发表机构
Airbus（空中客车公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图像中孤立像素检测问题，提出一种基于对比敏感感受野的非线性神经元模型，无需阈值，适用于二值及灰度图像，鲁棒高效。
AI 中文摘要
识别孤立点在图像处理应用中非常重要，例如医学成像、天文学和质量控制管理。其他领域，如网络安全，也提出了可以表述为图像处理问题的挑战。一个特别值得关注的例子是在空间组织的网络中识别异常单个节点，其中不同区域的节点组共享相似的特征值。该任务可能涉及二值图像和更复杂的灰度图像。然而，现有方法面临局限性：模板匹配对于灰度图像不可行，而基于二阶导数的方法对噪声高度敏感，并且需要用户指定的阈值。为了克服这些问题，提出了一种新方法，用于检测图像中有意义的单像素偏差。该方法修改并扩展了一个最初用于异常检测的神经元模型，使其在包含兴奋性和抑制性区域的空间直径受限的感受野上运行。其结果是该方法无需用户指定的阈值和参数，可应用于二值图像和灰度图像，提供有效、鲁棒且高效的解决方案。
英文摘要
Identifying isolated points is important in image processing applications such as medical imaging, astronomy and quality control management. Other domains, such as cybersecurity, also present challenges that can be framed as image processing problems. One example of particular interest is the identification of anomalous single nodes in spatially organised networks where groups of nodes in different regions share similar feature values. This task can involve both binary and more complex grayscale images. However, existing methods face limitations: template matching is infeasible for grayscale images, while 2nd order derivative based methods are highly sensitive to noise and require user-specified thresholds. To overcome these issues, a novel method is proposed for detecting meaningful single-pixel deviations in images. This approach modifies and extends a neuron model, originally designed for anomaly detection, to operate on spatially diameter limited receptive fields that incorporate excitatory and inhibitory regions. The result is a method that is free from user-specified thresholds and parameters, and can be applied to both binary and grayscale images, providing an effective, robust and efficient solution.
