Adaptive object detection based on modified Hebbian learning
This paper focuses on the issue of developing self-adapting automatic object detection systems for improving their performance. Two general methodologies for performance improvement are first introduced. They are based on parameter optimizing and input adapting. Different modified Hebbian learning rules are developed to build adaptive, feature extractors which transform the input data into a desired form for a given algorithm. To show its feasibility, an input adaptor for object detection is designed as an example and tested using multisensor data (optical, SAR, and FLIR). Test results are presented and discussed in the paper.