Two-stream Graph Convolutional Networks with Task-specific Loss for Dual-task Gait Analysis.

Dual-task gait systems can be utilized to assess elderly patients for cognitive decline. Although numerous research studies have been conducted to estimate cognitive scores, this field still faces two significant challenges. Firstly, it is crucial to fully utilize dual-task cost representations for diagnosis.

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping.

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.

Discrete Search Photometric Stereo for Fast and Accurate Shape Estimation.

We consider the problem of estimating surface normals of a scene with spatially varying, general bidirectional reflectance distribution functions (BRDFs) observed by a static camera under varying distant illuminations. Unlike previous approaches that rely on continuous optimization of surface normals, we cast the problem as a discrete search problem over a set of finely discretized surface normals. In this setting, we show that the expensive processes can be precomputed in a scene-independent manner, resulting in accelerated inference.

Exploring the mechanical and morphological rationality of tree branch structure based on 3D point cloud analysis and the finite element method.

Trees are thought to have acquired a mechanically optimized shape through evolution, but a scientific methodology to investigate the mechanical rationality of tree morphology remains to be established. The aim of this study was to develop a new method for 3D reconstruction of actual tree shape and to establish a theoretical formulation for elucidating the structure and function of tree branches. We obtained 3D point cloud data of tree shape of Japanese zelkova (Zelkova serrata) and Japanese larch (Larix kaempferi) using the NavVis Lidar scanner, then applied a cylinder structure extraction from point cloud data with error estimation.

Early Detection of Low Cognitive Scores from Dual-task Performance Data Using a Spatio-temporal Graph Convolutional Neural Network.

Detecting low cognitive scores at an early stage is important for delaying the progress of dementia. Investigations of early-stage detection have employed automatic assessment using dual-task (i.e.