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24 November 2009 Fuzzy synthesis evaluation for image target recognition performance
Yong Zhang, TaiBin Wu
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Proceedings Volume 7513, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology; 75130V (2009) https://doi.org/10.1117/12.835966
Event: International Conference on Optical Instrumentation and Technology, 2009, Shanghai, China
Abstract
With the rapid development of optoelectronic tracking and measurement technology, the testing and evaluation for the tracking system and its inner algorithms are urgently demanded. Automobile target recognition(ATR) technology for the image is a key part of the tracking system based on the image, and develops advanced and fast, which makes the performance evaluation difficult and complex. There is not a reliable and effective evaluation method adaptable to the developing technology. Therefore, a fuzzy synthesis evaluation method for ATR system or its detection and recognition algorithms group was proposed. The evaluation indexes were selected and designed, which weights were calculated by the direct method, W-road method and the change weight method. The simulation testing conditions, the size and the hypothesis test methods of the statistic swatch were discussed. The mean, the covariance dependency and the distribution indexes of the probability of detection(Pd) were effectively tested. The statistic ranges corresponding to evaluation ranks of these indexes were established. Finally, the simple model and the division model of the fuzzy synthesis evaluation algorithm were discussed. Tests show, this method is valuable for getting the occurrence probabilities of the different performance racks of the system or the algorithms group corresponding to vary environment levels.
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Yong Zhang and TaiBin Wu "Fuzzy synthesis evaluation for image target recognition performance", Proc. SPIE 7513, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology, 75130V (24 November 2009); https://doi.org/10.1117/12.835966
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KEYWORDS
Detection and tracking algorithms
Fuzzy logic
Automatic target recognition
Target recognition
Signal to noise ratio
Image analysis
Algorithm development
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