Perry A. Molley

Acousto-optic signal processing for real-time image recognition

The experimental results from an acousto-optic image correlator are presented. The performance ofthis system, which is evaluated in terms of the peak-to-sidelobe ratio, is shown to be improved by subtracting off the signal-dependent bias terms. These bias terms can be optically generated using the same acousto-optic correlator. Structured background features in the input scene can also dramatically affect the correlation result. This paper suggests a novel implementation of the difference-squared error algorithm using a modified acousto-optic architecture that can discriminate better than conventional correlation in scenes with structured background features. Experimental results from real-time systems constructed in the laboratory are presented.

Acousto-optic image correlator with a throughput rate of 1000 templates per second

A two dimensional image correlator based on acousto-optic (AO) and charge coupled devices (CCDs) is described that can be built with existing technology to provide 1000 frames per second operation. In recent years, architectures have been developed that perform the two dimensional correlation utilizing one dimensional input devices. The input scene is loaded into the acousto-optic device (AOD) one line at time. This line is then correlated against all of the rows of a reference template introduced into the optical system using a one dimensional array of LEDs or laser diodes. However, it generally takes a much greater time to load the AO cell than it does to process the information. This latency time severely limits the maximum throughput rate of the processor. This paper introduces a new acousto-optic correlator implementation that overcomes this bottleneck so that processing can occur close to 100% of the time. A grayscale image correlator is proposed that can be built using present technology that can realistically achieve throughput rates on the order of lO12 operations per second. This translates to over 1000 correlations per second for input scenes with dimensions of 512 x 512 pixels and reference templates of size 64 x 64 pixels.

A Comparison Of Real-Time Optical Correlators For Pattern Recognition

Two types of optical correlators have been built to investigate real-time pattern recognition. The first employs one-dimensional devices to perform the two dimensional correlation in real time. This architecture uses an array of light emitting diodes (LEDs) to input an electronically stored reference image into the processor in parallel. The input scene data is introduced into the processor one line at a time using an acousto-optic device (AOD). Multichannel time integrating correlations are performed in the row direction using the AOD and in the column direction using a charge coupled device (CCD) operating in the time delay and integrate mode. A processor has been built using this technology which correlates a 64 x 44 pixel binary reference image with a 256 x 232 input scene at video rates. The second correlator is a space integrating Fourier transform based correlator. A magneto optic-device (MOD) is used at the Fourier transform plane to rapidly change filter functions. The binary nature of the MOD device necessitates using either a binary phase or binary amplitude representation of the desired complex filter function. For this reason, several types of Binary Phase-Only Filter (BPOF) representations have been analyzed and experimentally investigated. Experimental correlation results have been obtained using both the Hartley BPOF and a newly developed class of complex binary filters, called Quad-Phase-Only Filters (QPOF). The performance of the two systems will be compared on the basis of processing speed, space bandwidth product, processor size and light efficiency. The inherent differences between incoherent and coherent processing and their implications for filter design will also be discussed. Finally, estimates of future performance will be presented.

Compact real-time acousto-optic image correlator

The design and development of a compact acousto-optic image correlator capable of performing real-time correlations on grayscale imagery will be described. The system utilizes one-dimensional optical devices to perform the desired two-dimensional correlation. The two-dimensional correlation is performed as a series of multichannel time-integrating correlations between each input image line and a reference template that is stored in an electronic memory. The rows of the reference template are introduced into the processor in parallel using a one-dimensional laser diode array. The correlation in the vertical direction is performed using a modified charge-coupled device (CCD) operating in the shift-and-add mode. A laser diode array, as opposed to an LED array used in previous systems, provides more power so that full use can be made of the dynamic range capabilities of the acoustooptic device and CCD. Key features of the system will be presented, including the random access template memory, the custom laser diode array consisting of 64 individually addressed laser diodes, and the custom optical design to achieve nearly diffraction-limited image quality and compactness.

Automatic target recognition and tracking using an acousto-optic image correlator

This paper discusses a hybrid electro-optic image processor, developed for automatic target recognition and tracking using an acousto-optic correlator and digital electronics. The optical system performs the computationally intensive correlation operation on the large 2-D input scenes. The electronics provide the decision-making capability and also perform part of the postprocessing needed for increasing the peak-to-clutter ratio in cluttered scenes. The system is able to analyze each correlation plane and apply a real-time template selection algorithm to accommodate scale or rotation changes of the target. A demonstration of the current system capabilities is presented using a terrain board with several different types of stationary and moving model vehicles.

Implementing The Difference-Squared Error Algorithm Using An Acousto-Optic Processor

Real-time electronic and optical pattern recognition systems use correlation as a means for discriminating objects of interest from unwanted objects and residual clutter in an input scene. However, correlation is not a good technique for certain gray-level input images particularly when the background has a high average value such as encountered in automatic target recognition environments. A better algorithm for these types of environments involves computing the difference-squared error between a reference template and the input image. This algorithm has been used for many years for recognizing gray-level images; however, because of the large number of bits of precision required to perform these computations, the algorithm is difficult to implement in real-time using an electronic embedded computer where small size and low power are at a premium. This paper describes a method for implementing the difference-squared algorithm on an acousto-optic time-integrating correlator. This implementation can accommodate the high dynamic range requirement which is inherent in gray-scale recognition problems. The acousto-optic correlator architecture is a natural fit for this implementation because of its capability to perform two-dimensional processing utilizing relatively mature one-dimensional input devices. Furthermore, since this architecture uses an electronically stored reference, rotational and scale variances can be accommodated by rapidly searching through a library of templates, as first described by Psaltis2. The ability to implement the difference-squared algorithm in an acousto-optic correlator architecture has the potential for solving many practical target recognition problems where real-time discrimination of gray-level objects using a compact, low power processor is required.

Design and testing of space-domain minimum-average correlation energy filters for 2-D acousto-optic correlators

Two-dimensional acousto-optic (AO) correlators differ from frequency plane correlators in that multiplying, shifting, and adding, rather than Fourier transforming are used to obtain the correlations. Thus, manu of the available composite filter design techniques are not aimed at designing filters for use in AO correlators because they yield frequency-domain functions. In this paper, a method is introduced for designing filter impulse responses of arbitrary extent for implementation on AO correlators. These filters are designed to yield sharp correlation peaks. Simulation results are included to illustrate the viability of the proposed approach. Also included are some initial results from the first successful use of gray-scale composite filters on an AO correlator.

Automatic target recognition using acousto-optic image correlator

A hybrid electro-optic image processor has been developed for automatic target recognition using an acousto-optic correlator and digital electronics. The optical system performs the computationally intensive correlation operation on the large 2-D input scenes. The electronics provide the decision-making capability and also perform part of the normalization needed for increasing the peak-to-sidelobe ratio in cluttered scenes. The system is able to analyze each correlation plane and apply a real-time template selection algorithm to accommodate scale or rotation changes of the target. A demonstration of the current system capabilities is presented using a terrain board with several different types of stationary and moving model vehicles.

Performance Of The Difference-Squared Error Algorithm On An Acousto-Optic Architecture

The difference-squared error algorithm provides an effective means for discriminating between objects of interest and structured background features. This paper briefly discusses the advantages of this algorithm over conventional correlation based recognition techniques. The output of a real-time acousto-optic processor which computes the difference-squared error between an input data stream and a reference function is presented. The output of this acousto-optic implementation is then compared to the desired theoretical results. Finally, future applications of this architecture for real-time two-dimensional pattern recognition is discussed.