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.

We suggest a new computational theory for position, size, and orientation invariant recognition of planar gray-scale forms in frontoparallel view. The theory employs a spatial-frequency representation which approximates the complex-logarithmic conformally mapped Wigner distribution of a gray-scale image. Our discussion emphasizes the theoretical significance, method of computation, and practical utility of this powerful new representation for image pattern recognition.

The Wigner distribution (WD) has recently received much attention from researchers interested in dual time/frequency or space/spatial-frequency signal representation. However, an adequate computable approximation to the WD has been lacking. In order to fill this void, we introduce in this paper a new and highly versatile approximation known as the composite pseudo Wigner distribution (CPWD).