Mathematical models for perceived roughness of three-dimensional surface textures
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This thesis reports and discusses results from a new methodology for investigating the visually perceived properties of surfaces; by doing so, it also discovers a measurement or estimator for perceived roughness of 1/Fβ noise surfaces. Advanced computer graphics were used to model natural looking surfaces (1/Fβ noise surfaces). These were generated and animated in real-time to enable observers to manipulate dynamically the parameters of the rendered surfaces. A method of adjustment was then employed to investigate the effects of changing the parameters on perceived roughness. From psychophysical experiments, it was found that the two most important parameters related to perceived roughness were the magnitude roll-off factor (β) and RMS height (σ) for this kind of surfaces. From the results of various extra experiments, an estimation method for perceived roughness was developed; this was inspired by common frequency-channel models. The final optimized model or estimator for perceived roughness in 1/Fβ noise surfaces found was based on a FRF model. In this estimator, the first filter has a shape similar to a gaussian function and the RF part is a simple variance estimator. By comparing the results of the estimator with the observed data, it is possible to conclude that the estimator accurately represents perceived roughness for 1/Fβ noise surfaces.