Natural image profiles are most likely to be step edges.
We introduce Geometric Texton Theory (GTT), a theory of categorical visual feature classification that arises through consideration of the metamerism that affects families of co-localised linear receptive-field operators. A refinement of GTT that uses maximum likelihood (ML) to resolve this metamerism is presented. We describe a method for discovering the ML element of a metamery class by analysing a database of natural images. We apply the method to the simplest case--the ML element of a canonical metamery class defined by co-registering the location and orientation of profiles from images, and affinely scaling their intensities so that they have identical responses to 1-D, zeroth- and first-order, derivative of Gaussian operators. We find that a step edge is the ML profile. This result is consistent with our proposed theory of feature classification.
Griffin
Lewis D
LD
Imaging Sciences, Guy's Campus, King's College, London, SE1 9RT, UK. lewis.griffin@kcl.ac.uk
Lillholm
M
M
Nielsen
M
M
D016208:Databases, Factual; D006801:Humans; D016013:Likelihood Functions; D008960:Models, Psychological; D011594:Psychometrics; D014796:Visual Perception / Q000502:physiology*