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    Dynamic 3D hand gesture recognition by learning weighted depth motion maps

    , Article IEEE Transactions on Circuits and Systems for Video Technology ; 12 July , 2018 , Pages: 1-1 ; 15582205 (Electronic ISSN) Azad, R ; Asadi Aghbolaghi, M ; Kasaei, S ; Escalera, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal... 

    Dynamic 3D hand gesture recognition by learning weighted depth motion maps

    , Article IEEE Transactions on Circuits and Systems for Video Technology ; Volume 29, Issue 6 , 2019 , Pages 1729-1740 ; 10518215 (ISSN) Azad, R ; Asadi Aghbolaghi, M ; Kasaei, S ; Escalera, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    Hand gesture recognition (HGR) from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of the human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of keyframes of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatiotemporal... 

    Spatiotemporal description of BTEX volatile organic compounds in a middle eastern megacity: tehran study of exposure prediction for environmental health research (Tehran SEPEHR)

    , Article Environmental Pollution ; Volume 226 , 2017 , Pages 219-229 ; 02697491 (ISSN) Amini, H ; Hosseini, V ; Schindler, C ; Hassankhany, H ; Yunesian, M ; Henderson, S. B ; Künzli, N ; Sharif University of Technology
    Abstract
    The spatiotemporal variability of ambient volatile organic compounds (VOCs) in Tehran, Iran, is not well understood. Here we present the design, methods, and results of the Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR) on ambient concentrations of benzene, toluene, ethylbenzene, p-xylene, m-xylene, o-xylene (BTEX), and total BTEX. To date, this is the largest study of its kind in a low- and middle-income country and one of the largest globally. We measured BTEX concentrations at five reference sites and 174 distributed sites identified by a cluster analytic method. Samples were taken over 25 consecutive 2-weeks at five reference sites (to be used for...