Correlation-based filters (e.g MACE, MACH) have been widely employed for automatic target acquisition. In general, a bank of filters is developed wherein each filter is trained to respond to a particular range of conditions (such as aspect angle). Individual filter outputs are utilized to determine a best match between objects in a scene and the training information. However, it is not uncommon for discrete clutter objects to correlate well with an individual filter, resulting in an unacceptable false alarm rate (FAR). It is the authors' hypothesis that although a clutter event may correlate well with an individual filter, there are discernable differences in the way clutter and targets correlate across the bank of filters. In this paper, the authors investigate a connectionist based approach that combines the individual filter outputs in a non-linear manner for improved performance. Particular attention is given to designing the correlation filter constraints in conjunction with the combination approach to optimize performance.
A study was conducted on the use of morphological processing for detection in Uncooled Infra Red (UCIR) data using the Clutter type 1 and Clutter type 2 databases. The CMO algorithm was used with various modifications. A fixed minimum peak value was used rather than a fraction of the maximum peak per image. This is much more realistic, since many real scenes will not contain targets. One-dimensional directional structuring elements (SEs) were used for the Close Minus Open algorithm. We used the range of gray levels within the output blob peaks (blob analysis) and larger windows in peak sorting to reduce false alarms. A new dilation minus erosion morphological algorithm gave the best result.
Image segmentation, a key component in many Automatic Target Recognition (ATR) systems, has received considerable attention in the research community in recent years. A variety of segmentation approaches exist, and attempts have been made to combine various approaches in order to find more robust solutions. In this paper, the authors describe an inference fusion architecture for combining individual segmentation concepts which results in improved performance over the individual algorithms. We consider segmentation algorithms with several disparate cost functions as experts with a narrowly defined set of goals. The information obtained from each expert is combined and weighted with available evidence using an agent based inference system, resulting in an adaptive, robust and highly flexible image segmentation. Results obtained by applying this approach will be presented.