Abstract: An acoustic event classifier uses particle swarm optimization (PSO) to perform a flexible time correlation of a sensed acoustic signature to reference acoustic signatures in a multi-dimensional parameter space. The classifier may fuse the acoustic signatures from multiple acoustic sensors to form the sensed acoustic signature. The approach is generally applicable to classify all types of acoustic events but is particularly well-suited to classify “explosive” events such as gun shots, mortar blasts, improvised explosive device blasts etc. that produce an acoustic signature having a shock wave component that is periodic and non-linear.

Abstract: A high resolution imaging system having a wide field-of-regard. The wide field image generation system (10) of the present invention is operative to provide a composite digital representation of a scene within a field-of-regard. The invention (10) includes a sensor arrangement (12, 14, and 16) for generating first and second frames of image data. A scene correlator (18) registers the first and second frames of image data within the field-of-regard. A temporal filter (22) averages image data in the first frame with image data at a respective corresponding location in the second frame to provide a third frame of image data. When displayed, the third frame of image data provides a wide field-of-regard, high resolution image with minimal undesirable seams therein.

Abstract: Image data compression is implemented by partitioning an image into relatively small blocks of pixels, matching each block to an orthogonal icon, and extracting attributes associated with the icon. A table of these orthogonal icons and attributes represents a data compressed image. Each block is processed separately from all other blocks. Orthogonal processing is used to preserve orthogonal features while attenuating non-orthogonal features. An optimum set of orthogonal icons and an optimum set of attributes for each icon further improves data compression and fidelity. The optimal set of orthogonal icons includes a flat icon, an edge icon, a ribbon icon, a corner icon, and a spot icon. An optimal set of attributes includes average intensities, intensity transition position and separation, and angle of the principal axes.

Abstract: Image data is processed by a low level feature detection processor that extracts low level features from an image. This is accomplished by converting a matrix of image data into a matrix of orthogonal icons that symbolically represent the image scene using a predetermined set of attributes. The orthogonal icons serve as the basis of processing by means of a high level graph matching processor which employs symbolic scene segmentation, description, and recognition processing that is performed subsequent to the low level feature detection. This processing generates attribute graphs representative of target objects present in the image scene. High level graph matching compares predetermined attributed reference graphs to the sensed graphs to produce a best common subgraph between the two based on the degree of similarity between the two graphs. The high level graph matching generates a recognition decision based on the value of the degree of similarity and a predetermined threshold.

Abstract: Data decompression of an image is implemented by reconstructing blocks of pixels from a table that stores orthogonal icons and related attributes representing a data compressed image. Each block of pixels is processed independently of all other blocks. Orthogonal processing is employed to reconstruct orthogonal features that relate to manufactured elements in an image. An optimum set of orthogonal icons and an optimum set of attributes for each icon are employed to further improve data decompression and fidelity. The optimal set of orthogonal icons includes a flat icon, an edge icon, a ribbon icon, a corner icon and a spot icon. The optimal set of attributes includes average intensities, intensity transition position and separation, and angle of the principal axis.