Optical production systems using neural networks and symbolic substitution

Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

Industrial use of a real-time optical inspection system

Real-time optical inspection techniques are discussed and implemented on laboratory systems. Specifically, we show how Fourier transform and Hough transform data can be used in an industrial inspection application. We also discuss how a 1-D synthetic discriminant function is used for label inspection. Real-time optical Hough transform inspection data using a 2-D spatial light modulator is presented and discussed. A color liquid crystal television (LCTV) is used as the input transducer to provide real-time data. New methods to process color image data on color LCTVs are detailed. A new high speed acoustooptic architecture to generate Hough transform slices is also detailed. A specific product inspection case study is discussed.

Optical symbolic substitution for morphological transformations

An optical architecture that uses symbolic substitution to perform morphological transformations is proposed. It is shown how the four basic morphological transformation operations can be posed as symbolic substitution problems. Representative examples of the application of morphological transformations to image processing are given.

Hough transform projections and slices for object discrimination and distortion estimation

A new technique for determining the distortion parameters (location, orientation, and scale) of general 2-D objects is introduced. It uses the straight-line Hough transform as a feature space. The technique is very efficient and robust, since the dimensionality of the feature space is low and since it uses input images directly (with no preprocessing such as segmentation). Because the feature space allows separation of translation and rotation effects, a hierarchical algorithm to discriminate among objects and to detect object rotation and translation using projections and slices of the Hough space is possible.

Real-time optical laboratory solution of parabolic differential equations

An optical laboratory matrix-vector processor is used to solve parabolic differential equations (the transient diffusion equation with two space variables and time) by an explicit algorithm. This includes optical matrix-vector nonbase-2 encoded laboratory data, the combination of nonbase-2 and frequency-multiplexed data on such processors, a high-accuracy optical laboratory solution of a partial differential equation, new data partitioning techniques, and a discussion of a multiprocessor optical matrix-vector architecture.

Reduced sensitivity algorithm for optical processors using constraints and ridge regression

Optical linear algebra processors that involve solutions of linear algebraic equations have significant potential in adaptive and inference machines. We present an algorithm that includes constraints on the accuracy of the processor and improves the accuracy of the results obtained from such analog processors. The constraint algorithm matches the problem to the accuracy of the processor. Calculation of the adaptive weights in a phased array radar is used as a case study. Simulation results prove the benefits advertised. The desensitization of the calculated weights to computational errors in the processor is quantified. Ridge regression isused to determine the parameter needed in the algorithm.

Directed graph for adaptive organization and learning of a knowledge base

A directed graph is considered for organization of a knowledge base for neural, associative, model-based, and other advanced processors. Its ability to self-organize itself, delete old information, and add new information and its many interconnections make it most suitable for optical realization and use in advanced neural and adaptive optical processors. An alphanumeric image space example is used as a case study, and an optical processor architecture to achieve this with impressive performance is discussed.

Rule-based symbolic processor for object recognition

The application of symbolic processing and rule-based methods for target recognition using correlation filters is considered. The concept of partitioning images is introduced, and its advantages are described. Techniques for rule development, symbolic substitution, error correction via associative processing, and on-line filter adaptation are advanced. Initial simulation results are also presented and discussed.

Optical associative processor for general linear transformations

A new technique for the realization of general linear transformations using associative memories is described. An optical architecture for its implementation is also presented. A low-level feature space processor using this architecture is proposed. The processor is capable of recognizing and locating objects of various shapes and uses certain linear transformations in the feature space for distortion invariance.

Minimum average correlation energy filters

The synthesis of a new category of spatial filters that produces sharp output correlation peaks with controlled peak values is considered. The sharp nature of the correlation peak is the major feature emphasized, since it facilitates target detection. Since these filters minimize the average correlation plane energy as the first step in filter synthesis, we refer to them as minimum average correlation energy filters. Experimental laboratory results from optical implementation of the filters are also presented and discussed.

Optical iconic filters for large class recognition

Approaches are advanced for pattern recognition when a large number of classes must be identified. Multilevel encoded multiple-iconic filters are considered for this problem. Hierarchical arrangements of iconic filters and/or preprocessing stages are described. A theoretical basis for the sidelobe level and noise effects of filters designed for large class problems is advanced. Experimental data are provided for an optical character recognition case study.

Model-based knowledge-based optical processors

An efficient 3-D object-centered knowledge base is described. The ability to on-line generate a 2-D image projection or range image for any object/viewer orientation from this knowledge base is addressed. Applications of this knowledge base in associative processors and symbolic correlators are then discussed. Initial test results are presented for a multiple degree of freedom object recognition problem. These include new techniques to achieve object orientation information and two new associative memory matrix formulations.

Error-correction coding in an associative processor

A technique for encoding binary outputs from optical filters or matrix memories used in an associative processor for object recognition is discussed. Binary coded output vectors (rather than unit vectors) are used and considerably improve storage capacity. The output codes or matrix memories are chosen from coding theory to enable error correction and detection. The error classification rate for the coded scheme is compared to the noncoded version for different amounts of noise in the input and output planes. Discussion of extensions to more classes, more errors, and multilevel coding are included.

Real-time deformation invariant optical pattern recognition using coordinate transformations

The well-known scale and rotation invariant polar-logarithmic coordinate transformation is used to achieve in-plane distortion invariant pattern recognition. The coordinate transform is produced by a computergenerated hologram on a laser printer. Attention is given to weighting terms in the output and their effect on resolution and the number of input plane pixels removed near the origin. The optically produced coordinate transformed input pattern is interfaced to a correlator by a pocket liquid crystal TV to provide real-time processing. Experimental results are included.

Detection of target trajectories using the Hough transform

Recently developed Hough transform techniques for curved object detection are applied to the detection of target trajectories. Multitarget missile trajectories are considered with noise present and missing data.

Phase correction of light modulators

A new and inexpensive holographic technique for measuring the phase errors of a two-dimensional spatial light modulator (SLM) is described, and experimental verification is provided. A new technique to correct spatial phase errors in any SLM is then detailed and experimentally demonstrated. This technique is employed for the Radio Shack liquid-crystal television SLM, and a shift-invariant correlator is obtained. Additional low-pass filtering techniques (appropriate for any SLM with a fixed pattern of modulating cells) are discussed that provide improved contrast and achieve proper correlations.

Frequency-domain synthesis of modified matched spatial filters

Modified matched spatial filters capable of recognition and discrimination are described. Synthesis of these filters in the frequency rather than the space domain is considered. Synthesis of a deconvolution and a binary frequency filter are discussed, and initial results are presented.

Optical linear algebra processors: noise and error-source modeling

The modeling of system and component noise and error sources in optical linear algebra processors (OLAP's) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.