Randy P. Broussard

Physiologically motivated image fusion using pulse-coupled neural networks

This paper uses a high level vision model to describe the information passing and linking within the primate visual system. Information linking schemes, such as state dependent modulation and temporal synchronization, are presented as methods the vision system uses to combine information using expectation to fill in missing information and remove unneeded information. The possibility of using linking methods derived from physiologically based theoretical models to combine current image processing techniques for pattern recognition purposes is investigated. These image processing techniques are transforms such as (but not limited to) wavelet filters, hit or miss filters, morphological filters, and difference of gausian filters. These particular filters are chosen because they simulate functions that are performed in the primate visual system. To implement the physiologically motivated linking methods, the Pulse Coupled Neural Network (PCNN) is chosen as a basic building block for the vision model which performs linking at the neuronal pulse level. Last, an image fusion network which incorporates information linking based on the PCNN is described, and initial results are presented.

Performance modeling for automatic target recognition systems

This paper explores using linear regression and artificial neural networks (ANN) to model the performance of an ATR algorithm based on a given set of data. Here, a probability of detection response surfaces as a function of relevant parameters is simulated. It is then shown that this surface can be approximated using either linear regression or an ANN with good results. These regression surfaces can provide valuable information to the ATR developer/customer in terms of trying to predict ATR performance in untested areas. The application of this ATR performance modeling methodology becomes clear when we consider applying it to a common problem, such as air-to-ground target detection, where the changing parameters of the target can give a good set of data points from which to build the response curve.

Fusion of focus of attention alternatives for FLIR imagery

A model-based vision (MBV) approach to automatic target cuing/recognition (ATC/R) using real infrared (IR) image data will be presented. The MBV-ATC/R is comprised of three parts: Focus of attention (FOA), indexing, and match/search. The FOA module analyzes the IR image and extracts (segments) regions of interest that may contain targets. The focus of this article will be on the FOA portion of the MBV-ATC/R approach. In particular, three methods of FOA will be optimized, compared, and fused. The first FOA module is a Least Asymmetrical Daubechies wavelet decomposition method. The second FOA module is a physiologically based Difference of Gaussians. The third FOA module is a Morphological hit- and-miss transform. The three FOA algorithms are individually optimized using a genetic algorithm. Then an adaptive pulse coupled neural network is used to fuse the results.