We introduce an object recognition system using Gabor filters to model the biological visual field and a saccadic behavioral model to emulate biological active vision. A high resolution image containing an object of interest is first processed by an ensemble of multi-resolution, multi- orientation Gabor filters. The object can then be described by an alternating sequence of fixation coordinates and the Gabor responses at pixel locations surrounding that fixation point. Once this sequence is memorized, a complex image can be searched for the same location/feature sequence, indicating the presence of the memorized object. The model is suitable for memorization of arbitrary objects, and a simple example is presented using a human face as the object of interest.
In this paper, it is shown that Evolution Programs can be used to search the weight space for Bayesian training of a Neural Network. Bayesian Analysis is an integration problem (as opposed to an optimization problem) over weight space. The first application of the Bayesian method primarily focused on using a Gaussian approximation of the posterior distribution in an area of high probability in the weight space instead of using formal integration. More recently, training a neural network in a Bayesian fashion has been accomplished by searching weight space for areas of high probability density which obviates the need for the Gaussian assumption. In particular, a hybrid Monte-Carlo method was used to search weight space in a logical manner to obtain an arbitrarily close approximation of the integration involved in a Bayesian analysis. Genetic Algorithms have been used in the past to determine the weights in an ANN, and (with some slight modifications) are ideally suited for searching the weight space to approximate the Bayesian integration. In this respect, the Bayesian framework provides a simple and elegant way to apply Evolution Programs to the ANN training problem. While this paper concentrates on using ANNs as classifiers, the generalization to regression problems is straightforward.
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.
A perceptual-based multiresolution image fusion technique is demonstrated using the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor data. The AVIRIS sensor, which simultaneously collects information in 224 spectral bands that range from 0.4 to 2.5 μm in approximately 10-nm increments, produces 224 images, each representing a single spectral band. The fusion algorithm consists of three stages. First, a Daubechies orthogonal wavelet basis set is used to perform a multiresolution decomposition of each spectral image. Next, the coefficients from each image are combined using a perceptual-based weighting. The weighting of each coefficient, from a given spectral band image, is determined by the spatial-frequency response (contrast sensitivity) of the human visual system. The spectral image with the higher saliency value, where saliency is based on a perceptual energy, will receive the larger weight. Finally, the fused coefficients are used for reconstruction to obtain the fused image. The image fusion algorithm is analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. By 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 to outperform previous fusion methods.
More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, used as a `second opinion' to radiologists, will aid in decreasing the number of false readings of mammograms. A novel feature extraction method is presented that provides increased classification power. Wavelets, previously only exploited for their segmentation benefits, are explored as features for classification. Daubechies4, Daubechies20, and biorthogonal wavelets are each investigated. Applied to 94 difficult-to- diagnose digitized microcalcification cases, performance is 74 percent correct classifications. Feature selection techniques are presented which further improve wavelet classification performance to 88 percent correct classification.
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