Susan S. Young

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An object recognition approach based on concurrent coarse-and-fine matching using a multilayer Hopfield neural network is presented. The proposed network consists of several cascaded single-layer Hopfield networks, each encoding object features at a distinct resolution, with bidirectional interconnections linking adjacent layers. The interconnection weights(More)
PURPOSE To determine the degree of irreversible image compression detectable in conservative viewing conditions. MATERIALS AND METHODS An image-comparison workstation, which alternately displayed two registered and magnified versions of an image, was used to study observer detection of image degradation introduced by irreversible compression. Five(More)
This paper presents a method for detecting and classifying a target from its foveal (graded resolution) imagery using a multiresolution neural network. Target identification decisions are based on minimizing an energy function. This energy function is evaluated by comparing a candidate blob with a library of target models at several levels of resolution(More)
Pooling experiments are used as a cost-effective approach for screening chemical compounds as part of the drug discovery process in pharmaceutical companies. When a biologically potent pool is found, the goal is to decode the pool, i.e., to determine which of the individual compounds are potent. We propose augmenting the data on pooled testing with(More)
The beta-binomial distribution has been proposed as a model for the incorporation of historical control data in the analysis of rodent carcinogenesis bioassays. Low spontaneous tumor incidences along with the small number and sizes of historical control groups combine to make the moment and maximum likelihood estimates of the beta-binomial parameters(More)
Empirical Bayes procedures for incorporating historical control information in bioassay carcinogenesis studies are receiving attention in the literature. In general, the empirical Bayes methods fail to take into account the error in estimating the parameters of a prior distribution. The implications of this are studied for the beta prior of Tarone (1982,(More)
We present a superresolution image reconstruction from a sequence of aliased imagery. The subpixel shifts (displacement) among the images are unknown due to the uncontrolled natural jitter of the imager. A correlation method is utilized to estimate subpixel shifts between each low-resolution aliased image with respect to a reference image. An error-energy(More)
Flash ladar systems are compact devices with high frame rates that hold promise for robotics applications, but these devices suffer from poor spatial resolution. This work develops a wavelet preprocessing stage to enhance registration of multiple frames and applies super-resolution to improve the resolution of flash ladar range imagery. The triangle(More)
There have been numerous applications of superresolution reconstruction algorithms to improve the range performance of infrared imagers. These studies show there can be a dramatic improvement in range performance when superresolution algorithms are applied to undersampled imager outputs. These occur when the imager is moving relative to the target, which(More)