The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform

Masatsugu Kidode - One of the best experts on this subject based on the ideXlab platform.

  • ICCV - Complex volume and pose tracking with probabilistic dynamical models and visual hull constraints
    2009 IEEE 12th International Conference on Computer Vision, 2009
    Co-Authors: Norimichi Ukita, Michiro Hirai, Masatsugu Kidode
    Abstract:

    We propose a method for estimating the pose of a human body using its approximate 3D volume (visual hull) obtained in real time from synchronized videos. Our method can cope with loose-Fitting Clothing, which hides the human body and produces non-rigid motions and critical reconstruction errors, as well as tight-Fitting Clothing. To follow the shape variations robustly against erratic motions and the ambiguity between a reconstructed body shape and its pose, the probabilistic dynamical model of human volumes is learned from training temporal volumes refined by error correction. The dynamical model of a body pose (joint angles) is also learned with its corresponding volume. By comparing the volume model with an input visual hull and regressing its pose from the pose model, pose estimation can be realized. In our method, this is improved by double volume comparison: 1) comparison in a low-dimensional latent space with probabilistic volume models and 2) comparison in an observation volume space using geometric constrains between a real volume and a visual hull. Comparative experiments demonstrate the effectiveness of our method faster than existing methods.

  • Complex volume and pose tracking with probabilistic dynamical models and visual hull constraints
    2009 IEEE 12th International Conference on Computer Vision, 2009
    Co-Authors: Norimichi Ukita, Michiro Hirai, Masatsugu Kidode
    Abstract:

    We propose a method for estimating the pose of a human body using its approximate 3D volume (visual hull) obtained in real time from synchronized videos. Our method can cope with loose-Fitting Clothing, which hides the human body and produces non-rigid motions and critical reconstruction errors, as well as tight-Fitting Clothing. To follow the shape variations robustly against erratic motions and the ambiguity between a reconstructed body shape and its pose, the probabilistic dynamical model of human volumes is learned from training temporal volumes refined by error correction. The dynamical model of a body pose (joint angles) is also learned with its corresponding volume. By comparing the volume model with an input visual hull and regressing its pose from the pose model, pose estimation can be realized. In our method, this is improved by double volume comparison: 1) comparison in a low-dimensional latent space with probabilistic volume models and 2) comparison in an observation volume space using geometric constrains between a real volume and a visual hull. Comparative experiments demonstrate the effectiveness of our method faster than existing methods.

  • ECCV (3) - Real-Time Shape Analysis of a Human Body in Clothing Using Time-Series Part-Labeled Volumes
    Lecture Notes in Computer Science, 2008
    Co-Authors: Norimichi Ukita, Ryosuke Tsuji, Masatsugu Kidode
    Abstract:

    We propose a real-time method for simultaneously refining the reconstructed volume of a human body with loose-Fitting Clothing and identifying body-parts in it. Time-series volumes, which are acquired by a slow but sophisticated 3D reconstruction algorithm, with body-part labels are obtained offline. The time-series sample volumes are represented by trajectories in the eigenspaces using PCA. An input visual hull reconstructed online is projected into the eigenspace and compared with the trajectories in order to find similar high-precision samples with body-part labels. The hierarchical search taking into account 3D reconstruction errors can achieve robust and fast matching. Experimental results demonstrate that our method can refine the input visual hull including loose-Fitting Clothing and identify its body-parts in real time.

George Havenith - One of the best experts on this subject based on the ideXlab platform.

  • Sweat-induced skin wetness perception can be significantly manipulated independently of the level of physical skin wetness [Abstract]
    The FASEB Journal, 2015
    Co-Authors: Davide Filingeri, Damien Fournet, Simon Hodder, George Havenith
    Abstract:

    Skin wetness perception is driven by inputs from cold and mechano sensitive skin afferents. We hypothesized that in the absence of skin cooling, sweat induced wetness perception can be manipulated by altering the mechanical interaction between skin, sweat and Clothing. Ten males (22±2years) performed an incremental walking protocol (5Km/h; gradient: 2 to 16%) during two trials designed to produce the same level of physical skin wetness but to induce lower (TIGHT-FIT) and higher (LOOSE-FIT) wetness perception. In the TIGHT-FIT, a tight Fitting Clothing ensemble was worn to reduce the mechanical interaction between skin, sweat and Clothing. In the LOOSE-FIT, a loose Fitting ensemble augmented this interaction. To limit sweat evaporation and skin cooling, a vapour impermeable ensemble was also worn during the trials. Heart rate, rectal temperature, mean skin temperature, skin conductance (SC), whole body skin wetness (wbody) and wetness perception were recorded. Exercise induced sweat production and physical skin wetness increased significantly (SC: 3.1±0.3 to 18.8±1.3µS, p 0.05). However, the reduced mechanical interaction generated by the TIGHT-FIT ensemble lowered significantly wetness perception (p

  • Tactile cues significantly modulate the perception of sweat-induced skin wetness independently of the level of physical skin wetness.
    Journal of Neurophysiology, 2015
    Co-Authors: Davide Filingeri, Damien Fournet, Simon Hodder, George Havenith
    Abstract:

