translation applied to each of multi-view images, our method generates a probabilistic plant 3D model, which represents the 3D branching pattern that cannot be directly observed. Experiments demonstrate the usefulness of the proposed approach in generating convincing branch structures in comparison to prior approaches.

This paper proposes a teleoperation interface by which an operator can control a robot from freely configured viewpoints using realistic images of the physical world. The viewpoints generated by the proposed interface provide human operators with intuitive control using a head-mounted display and head tracker, and assist them to grasp the environment surrounding the robot. A state-of-the-art free-viewpoint image generation technique is employed to generate the scene presented to the operator. In addition, an augmented reality technique is used to superimpose a 3D model of the robot onto the generated scenes. Through evaluations under virtual and physical environments, we confirmed that the proposed interface improves the accuracy of teleoperation.

This article describes a Mixed-Reality (MR) application that superimposes lost buildings of a historical site onto real scenes virtualized using spherical aerial images. The proposed application is set at a UNESCO World Heritage site in Japan, and is based on a novel framework that supports the photorealistic superimposition of virtual objects onto virtualized real scenes. The proposed framework utilizes Image-Based Rendering (IBR), which enables users to freely change their viewpoint in a real-world virtualization constructed using precaptured images. This framework combines the offline rendering of virtual objects and IBR to take advantage of the higher quality of offline rendering without the additional computational cost of online processing; that is, it incurs only the cost of online lightweight IBR, which is simplified through the pregeneration of structured viewpoints (e.g., at grid points).