Invariant image representation: A path toward solving the bin-picking problem

We review the definition of the bin-picking problem and briefly summarize past approaches toward its solution, arguing that a major problem with conventional approaches is their reliance upon impoverished image representations. We then describe a recently developed image representation that uniquely encodes the information in a grey-scale image, decouples the effects of illumination, reflectance, and angle of incidence, and is invariant, within a linear shift, to perspective, position, orientation, and size of all planar forms. This representation is composed of discrete approximations to the complex-logarithmic conformally mapped Wigner distributions of multiple deprojection images. Such a representation could open the frontiers to the development of general-purpose robot vision systems which routinely perform size, orientation, and perspective invariant part recognition while also specifying part location and attitude in 3-D space.