Method to aid object detection in images by incorporating contextual information

Automated object detector (AOD) systems use computer-based algorithms to detect objects of interest in an image. AODs typically measure how well each piece of an image represents a known object, then highlight those pieces where the measure exceeds some threshold. In theory, an AOD can scan a complex image to identify objects better and faster than a human can. In practice however, the simple comparisons performed by AODs are not very robust, and to avoid missing valid objects, they often detect questionable objects, resulting in false detections. A better approach, as described below, would be to encode, either manually via direct human input or automatically by a computer system, available contextual information concerning an image in order to filter out false alarms while increasing the probability that the AOD correctly detects valid objects. There is also the need to combine contextual information with an image to aid the human in detecting objects of interest when no AOD is available. The invention uses fuzzy logic and/or probability distributions to automatically calculate and display the effects of contextual information on the confidence that an object in an image is an object of interest. The goal is to assist in determining the location and type of target objects of interest in that imagery. The imagery can come from any kind of imaging sensor or can be non-sensor imagery (e.g., two-and three-dimensional maps), and can be live or archived imagery. The locations of context objects can be provided by a human or a computer. The resulting set of data, including the original imagery, the locations of context objects, any results from AOD, and predictions about target object type and location, can be combined into a display that helps a human better understand where target object appears in the imagery.