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Optimizing feature selection strategy for adaptive object identification in a noisy environment

机译：噪声环境下自适应目标识别的特征选择策略优化

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摘要

We present the development of a multi-stage automatic target recognition (MS-ATR) system for computer vision in robotics. This paper discusses our work in optimizing the feature selection strategies of the MS-ATR system. Past implementations have utilized Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filtering as an initial feature selection method, and principal component analysis (PCA) as a feature extraction strategy before the classification stage. Recent work has been done in the implementation of a modified saliency algorithm as a feature selection method. Saliency is typically implemented as a "bottom-up" search process using visual sensory information such as color, intensity, and orientation to detect salient points in the imagery. It is a general saliency mapping algorithm that receives no input from the user on what is considered salient. We discuss here a modified saliency algorithm that accepts the guidance of target features in locating regions of interest (ROI). By introducing target related input parameters, saliency becomes more focused and task oriented. It is used as an initial stage for the fast ROI detection method. The ROIs are passed to the later stages for feature extraction and target identification process.
机译：我们介绍了用于机器人技术中计算机视觉的多阶段自动目标识别（MS-ATR）系统的开发。本文讨论了我们在优化MS-ATR系统的特征选择策略方面的工作。过去的实现方法是在最佳分类之前，将最佳权衡最大平均相关高度（OT-MACH）过滤用作初始特征选择方法，并将主成分分析（PCA）用作特征提取策略。在实现改进的显着性算法作为特征选择方法方面，最近已经开展了工作。显着性通常使用视觉感应信息（例如颜色，强度和方向）来检测图像中的显着点，从而实现为“自下而上”的搜索过程。它是一种通用的显着性映射算法，它不接收来自用户的关于显着性的任何输入。我们在这里讨论一种改进的显着性算法，该算法在定位感兴趣区域（ROI）时接受目标特征的指导。通过引入与目标相关的输入参数，显着性变得更加集中和面向任务。它用作快速ROI检测方法的初始阶段。 ROI传递到后期进行特征提取和目标识别过程。

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