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首页> 外文会议>The 20th AIPR Workshop: Computer Vision Applications--Meeting the Challenges >Highly automated nonparametric statistical learning for autonomous target recognition
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Highly automated nonparametric statistical learning for autonomous target recognition

机译：高度自动化的非参数统计学习，可自动识别目标

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摘要

Abstract: Image pattern recognition is presented as three sequential tasks: feature extraction, object plausibility estimation (determining class likelihoods), and decision processing. Several data-driven techniques yield discriminant functions to produce object plausibility estimates from image features, including traditional statistical methods and neural network approaches. A statistical learning algorithm which integrates multiple-regression algorithms, functional networking strategies, and a statistical modeling criterion is presented. It provides a non-parametric learning algorithm for the synthesis of discriminant functions. Image understanding tasks such as object plausibility estimation require robust modeling techniques to deal with the uncertainty prevalent in real-world data. Specifically, these complex tasks require robust and cost-effective techniques to successfully integrate multi-source information. AbTech and others have shown that implementation of the statistical learning concepts discussed provide a modeling approach ideal for information fusion tasks such as autonomous object recognition for tactical targets and space-based assets. The results of using this approach to develop a prototype aircraft recognition system is presented.!19
机译：摘要：图像模式识别分为三个顺序任务：特征提取，目标似然估计（确定类似然性）和决策处理。几种数据驱动技术产生判别函数，以从图像特征生成对象似然性估计，包括传统的统计方法和神经网络方法。提出了一种将多元回归算法，功能网络策略和统计建模准则相结合的统计学习算法。它为判别函数的合成提供了一种非参数学习算法。诸如对象似然性估计之类的图像理解任务需要强大的建模技术来处理现实世界数据中普遍存在的不确定性。具体而言，这些复杂的任务需要强大且具有成本效益的技术来成功集成多源信息。 AbTech和其他机构已经表明，所讨论的统计学习概念的实施为信息融合任务（例如战术目标和天基资产的自主对象识别）的理想提供了一种建模方法。介绍了使用这种方法开发原型飞机识别系统的结果。！19

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