Adaptive object detection from multisensor data
This paper focuses on developing self-adapting automatic object detection systems to achieve robust performance. Two general methodologies for performance improvement are first introduced. They are based on optimization of parameters of an algorithm and adaptation of the input to an algorithm. Different modified Hebbian learning rules are used to build adaptive feature extractors which transform the input data into a desired form for a given object detection algorithm. To show its feasibility, input adaptors for object detection are designed and tested using multisensor data including SAR, FLIR, and color images. Test results are presented and discussed in the paper.