7 June 2017 Applying self-structured data learning algorithm to aerial infrared and visual images
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Previously, we proposed and implemented a Self-structuring Data Learning Algorithm. This realized software package and the concept are still progressing. Earlier, it was tested with synthetic data and exhibited interesting results. The objectives of this paper are testing the algorithm with raw infrared and visual images and updating the algorithm as required. We first performed registration transformation and detection from the images with an existing software package. We then registered the detections with the registration transformations from both infrared and visual images. The registered detections were delivered to the algorithm for target detection and tracking without modification. Results revealed inability to handle very noisy infrared image features. To overcome this problem, we developed multiscale grid processing to improve detection classification in the algorithm. This updated algorithm shows much better target detection and tracking with the real-world data. More algorithm enhancements are in work such as incorporating pattern recognition, classification, and fusion.
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Jenfeng Sam Li, Jenfeng Sam Li, Igor Ternovskiy, Igor Ternovskiy, James Graham, James Graham, Roman Ilin, Roman Ilin, } "Applying self-structured data learning algorithm to aerial infrared and visual images", Proc. SPIE 10185, Cyber Sensing 2017, 101850L (7 June 2017); doi: 10.1117/12.2266686; https://doi.org/10.1117/12.2266686

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