2 research outputs found

    Improved Feature Detection over Large Force Ranges Using History Dependent Transfer Functions

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    In this paper we present a history dependent transfer function (HDTF) as a possible approach to enable improved haptic feature detection in high dynamic range (HDR) volume data. The HDTF is a multi-dimensional transfer function that uses the recent force history as a selection criterion to switch between transfer functions, thereby adapting to the explored force range. The HDTF has been evaluated using artificial test data and in a realistic application example, with the HDTF applied to haptic protein-ligand docking. Biochemistry experts performed docking tests, and expressed that the HDTF delivers the expected feedback across a large force magnitude range, conveying both weak attractive and strong repulsive protein-ligand interaction forces. Feature detection tests have been performed with positive results, indicating that the HDTF improves the ability of feature detection in HDR volume data as compared to a static transfer function covering the same range.©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Petter Bivall Persson, Gunnar E. Höst, Matthew D. Cooper, Lena A. E. Tibell and Anders Ynnerman, Improved Feature Detection over Large Force Ranges Using History Dependent Transfer Functions, 2009, Third Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems, WorldHaptics 2009, 476-481. http://dx.doi.org/10.1109/WHC.2009.4810843VisMolL

    Improved feature detection over large force ranges using history dependent transfer functions

    No full text
    N.B.: When citing this work, cite the original article. ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
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