A generalized approach to real-time pattern recognition in sensed data

Abstract

Many applications that focus on target detection in an image scene develop algorithms specific to the task at hand. These algorithms tend to be dependent on the type of input data used in the application and thus generally fail when transplanted to other detection spaces. We wish to address this data dependency issue and develop a novel technique which autonomously detects, in real time, all target objects embedded in an image scene irrespective of the imagery representation. We accomplish this task using a heirarchical approach in which we use an optimal set of linear filters to reduce the data dimensionality of an image scene and then spatially locate objects in the scene with a neural network classifier. We prove the generality of this approach by applying it to two distinctly separate applications. In the first application, we use our algorithm to detect a specified set of targets for an Automatic Target Recognition (ATR) task. The data for this application is retrieved from two-dimensional camera imagery. In the second task, we address the problem of sub-pixel target detection in a hyperspectral image scene. This data set is represented by hyperspectral pixel bands in which target objects occupy a portion of a hyperspectral pixel. A summarized description of our algorithm is given in the following section.