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This research focuses on the application of attentive wide-field sensing in geomatics, specifically for selecting texture maps that optimize the visualization of rendered terrains. By utilizing non-parametric background subtraction and machine learning techniques, such as Gaussian mixture models and maximum a posteriori classification, we analyze how various textures affect visual perception and judgment of surface attitude. Our findings contribute to better data fusion techniques for synthetic vision systems, emphasizing the importance of human perception in interpreting complex visual data.
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Applied Research in Elder Lab • Attentive wide-field sensing • Geomatics – Choosing texture maps for rendered terrains York CVR/DRDC Joint Workshop
Attentive Surveillance Sensor Prototype 1 Foveal Panoramic York CVR/DRDC Joint Workshop
Artificial Attentive Sensing York CVR/DRDC Joint Workshop
Space-Time Mosaics York CVR/DRDC Joint Workshop
Motion Tracking in Attentive Sensor York CVR/DRDC Joint Workshop
Non-Parametric Background Subtraction Gaussian mixture model for background and foreground processes is learned by incremental Expectation Maximization (EM): Learned distributions for 2 sample pixel processes in the scene over the consecutive 700 frames Pixels are classified as foreground or background using a maximum a posteriori classifier (MAP): York CVR/DRDC Joint Workshop
Non-parametric Background Subtraction for Face Detection Some Results Learned Background Learned Foreground Original Frame Segmented Frame York CVR/DRDC Joint Workshop
Example York CVR/DRDC Joint Workshop
1000 ms 59 ms 506 ms Until Response What do we see in a glance? Psychophysical Method York CVR/DRDC Joint Workshop
12% Base = 52% 10% 8% Contribution 6% 4% 2% 0% Colour Local Features Familiarity Global Low-Level Completeness Global High-Level Factor What do we see in a glance? Results York CVR/DRDC Joint Workshop
Geomatics: Texture mapping terrain to optimize visual judgments of surface attitude York CVR/DRDC Joint Workshop
The Challenge: Enhanced/Synthetic Vision for Aviation York CVR/DRDC Joint Workshop
Experiment 1 - Textures Multi-scale random rectilinear (1D 1/f) Multi-scale random disks Multi-scale regular rectilinear Multi-scale random (2D 1/f) Single-scale random (2D bandpass) Single-scale random rectilinear (1D bandpass) Single-scale random disks Single-scale regular rectilinear York CVR/DRDC Joint Workshop
Experiment 1 Multi-scale random disks Slant: 40°, Tilt: 90° Simulated distance: 26 m Multi-scale random disks Slant: 40°, Tilt: 90° Simulated distance: 228 m York CVR/DRDC Joint Workshop
4 20 Multi-scale 3.5 Single-scale 10 3 2.5 0 2 Log (range of viewing distances) -10 1.5 Mean Slant Error (Degrees) -20 1 0.5 Range: multi-scale -30 0 Range: single-scale -40 -50 0 2 4 10 10 10 scale random rectilinear scale random disks scale random scale random rectilinear scale random disks scale regular rectilinear Simulated Distance (Meters) scale random scale regular - - - - - - - - rectilinear Single Single Single Single Multi Multi Multi Multi Texture Experiment 1 Results York CVR/DRDC Joint Workshop
Understanding the human perception of fused displays York CVR/DRDC Joint Workshop
Transparency is used in ESVS systems to convey multiple streams of data on a single display York CVR/DRDC Joint Workshop
How do humans perceive fused terrain displays? York CVR/DRDC Joint Workshop
Perceived slant of fused surfaces can be predicted by optimal estimation model Slant relative to mean slant (deg) Perceived slant of surface B alone Perceived slant of surface A alone Optimal fusion estimator Slant Difference (deg) York CVR/DRDC Joint Workshop
