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The military has a requirement for small, low-power, low- cost pattern recognition systems that are capable of locating and identifying high value hostile targets. Miniature optical correlators can perform 2D pattern recognition at greater rates than digital platforms of equivalent size, power and/or weight. The patented miniature ruggedized optical correlator (MROC) can be built to meet the environmental, size, power, and weight requirements of military and rugged commercial applications, and at a cost that will permit wide deployment of the capability. The second version of the MROC correlator consists of a ferroelectric liquid crystal device in the input plane for high light efficiency and incorporates a reflective magneto optic spatial light modulator device in the filter pane for very high speed operation. The correlator has a volume of approximately 20 cubic inches. The MROC module, which includes all drive electronics and interfaces, is a 6U VME module that occupies 5 VME card slots. In this paper we will provide a brief review of the MROC construction and present sample results obtained from the MROC II breadboard. Initial tests demonstrated very high correlation levels, i.e. excellent discrimination, at pattern matching rates of 1920 per second on visible and simulated LADAR images of military vehicles and digital images of fingerprints.

The recognition system rapid application prototyping tool (RSRAPT) was developed to evaluate various potential configurations of miniature ruggedized optical correlator (MROC) modules and to rapidly assess the feasibility of their use within systems such as missile seekers. RSRAPT is a simulation environment for rapidly prototyping, developing, and evaluating recognition systems that incorporate MROC technology. It is designed to interface to OLE compliant Windows applications using standard OLE interfaces. The system consists of nine key functional elements: sensor, detection, segmentation, pre-processor, filter selection, correlator, post-processor, identifier, and controller. The RSRAPT is a collection of object oriented server components, a client user interface and a recognitions system image and image sensor database. The server components are implemented to encapsulate processes that are typical to any optical-correlator based pattern recognition system. All the servers are implemented as Microsoft component object model objects. In addition to the system servers there are two key 'helper servers.' The first is the image server, which encapsulates all 'images'. This includes gray scale images and even complex images. The other supporting server is the filter generation server. This server trains the system on user data by calculating filters for user selected image types. The system hosts a library of standard image processing routines such as convolution, edge operators, clustering algorithms, median filtering, morphological operators such as erosion and dilation, connected components, region growing, and adaptive thresholding. In this paper we describe the simulator and show sample results from diverse applications.