초록 ▼

A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level. The system extracts information derived from each sensor source to obtain detections and their feature attributes. At leas

A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level. The system extracts information derived from each sensor source to obtain detections and their feature attributes. At least two processing streams, one for each sensor source, are provided for converting the detections and their feature attributes into hypotheses on identity and class of detected objects. The detections are shared and combined between the two processing streams using hierarchical information fusion algorithms to determine which ones of the hypotheses on identity and class of detected objects have sufficient probabilities for classifying the information.

대표청구항 ▼

[ We claim:] [8.] A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, wherein the method is adapted for a hierarchical real-time SAR/FLIR air-to-ground targeting object recog

[ We claim:] [8.] A hierarchical object recognition method for aggregation, interpretation and classification of information from multiple sensor sources on the detection feature attribute level, wherein the method is adapted for a hierarchical real-time SAR/FLIR air-to-ground targeting object recognition system for automated target classification on the detection feature attribute level, comprising the following steps:extracting information derived from each said sensor source to obtain detections and their feature attributes;providing at least two processing streams, one for each said sensor source, for converting said detections and their feature attributes into hypotheses on identity and class of detected objects;providing a plurality of fusion engines having hierarchical information fusion algorithms for fusing said hypotheses at different feature levels for each said processing streams; andsharing and combining the detection feature attributes and hypotheses about the detections between the two processing streams using hierarchical information fusion algorithms to determine which ones of said hypotheses on identity and class of detected objects have the highest probabilities, and at least one of said fusion engines providing said fused hypotheses of said one fusion engine to said processing streams for determining hypotheses based on said feature attributes.