A physiologically motivated human color visual system model which represents visual information with one brightness component (A) and two chromatic components (C/sub 1/ and C/sub 2/) is used to create stimuli for testing the color perception of deuteranomalous trichromats. Two experiments are performed. Using simple ramp patterns, the first experiment finds that three deuteranomalous trichromat test subjects can distinguish variations only in the C/sub 2/ component of the color vision model. This finding is further tested in the second experiment: a set of paired comparison preference tests. Two altered versions of each of three natural color images are prepared by setting either one of the color components to a constant over the full image. Pairs of an original and a distorted image are presented to the test subjects, and they are asked to indicate which image they prefer. The experimental results indicate that the C/sub 1/ channel is severely attenuated in the deuteranomalous trichromat test subjects, and that nearly all their color sensation is mediated by the C/sub 2/ channel of the color vision model.

A model of the human color vision system is used to construct constant brightness color Mach band stimuli along two chromatic axes for display on a standard workstation color monitor. Chromatic (not brightness) Mach bands appear in the displayed stimuli as over- and under-shoots along the axis of the varied component. The stimuli are displayed on several different but similar monitors without affecting the appearance of the Mach bands. Anomalous trichromats see no change in color when presented with a stimulus containing variations along only one of the two chromatic axes.

Three hierarchical multiresolution image fusion techniques are implemented and tested using image data from the Airborne Visual/Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The methods presented focus on combining multiple images from the AVIRIS sensor into a smaller subset of images white maintaining the visual information necessary for human analysis. Two of the techniques are published algorithms that were originally designed to combine images from multiple sensors, but are shown to work well on multiple images from the same sensor. The third method presented was developed specifically to fuse hyperspectral images for visual analysis. This new method uses the spatial frequency response (contrast sensitivity) of the human visual system to determine which features in the input images need to be preserved in the composite image(s) thus ensuring the composite image maintains the visually relevant features from each input image. The image fusion algorithms are analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. After analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast sensitivity based fusion is shown to provide excellent fusion results and, in every case, outperformed the other two methods.

The major goal of this research was to investigate speech coding techniques in an attempt to achieve high quality speech transmittable at 4800 bits per second. The approach taken to achieve this goal was to code the frequency domain representation of speech. Speech was represented by a sparse set of frequency components. Four frequency selections schemes were implemented, and the resulting frequency coefficients (magnitude and phase) were coded in an efficient manner for transmission. Specific techniques involved in the speech coder included: (1) a recurrent neural architecture to make a periodic/noiselike decision, (2) the use of variable length windows for analysis and synthesis, and (3) a representation of noiselike speech using frequency banded energy information. The quality of the reconstructed speech was tested using listening tests which compared the different frequency selection schemes, along with original and sampled speech. The system did not achieve 'toll quality' speech; however, the resulting speech was highly intelligible. Specific quality degradation was noted at window transitions.

A new iterative maximum picking neural net (MAXnet) is presented. This formulation determines the value and the location either for a unique maximum or for multiple maxima. This new net converges, for many commonly occurring distributions, in O(log M) iterations using only simple computing elements.

The relationship between backpropagation and extended Kalman filtering for training multilayer perceptrons is examined. These two techniques are compared theoretically and empirically using sensor imagery. Backpropagation is a technique from neural networks for assigning weights in a multilayer perceptron. An extended Kalman filter can also be used for this purpose. A brief review of the multilayer perceptron and these two training methods is provided. Then, it is shown that backpropagation is a degenerate form of the extended Kalman filter. The training rules are compared in two examples: an image classification problem using laser radar Doppler imagery and a target detection problem using absolute range images. In both examples, the backpropagation training algorithm is shown to be three orders of magnitude less costly than the extended Kalman filter algorithm in terms of a number of floating-point operations.>

The problem of fusing information from multiple sensors to perform pattern recognition is addressed. A technique is proposed for performing target/nontarget discrimination using information from absolute range and forward looking infrared (FLIR) sensors. A multilayer perceptron is used to perform the feature-level fusion and is compared to ak-nearest-neighbor classifier as well as a nonparametric Bayesian classifier using Parzen windows. All three classifiers show statistically significant improvement from the fusion process; however, the multilayer perceptron uses far fewer free parameters, which should improve generalization capability. Also, the multilayer perceptron requires much less computation than the traditional classifiers

The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear.

An algorithm is presented in this paper which advances research in noisy background target recognition. This recognition process first uses an enhancement algorithm which extracts edges from an input scene. The edge enhanced target scene is searched with an edged object template and the correlation output displayed. High correlation peaks could be targets or high energy noise correlated with the edged template. To distinguish between these two conditions, the correlation peaks are used as centers for normalization of the input scene. An area equal to the size of the target is normalized to the template energy around each correlation center, the rest of the image being blanked, and the new scene is recorrelated with the template.

On October 25, 1982, an X-Y addressable, 16- electrode, 4-by-4 Array, multiplexed switching electrode chip was implanted on the brain of a laboratory beagle. The purpose was to monitor the electroencephalogram (EEG) and visually evoked response (VER) data. This paper proposes a second-generation brain chip with design improvements incorporated, describes the fabrication techniques used to design a chip capable of monitoring visually evoked brain signals, and discusses the theory of operation associated with the chip. The second-generation brain chip includes self-contained power with on-board multiplexing and clocking circuits. The most significant improvement is the incorporation of analog-to-digital (A/D) converters. By using A/D converters, brain signal data can be converted at the site of implant, eliminating induced noise from analog wires used on the original chip.