Stochastic and Deterministic Models for Haptic Pseudo-Textures

The haptic environment, due to the limited bandwidth of haptic sense, must be enhanced to aid comprehension. The enhancement procedures focused on here include techniques for pseudo-texturing in virtual environments. Generated texture models are automatically tunable according to the characteristics or properties of data to which it is applied. The proposed methods include stochastic, correlated, and harmonic models as well as several popular halftoning algorithms such as Amplitude Modulation (AM), Bayer´s, and Error Diffusion. Presented analysis determines the parameter change required to yield the minimal perceivable change in texture for each texture level and proposed texturing method. We also consider edge and boundary enhancement. Finally, user evaluations are presented comparing the presented methods in a haptic visualization application.