    Humans sense the wetness of a wet surface though the somatosensory integration of thermal and tactile inputs generated by the interaction between skin and moisture. However, little is known on how wetness is sensed when moisture is produced via sweating. We tested the hypothesis that, in the absence of skin cooling, intermittent tactile cues, as coded by low-threshold skin mechanoreceptors, modulate the perception of sweat-induced skin wetness, independently of the level of physical wetness. Ten males (22 yr) performed an incremental exercise protocol during 2 trials designed to induce the same physical skin wetness but to induce lower (TIGHT-FIT) and higher (LOOSE-FIT) wetness perception. In the TIGHT-FIT, a tight Fitting Clothing ensemble limited intermittent skin-sweat-Clothing tactile interactions. In the LOOSE-FIT, a loose Fitting ensemble allowed free skin-sweat-Clothing interactions. Heart rate, core and skin temperature, skin conductance (GSC), physical (wbody) and perceived skin wetness were recorded. Exercise-induced sweat production and physical wetness increased significantly (GSC: 3.1 µS, SD 0.3 to 18.8 µS, SD 1.3, p

Norimichi Ukita - One of the best experts on this subject based on the ideXlab platform.

  • ICCV - Complex volume and pose tracking with probabilistic dynamical models and visual hull constraints
    2009 IEEE 12th International Conference on Computer Vision, 2009
    Co-Authors: Norimichi Ukita, Michiro Hirai, Masatsugu Kidode
    Abstract:

    We propose a method for estimating the pose of a human body using its approximate 3D volume (visual hull) obtained in real time from synchronized videos. Our method can cope with loose-Fitting Clothing, which hides the human body and produces non-rigid motions and critical reconstruction errors, as well as tight-Fitting Clothing. To follow the shape variations robustly against erratic motions and the ambiguity between a reconstructed body shape and its pose, the probabilistic dynamical model of human volumes is learned from training temporal volumes refined by error correction. The dynamical model of a body pose (joint angles) is also learned with its corresponding volume. By comparing the volume model with an input visual hull and regressing its pose from the pose model, pose estimation can be realized. In our method, this is improved by double volume comparison: 1) comparison in a low-dimensional latent space with probabilistic volume models and 2) comparison in an observation volume space using geometric constrains between a real volume and a visual hull. Comparative experiments demonstrate the effectiveness of our method faster than existing methods.

  • Complex volume and pose tracking with probabilistic dynamical models and visual hull constraints
    2009 IEEE 12th International Conference on Computer Vision, 2009
    Co-Authors: Norimichi Ukita, Michiro Hirai, Masatsugu Kidode
    Abstract:

    We propose a method for estimating the pose of a human body using its approximate 3D volume (visual hull) obtained in real time from synchronized videos. Our method can cope with loose-Fitting Clothing, which hides the human body and produces non-rigid motions and critical reconstruction errors, as well as tight-Fitting Clothing. To follow the shape variations robustly against erratic motions and the ambiguity between a reconstructed body shape and its pose, the probabilistic dynamical model of human volumes is learned from training temporal volumes refined by error correction. The dynamical model of a body pose (joint angles) is also learned with its corresponding volume. By comparing the volume model with an input visual hull and regressing its pose from the pose model, pose estimation can be realized. In our method, this is improved by double volume comparison: 1) comparison in a low-dimensional latent space with probabilistic volume models and 2) comparison in an observation volume space using geometric constrains between a real volume and a visual hull. Comparative experiments demonstrate the effectiveness of our method faster than existing methods.

  • ECCV (3) - Real-Time Shape Analysis of a Human Body in Clothing Using Time-Series Part-Labeled Volumes
    Lecture Notes in Computer Science, 2008
    Co-Authors: Norimichi Ukita, Ryosuke Tsuji, Masatsugu Kidode
    Abstract:

    We propose a real-time method for simultaneously refining the reconstructed volume of a human body with loose-Fitting Clothing and identifying body-parts in it. Time-series volumes, which are acquired by a slow but sophisticated 3D reconstruction algorithm, with body-part labels are obtained offline. The time-series sample volumes are represented by trajectories in the eigenspaces using PCA. An input visual hull reconstructed online is projected into the eigenspace and compared with the trajectories in order to find similar high-precision samples with body-part labels. The hierarchical search taking into account 3D reconstruction errors can achieve robust and fast matching. Experimental results demonstrate that our method can refine the input visual hull including loose-Fitting Clothing and identify its body-parts in real time.

Davide Filingeri - One of the best experts on this subject based on the ideXlab platform.

