A new neural net is described that can easily and cost-effectively accommodate multiple objects in the field of view in parallel. The use of a correlator achieves shift invariance and accommodates multiple objects in ...
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A new neural net is described that can easily and cost-effectively accommodate multiple objects in the field of view in parallel. The use of a correlator achieves shift invariance and accommodates multiple objects in parallel. Distortion-invariant filters provide aspect-invariant distortion. Symbolic encoding, the use of generic object parts, and a production system neural net allow large class problems to be addressed. optical laboratory data on the production system inputs are provided and emphasized. Test data assume binary inputs, although analog (probability) input neurons are possible.
A new distortion-invariant optical correlation filter to produce easily detectable correlation peaks in the presence of noise and clutter and to provide better intraclass recognition is presented. The basic ideas of t...
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A new distortion-invariant optical correlation filter to produce easily detectable correlation peaks in the presence of noise and clutter and to provide better intraclass recognition is presented. The basic ideas of the minimum variance synthetic discriminant function correlation filter (which minimizes noise variance in the output correlation peak/plane) and the minimum average correlation energy filter (which minimizes the average correlation plane energy over all the training images) are unified in a new filter that produces sharp correlation peaks while maintaining an acceptable signal-to-noise ratio in the correlation plane output. This new minimum noise and correlation energy filter approach introduces the concept of using the spectral envelope of the training images and the noise power spectrum to obtain a tight bound to the energy minimization problem that is associated with distortion-invariant filters in noise while allowing the user a variable parameter to adjust depending on the noise or clutter that is expected. We present the mathematical basis for the minimum noise and correlation energy filter and the initial simulation results.
A neural network solution to the data association problem in multitarget tracking is presented. It uses position and velocity measurements of the targets over two consecutive time frames. A quadratic neural energy fun...
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A neural network solution to the data association problem in multitarget tracking is presented. It uses position and velocity measurements of the targets over two consecutive time frames. A quadratic neural energy function, which is suitable for an opticalprocessing implementation, results. Simulation results using realistic target trajectories with target measurement noise including platform movement or jitter are presented. The results show that the network performs well when track data are corrupted by significant noise. Several possible optical neural network architectures to implement this algorithm are discussed, including a new all-optical matrix-vector multiplication approach. The matrix structure is employed to allow binary-ternary spatial light modulators to be used.
optical processors can perform the required operations for the various levels of a hierarchical/ inference computer vision system for scene analysis (detection, enhancement, recognition, feature extraction, and classi...
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optical processors can perform the required operations for the various levels of a hierarchical/ inference computer vision system for scene analysis (detection, enhancement, recognition, feature extraction, and classification) on a multifunctional programmable optical architecture.
Various errors, including analog accuracy, nonlinearities, and noise, are present in all neural networks. The author considers their effects in training and testing on two different pattern recognition neural nets. He...
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Various errors, including analog accuracy, nonlinearities, and noise, are present in all neural networks. The author considers their effects in training and testing on two different pattern recognition neural nets. He shows that the neural nets considered allow some such effects to be included inherently in the neural net synthesis algorithm and that the effect of the other error sources can be trained out by proper selection of neural net design parameters. Multiclass distortion-invariant pattern recognition neural nets are considered. The results are applicable to analog VLSI and optical neural nets.< >
Several recent advances are described that use neural-network methods to produce the higher-order decision surface required for difficult pattern recognition discrimination problems. Work at Carnegie Mellon University...
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Several recent advances are described that use neural-network methods to produce the higher-order decision surface required for difficult pattern recognition discrimination problems. Work at Carnegie Mellon University is emphasized and includes new hyperspherical Ho-Kashyap neural nets and new piecewise quadratic neural nets. Also addressed are Fourier neural-net interconnections to handle multiple objects and achieve morphological, image processing, and enhancement functions.< >
We present an optical correlator implementation of the morphological hit-miss transform. This provides improved recognition of objects in clutter compared with standard optical pattern-recognition correlator technique...
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We present an optical correlator implementation of the morphological hit-miss transform. This provides improved recognition of objects in clutter compared with standard optical pattern-recognition correlator techniques. The hit-miss transform is modified to use rank-order filtering since this gives better performance in noise and clutter. By varying the correlation plane threshold, we can perform dilations, rank-order filters, and erosions on the same multifunctional coherent optical correlator system. We quantify the thresholds required for generic object part recognition and provide simulated and optical laboratory data.
The Hough transform (HT) detects lines in an input but not their location. We describe a new way to determine the position of a line from HT data. The line position information is extracted from the shape of the HT pa...
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The Hough transform (HT) detects lines in an input but not their location. We describe a new way to determine the position of a line from HT data. The line position information is extracted from the shape of the HT pattern around the HT peak. Results are shown illustrating this algorithm on single- and multiple-line input images.
Real-time optical laboratory data is provided on the CMU hybrid optical/digital neural network (NN). Our simulator verifies prior laboratory results and identifies the major system error source as the lack of a true z...
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The performance of two new optical correlation filters (G-MACE and MINACE) for large class (many fonts and true class words) OCR (optical character recognition) applications is considered. We consider filters that can...
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