1 April 1997 Multisensor integration and image recognition using Fuzzy Adaptive Resonance Theory
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Abstract
The main objective of this work was to investigate the use of 'sensor based real time decision and control technology' applied to actively control the arrestment of aircraft (manned or unmanned). The proposed method is to develop an adaptively controlled system that would locate the aircraft's extended tailhook, predict its position and speed at the time of arrestment, adjust an arresting end effector to actively mate with the arresting hook and remove the aircraft's kinetic energy, thus minimizing the arresting distance and impact stresses. The focus of the work presented in this paper was to explore the use of fuzzy adaptive resonance theorem (fuzzy art) neural network to form a MSI scheme which reduces image data to recognize incoming aircraft and extended tailhook. Using inputs from several image sources a single fused image was generated to give details about range and tailhook characteristics for an F18 naval aircraft. The idea is to partition an image into cells and evaluate each using fuzzy art. Once the incoming aircraft is located in a cell that subimage is again divided into smaller cells. This image is evaluated to locate various parts of the aircraft (i.e., wings, tail, tailhook, etc.). the cell that contains the tailhook provides resolved position information. Multiple images from separate sensors provides opportunity to generate range details overtime.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steven Michael Singer, Steven Michael Singer, } "Multisensor integration and image recognition using Fuzzy Adaptive Resonance Theory", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269770; https://doi.org/10.1117/12.269770
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