  • Sweat-induced skin wetness perception can be significantly manipulated independently of the level of physical skin wetness [Abstract]
    The FASEB Journal, 2015
    Co-Authors: Davide Filingeri, Damien Fournet, Simon Hodder, George Havenith
    Abstract:

    Skin wetness perception is driven by inputs from cold and mechano sensitive skin afferents. We hypothesized that in the absence of skin cooling, sweat induced wetness perception can be manipulated by altering the mechanical interaction between skin, sweat and Clothing. Ten males (22±2years) performed an incremental walking protocol (5Km/h; gradient: 2 to 16%) during two trials designed to produce the same level of physical skin wetness but to induce lower (TIGHT-FIT) and higher (LOOSE-FIT) wetness perception. In the TIGHT-FIT, a tight Fitting Clothing ensemble was worn to reduce the mechanical interaction between skin, sweat and Clothing. In the LOOSE-FIT, a loose Fitting ensemble augmented this interaction. To limit sweat evaporation and skin cooling, a vapour impermeable ensemble was also worn during the trials. Heart rate, rectal temperature, mean skin temperature, skin conductance (SC), whole body skin wetness (wbody) and wetness perception were recorded. Exercise induced sweat production and physical skin wetness increased significantly (SC: 3.1±0.3 to 18.8±1.3µS, p 0.05). However, the reduced mechanical interaction generated by the TIGHT-FIT ensemble lowered significantly wetness perception (p

  • Tactile cues significantly modulate the perception of sweat-induced skin wetness independently of the level of physical skin wetness.
    Journal of Neurophysiology, 2015
    Co-Authors: Davide Filingeri, Damien Fournet, Simon Hodder, George Havenith
    Abstract:

    Humans sense the wetness of a wet surface though the somatosensory integration of thermal and tactile inputs generated by the interaction between skin and moisture. However, little is known on how wetness is sensed when moisture is produced via sweating. We tested the hypothesis that, in the absence of skin cooling, intermittent tactile cues, as coded by low-threshold skin mechanoreceptors, modulate the perception of sweat-induced skin wetness, independently of the level of physical wetness. Ten males (22 yr) performed an incremental exercise protocol during 2 trials designed to induce the same physical skin wetness but to induce lower (TIGHT-FIT) and higher (LOOSE-FIT) wetness perception. In the TIGHT-FIT, a tight Fitting Clothing ensemble limited intermittent skin-sweat-Clothing tactile interactions. In the LOOSE-FIT, a loose Fitting ensemble allowed free skin-sweat-Clothing interactions. Heart rate, core and skin temperature, skin conductance (GSC), physical (wbody) and perceived skin wetness were recorded. Exercise-induced sweat production and physical wetness increased significantly (GSC: 3.1 µS, SD 0.3 to 18.8 µS, SD 1.3, p

Damien Fournet - One of the best experts on this subject based on the ideXlab platform.

  • Sweat-induced skin wetness perception can be significantly manipulated independently of the level of physical skin wetness [Abstract]
    The FASEB Journal, 2015
    Co-Authors: Davide Filingeri, Damien Fournet, Simon Hodder, George Havenith
    Abstract:

    Skin wetness perception is driven by inputs from cold and mechano sensitive skin afferents. We hypothesized that in the absence of skin cooling, sweat induced wetness perception can be manipulated by altering the mechanical interaction between skin, sweat and Clothing. Ten males (22±2years) performed an incremental walking protocol (5Km/h; gradient: 2 to 16%) during two trials designed to produce the same level of physical skin wetness but to induce lower (TIGHT-FIT) and higher (LOOSE-FIT) wetness perception. In the TIGHT-FIT, a tight Fitting Clothing ensemble was worn to reduce the mechanical interaction between skin, sweat and Clothing. In the LOOSE-FIT, a loose Fitting ensemble augmented this interaction. To limit sweat evaporation and skin cooling, a vapour impermeable ensemble was also worn during the trials. Heart rate, rectal temperature, mean skin temperature, skin conductance (SC), whole body skin wetness (wbody) and wetness perception were recorded. Exercise induced sweat production and physical skin wetness increased significantly (SC: 3.1±0.3 to 18.8±1.3µS, p 0.05). However, the reduced mechanical interaction generated by the TIGHT-FIT ensemble lowered significantly wetness perception (p

  • Tactile cues significantly modulate the perception of sweat-induced skin wetness independently of the level of physical skin wetness.
    Journal of Neurophysiology, 2015
    Co-Authors: Davide Filingeri, Damien Fournet, Simon Hodder, George Havenith
    Abstract:

    Humans sense the wetness of a wet surface though the somatosensory integration of thermal and tactile inputs generated by the interaction between skin and moisture. However, little is known on how wetness is sensed when moisture is produced via sweating. We tested the hypothesis that, in the absence of skin cooling, intermittent tactile cues, as coded by low-threshold skin mechanoreceptors, modulate the perception of sweat-induced skin wetness, independently of the level of physical wetness. Ten males (22 yr) performed an incremental exercise protocol during 2 trials designed to induce the same physical skin wetness but to induce lower (TIGHT-FIT) and higher (LOOSE-FIT) wetness perception. In the TIGHT-FIT, a tight Fitting Clothing ensemble limited intermittent skin-sweat-Clothing tactile interactions. In the LOOSE-FIT, a loose Fitting ensemble allowed free skin-sweat-Clothing interactions. Heart rate, core and skin temperature, skin conductance (GSC), physical (wbody) and perceived skin wetness were recorded. Exercise-induced sweat production and physical wetness increased significantly (GSC: 3.1 µS, SD 0.3 to 18.8 µS, SD 1.3, p