Stereo acuity, operating time, acceptance, and visual fatigue scores were recorded for clinical evaluation. Experiment 2 was an observational, analytic, cross-sectional study involving 135 healthy participants, each of whom was asked to complete measurements on both the autostereoscopic and conventional devices in random order. Experiment 1 was performed under the same environmental conditions to compare luminance, crosstalk, and spectrum between the autostereoscopic dynamic stereopsis assessment device and the conventional dynamic random-dot stereopsis measuring instrument. The autostereoscopic dynamic stereopsis assessment device was based on a directional backlight technology. This study evaluated the optical parameters and clinical performance of the autostereoscopic display system for clinical dynamic stereopsis assessment. A novel clinical dynamic stereopsis assessment method was created based on an autostereoscopic display system that did not require additional auxiliary glasses. cunea can provide important data support for sample augmentation and species identification of moth pests.Īlthough it is recognized that dynamic stereopsis is vital in daily life, there is still room for improvement in assessment methods. The results showed that the relative error of the wing angle was between 0.32% and 3.03%, the root mean square error was 1.9363, and the average relative error of the torso was 2.77%. Finally, the 3D posture information of the wings and abdomen obtained by this method was compared with that obtained by Metrology-grade 3D scanner measurement. The 3D posture information of the torso was obtained by edge extraction and curve fitting. The 3D pose information of the wings was extracted using boundary tracking, edge fitting, precise positioning and matching, and calculation. Four images of every collected sample of H. In this study, an algorithm of the 3D posture information extraction method for Hyphantria cunea (Drury) based on monocular vision is proposed. Obtaining three-dimensional (3D) posture information of pests can provide information for 3D model deformation and generate training samples for deep learning models. (Due to the column interleaving, the dots in panels C and D are now rectangular, but the aspect ratio could be changed easily by increasing the height of the dot to produce a square dot on the screen.)Ĭurrently, the robustness of pest recognition algorithms based on sample augmentation with two-dimensional images is negatively affected by moth pests with different postures. (D) Monochromatic version of panel C where the target can be discriminated from the background. Note that the target dots are wider than the background dots.

Left and right images are drawn interleaved (the red filter should be placed in front of the right eye).

(B) Sketched version of the stereogram representing the 3-D percept. In this example we do not need antialiasing, but in general we may wish to display subpixel disparities. Note that the dot position was not restricted to integer disparities, and antialiasing was used to depict noninteger positions. Background dots have a parallax of À2 H-pixels and the target dots have a parallax of 2 H-pixels. (A) Left and right images of the stereogram for cross-fusing. Example of a random-dot stereogram presented on a column-interleaved display showing the artifact.

Stereogram dinosaur