Investigations into trainable picture processing systems

Abstract

This work concerns the development of a new type of picture processing system for images represented as digital arrays of pixels. This is a synthesis of two established ideas, already under independent investigation. The first of these is picture processing by look-up tables. This is a fast method of generating pixel outputs as a result of input pixels accessing a particular region of a look-up table, pre-loaded with the required data. The second idea is the use of RAMs as learning machines. Here, RAM elements are connected together so as to be alterable in data content by training stimuli in a coherent manner. This results in a system able to exhibit definite responses to later test stimuli, and thus identify these stimuli unambiguously. The methods used for bringing these two conceptstogether are described here. A practicable picture processor results, which can be trained by examples. That is, it can perform a picture transformation simply by presenting to the machine (in a prior training phase) examples of the process. From this, the machine deduces the information necessary to be able to perform the same transformation on unseen patterns. Experiments have been performed on a wide range of variations on this theme. Different types of machines acting on different data and tasks have been tried, under various conditions. A description is given of these machine variations, together with a generalized system fordescribing such variations more formally. The machines were simulated in practice on a microcomputer system; The simulation software used in these investigations is also described. Finally, the implications and limitations of such machines are discussed with reference to their ultimate performance and possible applications in fields other than picture processing.