A new approach to recover 3-D shape from a Scanning Electron Microscope (SEM) image is described. With an ideal SEM image, 3-D shape can be recovered using the Fast Marching Method (FMM) applied to the Eikonal equation. However, when the light source direction is oblique, the correct shape cannot be obtained by the usual one-pass FMM. The new approach modifies the intensities in the original SEM image using an additional SEM image of a sphere and Neural Network (NN) training. Image modification is a two degree-of-freedom (DOF) rotation. No assumption is made about the specific functional form for intensity in an SEM image. The correct 3-D shape can be obtained using the FMM and NN learning, without iteration. The approach is demonstrated through computer simulation and validated through real experiment.
The virtual scissors using virtual hands we propose enable users can cut virtual paper with their own hands. One purpose of our proposal is to facilitate implementing different tools simply by changing software parameters. Another is to make a general-purpose system with small-scale input and output devices for general applications, e.g., only using thin haptic information and force feedback. With such virtual reality (VR) scissors, we introduced feedback to cover any impressions such as interface interference during use. We evaluated whether an interaction occurred between vibration feedback and sound effects. Using this system, we found that users could manipulate virtual scissors through a data-glove similar to the use of real ones.
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