Steven P. Brumby

Learn More
—We have developed an automated feature detec-tion/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in(More)
We present results quantifying the exploitability of compressed remote sensing imagery. The performance of various feature extraction and classification tasks is measured on hyperspectral images coded using the JPEG-2000 Standard. Spectral decorrelation is performed using the Karhunen-Lò eve Transform and the 9-7 wavelet transform as part of the JPEG-2000(More)
The search for a suitable golf course is a very important issue in the travel plans of any modern manager. Modern management is also infamous for its penchant for high-tech gadgetry. Here we combine these two facets of modern management life. We aim to provide the cutting-edge manager with a method of finding golf courses from space! In this paper, we(More)
We propose a system for solving pixel-based multi-spectral image classification problems with high throughput pipelined hardware. We introduce a new shared weight network architecture that contains both neural network and morphological network functionality. We then describe its implementation on Reconfigurable Computers. The implementation provides(More)
—Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We(More)
—Automated, invariant object detection has proven itself to be a substantial challenge for the artificial intelligence research community. In computer vision, many different benchmarks have been established using whole-image classification based on datasets that are too small to eliminate statistical artifacts. As an alternative, we used a new dataset(More)
Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of(More)
Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization,(More)