Non-rigid Structure from Motion (NRSfM) is a classical computer vision problem. And the main methods used to solve it are in general based on shape models or trajectory models. This paper will provide an overview over kinds of solutions proposed in these researches. It not only gives out the theoretical insights proposed by researchers in recent years, but also discusses them with their pros an...

A time-dependent trajectory algorithm is used to determine the sources of the Pacific Equatorial Undercurrent (EUC) in a global climate model with 1/4 degree (eddypermitting) resolution and forced with realistic winds. The primary sources and pathways are identified and the transformation of properties in temperature/salinity space are explored. An estimate for the quantity of recirculation, a ...

This paper introduces a method to minimize norms on nonlinear trajectory sensitivities during open-loop trajectory optimization. Specifically, we derive new parametric sensitivity terms that measure the variation in nonlinear (continuous-time) trajectories due to variations in model parameters, and hybrid sensitivities, which account for variations in trajectory caused by sudden transitions fro...

BACKGROUND Classifying medication adherence is important for efficiently targeting adherence improvement interventions. The purpose of this study was to evaluate the use of a novel method, group-based trajectory models, for classifying patients by their long-term adherence. RESEARCH DESIGN We identified patients who initiated a statin between June 1, 2006 and May 30, 2007 in prescription clai...

Efficient skill acquisition is crucial for creating versatile robots. One intuitive way to teach a robot new tricks is to demonstrate a task and enable the robot to imitate the demonstrated behavior. This approach is known as imitation learning. Classical methods of imitation learning, such as inverse reinforcement learning or behavioral cloning, suffer substantially from the correspondence pro...

This is a complementary document to the paper presented in [1], where more detailed proofs are provided for some results. The main paper addresses the problem of trajectory tracking control of autonomous rotorcraft in operation scenarios where only relative position measurements obtained from LiDAR sensors are possible. The proposed approach defines an alternative kinematic model, directly base...

This paper proposes a movement trajectory planning model, which is a maximum task achievement model in which signal-dependent noise is added to the movement command. In the proposed model, two optimization criteria are combined, maximum task achievement and minimum energy consumption. The proposed model has the feature that the end-point boundary conditions for position, velocity, and accelerat...

This paper focuses on resolving the trajectory tracking problem of two wheeled mobile robot. We begin by presenting the kinematic model of the robot which is the base of the control law then we present a PI controller and a model predictive controller to solve the problem of trajectory tracking. We performed a comparison between the performances of the classical PI controller and the predictive...

Many studies of spatiotemporal pattern discovery partition data space into disjoint cells for effective processing. However, the discovery accuracy of the space-partitioning schemes highly depends on space granularity. Moreover, it cannot describe data statistics well when data spreads over not only one but many cells. In this study, we introduce a novel approach which takes advantages of the e...

In this paper we propose a probabilistic sequential model of Human-Robot Spatial Interaction (HRSI) using a well-established Qualitative Trajectory Calculus (QTC) to encode HRSI between a human and a mobile robot in a meaningful, tractable, and systematic manner. Our key contribution is to utilise QTC as a state descriptor and model HRSI as a probabilistic sequence of such states. Apart from th...