Abstract: A method and apparatus for forecasting the likely occurrence of convective weather events, such as thunderstorms. An image filter is used to identify areas of interest within a meteorological image that are likely to contain convective weather. The image filter and an image difference processor identify sub-image regions within the meteorological image that are likely to experience a growth and/or decay of weather events. The classification filter classifies sub-image regions within the meteorological image into a number of predetermined storm categories. The meteorological images are filtered using matched filters, features within the filtered images are tracked, and the resulting track vectors are combined according to the storm classification. The meteorological image, interest image, growth/decay image, classification image, and combined vectors are processed to produce the short-term forecast.

Abstract: A method and apparatus for forecasting the likely occurrence of convective weather events, such as thunderstorms. An image filter is used to identify areas of interest within a meteorological image that are likely to contain convective weather. The image filter and an image difference processor identify areas within the meteorological image that are likely to experience a growth and/or decay of weather events. The meteorological image, interest image and growth/decay image are processed to produce the short-term forecast.

Abstract: A method and apparatus for forecasting the likely occurrence of convective weather events, such as thunderstorms. An image filter is used to identify areas of interest within a meteorological image that are likely to contain convective weather. The image filter and an image difference processor identify sub-image regions within the meteorological image that are likely to experience a growth and/or decay of weather events. The classification filter classifies sub-image regions within the meteorological image into a number of predetermined storm categories. The meteorological images are filtered using matched filters, features within the filtered images are tracked, and the resulting track vectors are combined according to the storm classification. The meteorological image, interest image, growth/decay image, classification image, and combined vectors are processed to produce the short-term forecast.

Abstract: A method and apparatus for forecasting the likely occurrence of convective weather events, such as thunderstorms. An image filter is used to identify areas of interest within a meteorological image that are likely to contain convective weather. The image filter and an image difference processor identify areas within the meteorological image that are likely to experience a growth and/or decay of weather events. The meteorological image, interest image and growth/decay image are processed to produce the short-term forecast.

Abstract: A computer module is interactively trained to recognize patterns of spectral or textural features in imagery. The interactively trained module is based on techniques of knowledge-based image processing and an approach that uses interest images to provide a means of continuous feedback of module performance to the user. The user, in turn, responds by indicating where the module is making mistakes. A set of user indicated examples and counter examples form the inputs to a machine learning program called Functional Template Learning. The trained module is exportable as an independent agent to search large databases for matching patterns. Following retrieval of images having matching patterns, a user can further refine the agent's performance by indicating where mistakes have been made. The overall search tool employing the trained module is capable of prescreening and highlighting images, significantly reducing the workload of analysts attempting to detect regions or objects in imagery.

Abstract: Computer apparatus and method in a computer system provides shape matching. A template of a desired shape is matched to a subset of points in a subject image. The matching is determined according to an arbitrarily complex function of image point values and template point values followed by an arbitrary operation on the plurality of results of the arbitrarily complex function. In the case of fuzzy template matching, the operation is the average preferably followed by clipping to the range [0, 1] for defining fuzzy membership values. The arbitrarily complex function may be formed of a discrete array of single variable functions or single variable lookup tables.

Abstract: A matching machine is provided for use in model-based entity recognition systems. Input to the matching machine includes a detected data event separated into portions and a set of predefined hierarchical models of desired entities. The matching machine employs recursive procedures to match data event portions against model parts, and records in list structures matches between event portions and model parts. For each match, a degree of match is calculated according to a function defining the involved model part. An essential feature of calculating a degree of match is the accumulation of working degrees of match (i.e. figures of merit reflecting the strength of match of a subset of data event portions to a subset of model parts and the degree to which the relationship of portions matches the predefined relationship of model parts in arbitrarily complex models). The degree of match is recorded in the list structures.