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首页> 外文会议>Conference on Passive Millimeter-Wave Imaging Technology VI and Radar Sensor Technology VII Apr 23-24, 2003 Orlando, Florida, USA >Performance Modeling of Vote-based Object Recognition
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Performance Modeling of Vote-based Object Recognition

机译：基于投票的对象识别性能建模

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摘要

The focus of this paper is on predicting the bounds on performance of a vote-based object recognition system, when the features are distorted by uncertainty in both feature locations and magnitudes, by occlusion and by clutter. A method is presented to calculate lower and upper bound predictions of the probability that objects with various levels of distorted features will be recognized correctly. The prediction method takes model similarity into account, so that when models of objects are more similar to each other, then the probability of correct recognition is lower. The effectiveness of the prediction method is validated in a synthetic aperture radar (SAR) automatic target recognition (ATR) application using MSTAR public data, which are obtained under different depression angles, object configurations and object articulations. Experiments show the performance improvement that can obtained by considering the feature magnitudes, compared to a previous performance prediction method that only considered the locations of features. In addition, the predicted performance is compared with actual performance of a vote-based SAR recognition system using the same SAR scatterer location and magnitude features.
机译：本文的重点是预测当基于特征位置和大小的不确定性，遮挡和混乱而使特征变形时，基于投票的对象识别系统的性能界限。提出了一种方法，用于计算将正确识别具有各种变形特征级别的对象的概率的上下限预测。该预测方法考虑了模型的相似性，因此，当对象的模型彼此更相似时，正确识别的可能性就会降低。该预测方法的有效性在使用MSTAR公共数据的合成孔径雷达（SAR）自动目标识别（ATR）应用中得到了验证，该数据是在不同的俯角，物体配置和物体关节下获得的。实验表明，与以前只考虑特征位置的性能预测方法相比，通过考虑特征量可以提高性能。此外，将预测性能与使用相同SAR散射体位置和幅度特征的基于投票的SAR识别系统的实际性能进行比较。

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