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Roads and pipes detection within LADAR intensity images through fuzzy techniques

机译:通过模糊技术在LADAR强度图像中检测道路和管道

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There is uncertainty in many aspects of image processing and computer vision. Fuzzy set theory and fuzzy logic are ideally suited for dealing with such uncertainty. Image segmentation is an important step in many computer vision algorithms, and errors made in this stage will impact all higher-level activities. This paper extends our earlier and on-going work in image-labeled segmentation (E. Montseny and P. Sobrevilla, 1998), wherein methods which incorporate the uncertainty of object and region definition and the faithfulness of the features to represent various objects were considered. To apply our previous system and framework to LADAR (LAser raDAR) images, it has been modified and improved. We have introduced new fuzzy morphological structural elements to eliminate "vertical noise" and false detections, and we have improved the segmentation of the histogram for dealing with problems due to the wide range and variability of gray levels of the elements appearing within these images.
机译:在图像处理和计算机视觉的许多方面都存在不确定性。模糊集理论和模糊逻辑非常适合处理此类不确定性。图像分割是许多计算机视觉算法中的重要步骤,在此阶段发生的错误将影响所有更高级别的活动。本文扩展了我们先前和正在进行的图像标记分割的工作(E. Montseny和P. Sobrevilla,1998),其中考虑了结合了对象和区域定义的不确定性以及代表各个对象的特征的真实性的方法。 。为了将我们先前的系统和框架应用于LADAR(LAser raDAR)图像,已对其进行了修改和改进。我们已经引入了新的模糊形态学结构元素,以消除“垂直噪声”和错误检测,并且由于出现在这些图像中的元素的灰度级范围广且变化大,我们改进了直方图的分割以解决问题。

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