Hierarchical Target Model Analysis (HTMA) is an automatic pattern matching process for categorizing tactical targets. Stored target model information is re-projected into the image space using the sensor camera model state vector. The analysis is carried out in image gradient angle space for greater flexibility and reduced processing. Re-sampling the gradient angle space allows the classification process to work at a wider variety of target ranges. The target model database is built from an assortment of both target operating and background environmental conditions. Incremental classification is possible by applying the matching strategy at increasing target resolution levels that are either self or range closure induced. The first application of this process has been on thermal imagery. It can easily be extended to other image domains.
The first step in an automatic image target acquisition system is determining the location of candidate objects. Screening for targets must also be done within a tactical scenario timeframe. The screening process must only require a portion of the processing workload since other algorithms must execute in the same time frame. The detection of these candidate objects is allocated to two functions within the same algorithm. The first is a pre-screener and other is a clutter rejection component that will categorize the object nomination into target or non-target classes. This paper describes a screener that meets the necessary requirements for tactical operations. It uses the magnitude and direction of the image gradient. Locations are nominated by looking at local neighborhoods in this gradient space. Regions of interest are then selected and various features are extracted. These features are selected both for their information content and their ease of calculation. Using a Bayes approach, target candidates are selected as plausible targets of interest.
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