Automatic target detection (ATR) generally refers to the localization of potential targets by computer processing of data from a variety of sensors. Automatic detection is applicable for data reduction purposes in the reconnaissance domain and is therefore aimed at reducing the workload on human operators. ATR covers activities such as the localization of individual objects in large areas or volumes for assessing the battlefield simulation. An increase of reliability and efficiency of the overall reconnaissance process is expected. The results of automatic image evaluation are offered to the image analyst as hypotheses. In this paper cluttered images from an infrared sensor are analyzed with the aim of finding Regions of Interest (ROIs), where hints for man-made objects have to be found. This analysis uses collateral data from acquisition time and location (e.g. day time, weather condition, resolution, sensor specification and orientation etc.). The assumed target size in the image is also compared by using collateral data. Based on the collateral data, the algorithm adjusts its parameters in order to find ROIs and to detect targets. Low contrast conditions can be successfully tackled if the directions of the grey value gradient are considered, which are nearly independent of the contrast. Blobs are generated by applying adaptive thresholds in the ROIs. Here the evaluation of histograms is very important for the extraction of structured features. The height, aspect angle, and camera parameters are approximately known for an estimation of target sizes in the image domain out of the collateral data.
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