Publications

Modern high-level programming languages often contain constructs whose semantics are non-trivial. In practice however, software developers generally restrict the use of such constructs to settings in which their semantics is simple (programmers use language constructs in ways they understand and can reason about). As a result, when developing tools for analyzing and manipulating software, a disproportionate amount of effort ends up being spent developing capabilities needed to analyze constructs in settings that are infrequently used. This paper takes the position that such distinctions between theory and practice are an important measure of the analyzability of a language.

This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.