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Geometric and pattern recognition combined algorithms applied to digital image processing in aerial search and rescue applications.

机译:几何和模式识别相结合的算法应用于航空搜索和救援应用中的数字图像处理。

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摘要

The difficult task of generating an automated target detection system from aerial imagery can be achieved through different approaches. However, the robustness of any machine vision system lays on the implementation of vision algorithms. By combining geometric and pattern recognition algorithms, a more effective machine vision scheme is developed. Three modules-Color Identification (CIM), Color Matching (CMM), and Pattern Recognition (PRM)- work as main filters to process images and predict the location of targets based on a set templates. Multiple target detection is also accomplished by implementing a hide and seek method where all matching objects are scanned individually and isolated to keep count of the total amount of similar possible targets. Two different scenarios are analyzed in this process: Outback Challenge from 2012 Search and Rescue Competition and Bird counting where Cormorants and Common Murres interact in the same habitat. Results are analyzed in two different groups for each scenario. First group includes individual module analysis, while second group combines all three modules (CIM,CMM, and PRM) in every analysis.
机译:可以通过不同的方法来完成从航空影像生成自动目标检测系统的艰巨任务。但是,任何机器视觉系统的鲁棒性都取决于视觉算法的实现。通过结合几何和模式识别算法,开发了一种更有效的机器视觉方案。颜色识别(CIM),颜色匹配(CMM)和图案识别(PRM)这三个模块是主要过滤器,用于处理图像并根据一组模板预测目标的位置。多目标检测还可以通过实施隐藏和查找方法来实现,在该方法中,将单独扫描所有匹配的对象并进行隔离以保持对类似可能目标总数的计数。在此过程中,分析了两种不同的情况:2012年搜寻与救援比赛的内陆挑战赛以及counting和共同Murres在同一栖息地互动的鸟类计数。针对每种情况,将结果分为两个不同的组进行分析。第一组包括每个模块的分析,而第二组则在每个分析中合并所有三个模块(CIM,CMM和PRM)。

著录项

  • 作者单位

    Embry-Riddle Aeronautical University.;

  • 授予单位 Embry-Riddle Aeronautical University.;
  • 学科 Engineering Mechanical.
  • 学位 M.S.M.E.
  • 年度 2013
  • 页码 82 p.
  • 总页数 82
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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