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首页> 外文期刊>International Journal of Computer Applications in Technology >An image segmentation algorithm based on combination of slope width reduction and cross cortical model
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An image segmentation algorithm based on combination of slope width reduction and cross cortical model

机译:一种基于斜率宽度减小和交叉皮质模型组合的图像分割算法

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摘要

An image segmentation algorithm based on the ramp width reduction combined with an Intersecting Cortical Model (ICM) is proposed against problems that ICM in the segmentation of image with weak edge produces geometric distortion. By virtue of prewitt boundary operator and edge ramp model, the algorithm defines the objective edge point, adjusts the grey level of edge pixel, and reduces the width of image edge. On this basis, the paper uses 2D histogram to expand the cross entropy to 2D space so as to obtain the optical segmentation threshold of ICM. The experiment indicates that the algorithm not only overcomes the impact of edge blur and segment the image with weak edge accurately, but also improves the processing speed greatly.
机译:提出了一种基于斜坡宽度减小与交叉皮质模型(ICM)组合的图像分割算法,以反对ICM在具有弱边缘的图像分割中的问题产生几何失真。 借助于PREWITT边界操作员和边缘斜坡模型,算法定义了目标边缘点,调整边缘像素的灰度级,并降低图像边缘的宽度。 在此基础上,本文使用2D直方图将交叉熵扩展到2D空间,以便获得ICM的光学分割阈值。 实验表明,该算法不仅克服了边缘模糊的影响并准确地将图像分段为弱边缘,但也大大提高了处理速度。

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