Supervised geodesic propagation for semantic label transfer

Abstract. In this paper we propose a novel semantic label transfer method using supervised geodesic propagation (SGP). We use supervised learning to guide the seed selection and the label propagation. Given an input image, we first retrieve its similar image set from annotated databases. A Joint Boost model is learned on the similar image set of the input image. Then the recognition proposal map of the input image is inferred by this learned model. The initial distance map is defined by the proposal map: the higher probability, the smaller distance. In each iteration step of the geodesic propagation, the seed is selected as the one with the smallest distance from the undetermined superpixels. We learn a classifier as an indicator to indicate whether to propagate labels between two neighboring superpixels. The training samples of the indicator are annotated neighboring pairs from the similar image set. The geodesic distances of its neighbors are updated according to the combination of the texture and boundary features and the indication value. Experiments on three datasets show that our method outperforms the traditional learning based methods and the previous label transfer method for the semantic segmentation work

A Layered Approach for Robust Spatial Virtual Human Pose Reconstruction Using a Still Image

Pedestrian detection and human pose estimation are instructive for reconstructing a three-dimensional scenario and for robot navigation, particularly when large amounts of vision data are captured using various data-recording techniques. Using an unrestricted capture scheme, which produces occlusions or breezing, the information describing each part of a human body and the relationship between each part or even different pedestrians must be present in a still image. Using this framework, a multi-layered, spatial, virtual, human pose reconstruction framework is presented in this study to recover any deficient information in planar images. In this framework, a hierarchical parts-based deep model is used to detect body parts by using the available restricted information in a still image and is then combined with spatial Markov random fields to re-estimate the accurate joint positions in the deep network. Then, the planar estimation results are mapped onto a virtual three-dimensional space using multiple constraints to recover any deficient spatial information. The proposed approach can be viewed as a general pre-processing method to guide the generation of continuous, three-dimensional motion data. The experiment results of this study are used to describe the effectiveness and usability of the proposed approach

Arm Posture Changes and Influences on Hand Controller Interaction Evaluation in Virtual Reality

In virtual reality (VR) applications, hand-controller interaction is largely limited by the biomechanical structure of the arm and its kinematical features. Earlier research revealed that different arm postures generate distinct arm fatigue levels in mid-air operational tasks; however, how they impact interaction performance, e.g., accuracy of target grasp and manipulation, has been less investigated. To fill this gap in knowledge, we conducted an empirical experiment in which thirty participants were recruited to complete a series of target acquisition tasks in a specifically designed VR application. Results show that (1) a bent arm posture resulted in a higher interaction accuracy than a stretched arm posture; (2) a downward arm posture interacted more accurately than an upraised arm posture; since two arms are bilaterally symmetric, (3) either selected arm interacted more accurately on the corresponding side than on the opposite side; and (4) the user-preferred or dominant arm interacted more persistently than the non-dominant one, though two arms generated little difference in interaction accuracy. Implications and suggestions are discussed for designing more efficient and user-satisfying interactive spaces in VR

Arm Posture Changes and Influences on Hand Controller Interaction Evaluation in Virtual Reality

In virtual reality (VR) applications, hand-controller interaction is largely limited by the biomechanical structure of the arm and its kinematical features. Earlier research revealed that different arm postures generate distinct arm fatigue levels in mid-air operational tasks; however, how they impact interaction performance, e.g., accuracy of target grasp and manipulation, has been less investigated. To fill this gap in knowledge, we conducted an empirical experiment in which thirty participants were recruited to complete a series of target acquisition tasks in a specifically designed VR application. Results show that (1) a bent arm posture resulted in a higher interaction accuracy than a stretched arm posture; (2) a downward arm posture interacted more accurately than an upraised arm posture; since two arms are bilaterally symmetric, (3) either selected arm interacted more accurately on the corresponding side than on the opposite side; and (4) the user-preferred or dominant arm interacted more persistently than the non-dominant one, though two arms generated little difference in interaction accuracy. Implications and suggestions are discussed for designing more efficient and user-satisfying interactive spaces in VR

emergence of relativistic effect in probabilistic flooding of mobile ad hoc networks

Broadcasting is a fundamental operation in Mobile Ad hoc Networks (MANETs) whereby a source node transmits a message that is to be disseminated to all the nodes in the network. This paper shows that for several classes of MANETs with distinct topologies, the broadcasting behavior of probabilistic flooding in a network and that of blind flooding in the network possess a single mathematical form of equation under certain conditions. That is, the principle of relativity can be observed in MANETs. © 2011 Elsevier B.V. All rights reserved.Broadcasting is a fundamental operation in Mobile Ad hoc Networks (MANETs) whereby a source node transmits a message that is to be disseminated to all the nodes in the network. This paper shows that for several classes of MANETs with distinct topologies, the broadcasting behavior of probabilistic flooding in a network and that of blind flooding in the network possess a single mathematical form of equation under certain conditions. That is, the principle of relativity can be observed in MANETs. © 2011 Elsevier B.V. All rights reserved

Optimizing neighborhood projection with relaxation factor for inextensible cloth simulation

In this paper, we propose a novel method for inextensible cloth simulation. Our method introduces a neighborhood projection optimized with a relaxation factor. The neighborhood projection enforces inextensibility by modifying particle positions with a Jacobi-style iteration, leading to conservation of linear and angular quasi momenta. The relaxation factor is estimated according to the corrections and constraints, and is used to scale the corrections while keeping convergence to a smaller number of iterations. Experimental results demonstrate that our method increases the simulation efficiency, and stably handles inextensible cloth even in overconstrained situations. In addition to the simulation of hanging cloth and draping cloth, the simulated umbrella demonstrates the characters of our method for this type of objects