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An efficient modified fuzzy possibilistic c-means algorithm for segmenting color based hyperspectral images

机译:一种有效的改进的模糊可能性c均值算法,用于分割基于颜色的高光谱图像

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In computer vision, segmentation refers to the process of partitioning a digital image into multiple segments. Image segmentation is typically used to locate objects and boundaries in images. Image segmentation is the process of assigning a label to every pixel in an image such that pixel with the same label share contain visual characteristics. In this paper present a new approach for color based image segmentation by applying modified fuzzy possiblitic c-means algorithm. Normally, due to the progress in spatial resolution of satellite imagery. The methods of segment-based image analysis for generating and updating geographical information are being more and more important. So in this paper the main objective of this paper is to get a non-overlapping of image and a reliable output.
机译:在计算机视觉中,分段是指将数字图像划分为多个段的过程。图像分割通常用于在图像中定位对象和边界。图像分割是为图像中的每个像素分配标签,以使具有相同标签共享的像素包含视觉特征的过程。本文提出了一种新的基于颜色的图像分割方法,该方法应用了改进的模糊后验c均值算法。通常,由于卫星图像的空间分辨率的进步。用于生成和更新地理信息的基于片段的图像分析方法越来越重要。因此,本文的主要目的是获得图像的不重叠和可靠的输出。

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