Perception and Classification of Surface Texture
illumination affects the output of Filter Response Filters (FRF).
FRF are of interest because:
(a) they are commonly used as texture features in automated texture
classification systems, and
(b) they are typically proposed as the back pocket model of the first
stage of the human visual system.
I'll show how naïve classifiers built using these simple features can
fail, and how the model can be used to produce a classifier that is
robust to illumination variation.
What this will show is that single still images are not often not
sufficient for the purposes of surface classification - either for human
or automated systems.
I'll conclude by describing some of our recent research that is
investigating our perceptions of surface texture.