Optical processing for landmark identification

A study of optical pattern recognition techniques, available components and airborne optical systems for use in landmark identification was conducted. A data base of imagery exhibiting multisensor, seasonal, snow and fog cover, exposure, and other differences was assembled. These were successfully processed in a scaling optical correlator using weighted matched spatial filter synthesis. Distinctive data classes were defined and a description of the data (with considerable input information and content information) emerged from this study. It has considerable merit with regard to the preprocessing needed and the image difference categories advanced. A optical pattern recognition airborne applications was developed, assembled and demontrated. It employed a laser diode light source and holographic optical elements in a new lensless matched spatial filter architecture with greatly reduced size and weight, as well as component positioning toleranced

A Cellular Neural Associative Array for Symbolic Vision

. A system which combines the descriptional power of symbolic representations with the parallel and distributed processing model of cellular automata and the speed and robustness of connectionist symbol processing is described. Following a cellular automata based approach, the aim of the system is to transform initial symbolic descriptions of patterns to corresponding object level descriptions in order to identify patterns in complex or noisy scenes. A learning algorithm based on a hierarchical structural analysis is used to learn symbolic descriptions of objects. The underlying symbolic processing engine of the system is a neural based associative memory (AURA) which use enables the system to operate in high speed. In addition, the use of distributed representations allow both efficient inter-cellular communications and compact storage of rules. 1 Introduction One of the basic features of syntactic and structural pattern recognition systems is the use of the structure of the patterns..