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Neuronal Edge Detection with Median Filtering and Gradient Sharpening

机译:具有中值滤波和梯度锐化的神经元边缘检测

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摘要

Edge detection is the process of finding out edge of the objects from images. It is the fundamental of pattern recognition in digital image processing field, and it plays an important role in the application of computer vision. A method for edge detection based on median filtering and gradient sharpening was proposed in this paper. At first median filtering was used to remove the noise because it can protect the edge of the image at the same time. Secondly, gradient sharpening was employed to enhance the strength of the edge pixels, and then the initial edge of the image was obtained by simple binarization. The system used a connected component labeling method to remove the residues black block within the enclosed boundaries of the image. By comparing the results of the experiment with the manual extraction results and error analysis, the shortcoming of this algorithm was found for future work. Practice has proved that the edge detection method of this paper has a good effect to speckle images.
机译:边缘检测是从图像中查找对象的边缘的过程。它是数字图像处理领域的模式识别的基础,并且在计算机视觉中起着重要作用。本文提出了一种基于中值滤波和梯度锐化的边缘检测方法。在第一中间滤波中用于消除噪声,因为它可以同时保护图像的边缘。其次,采用梯度锐化来增强边缘像素的强度,然后通过简单二值化获得图像的初始边缘。该系统使用连接的部件标记方法来在图像的封闭边界内移除残留黑块。通过将实验结果与手动提取结果和误差分析进行比较,发现该算法的缺点是未来的工作。实践证明,本文的边缘检测方法对斑点图像具有良好的效果。

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