<title>Architecture for processing image algebra operations</title>

Abstract

The proposed architecture is a logical design specifically for image algebra and other matrix related operations. The design is a fine grain SIMD concept consisting of three tightly coupled components: a spatial configuration processor, a weighting processor (point-wise), and an accumulation processor (point-wise). The flow of data and image processing operations are directed by a control buffer and pipe lined to each of the three processing components. The low-level abstraction of the proposed computational system is founded on the mathematical principle of discrete convolution and its geometrical decomposition. This geometrical decomposition combined with array processing requires redefining specific algebraic operations and reorganizing their order of parsing in the abstract syntax. The logical data flow of such an abstraction leads to a division of operations, those defined by point-wise operations, the others in terms of spatial configuration. The effect of this particular decomposition allows convolution type operations to be computed strictly as a function of the number of elements in the template (mask, filter, etc.) instead of the number of picture elements in the image. The potential utility of this architectural design lies in its ability to provide order statistic filtering and all the arithmetic and logic operations of the image algebra's generalized matrix product. The generalized matrix product is the most powerful fundamental formulation in the algebra, thus allowing a wide range of applications.