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Roads and pipes detection within LADAR intensity images through fuzzy techniques

机译:通过模糊技术在拉达强度图像内检测道路和管道

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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, errors made in this stage will impact all higher level activities. This paper extends our earlier and on-going work in image labeled segmentation wherein methods which incorporate the uncertainty of object and region definition, and the faithfullness of the features to represent various objects, are considered in the sense of Pal, and Bezdek and Sutton. To apply our previous system and framework to LADAR 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:图像处理和计算机视觉的许多方面存在不确定性。模糊集理论和模糊逻辑非常适合处理这种不确定性。图像分割是许多计算机视觉算法中的一个重要步骤,在此阶段制作的错误将影响所有更高的级别活动。本文在图像标记的分割中延伸了我们之前和持续的工作,其中包含对象和区域定义的不确定性的方法,以及特征来代表各种物体的信徒,在PAL和Bezdek和Sutton的意义上被认为是考虑的。要将我们以前的系统和框架应用于Ladar Images,它已被修改和改进。我们引入了新的模糊形态结构元素,以消除“垂直噪声”和假检测,并且我们改进了由于这些图像内出现的元素的宽范围和变化而在处理问题的直方图的分割。

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