Surface Reflectance Estimation and Natural Illumination Statistics

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves highly underconstrained due to the variety of potential illumination distributions and surface reflectance properties. We have found that the spatial structure of real-world illumination possesses some of the statistical regularities observed in the natural image statistics literature. A human or computer vision system may be able to exploit this prior information to determine the most likely surface reflectance given an observed image. We develop an algorithm for reflectance classification under unknown real-world illumination, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image. We also develop an automatic feature selection method.

Hierarchical Shape Description of Objects by Selection and Modification of Prototypes

An approach towards shape description, based on prototype modification and generalized cylinders, has been developed and applied to the object domains pottery and polyhedra: (1) A program describes and identifies pottery from vase outlines entered as lists of points. The descriptions have been modeled after descriptions by archeologists, with the result that identifications made by the program are remarkably consisten with those of the archeologists. It has been possible to quantify their shape descriptors, which are everyday terms in our language applied to many sorts of objects besides pottery, so that the resulting descriptions seem very natural. (2) New parsing strategies for polyhedra overcome some limitations of previous work. A special feature is that the processes of parsing and identification are carried out simultaneously.