Improved Algorithm about Subpixel Edge Detection Based on Zernike Moments and Three-Grayscale Pattern

Xiaoyue Zheng, Yuanwei Bi
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引用次数: 6

Abstract

The principle of Zernike moments and the method of sub-pixel edge detection based on Zernike moments were introduced in this paper. With the consideration of the limitation of the subpixel edge detection algorithm by Ghosal, such as the lower location precision of the edge and the extracted wider edge than that of the original image, an improved algorithm was proposed. A new pattern with three-grayscale for edge detection was put forward to detect the edge calculating masks of size seven multiply seven to get difference order Zernike moments. Additionally, experiments were designed and implemented. The experiment results show that accuracy of the improved algorithm is higher than that obtained from using Ghosal algorithm. This Edge detection is a problem of fundamental importance in image analysis. Edges characterize boundaries are therefore a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts - a jump in intensity from one pixel to the next. Edge detecting an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. There are many ways to perform edge detection. However, the majority of different methods may be grouped into two categories, gradient and Laplacian. The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image. The Laplacian method searches for zero crossings in the second derivative of the image to find edges. According to those methods, the several operators were proposed such as Roberts operator, Laplacian operator and Canny operator. Roberts operator directly calculates the image difference, so it can't suppress the noise; Laplacian operator has good effect in detecting Roof-edge, but it is sensitive for noise and low accuracy. The Canny operator works in a multi- stage process (1). First of all the image is smoothed by Gaussian convolution. Then a simple 2-D first derivative operator is applied to the smoothed image to highlight regions of the image with high first spatial derivatives. Edges give rise to ridges in the gradient magnitude image. The algorithm then
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基于Zernike矩和三灰度模式的亚像素边缘检测改进算法
介绍了泽尼克矩的基本原理和基于泽尼克矩的亚像素边缘检测方法。针对Ghosal亚像素边缘检测算法的局限性,如边缘定位精度较低、提取的边缘较原始图像宽等,提出了一种改进算法。提出了一种新的三灰度边缘检测模式,检测大小为7 × 7的边缘计算掩模,得到差阶泽尼克矩。此外,还设计并实施了实验。实验结果表明,改进算法的精度高于Ghosal算法。在图像分析中,边缘检测是一个非常重要的问题。因此,边缘表征边界是图像处理中一个基本的重要问题。图像中的边缘是具有强烈强度对比的区域——从一个像素到下一个像素的强度跳跃。图像边缘检测可以显著减少数据量,过滤掉无用信息,同时保留图像中的重要结构属性。有许多方法可以执行边缘检测。然而,大多数不同的方法可以分为两类,梯度和拉普拉斯。梯度法通过在图像的一阶导数中寻找最大值和最小值来检测边缘。拉普拉斯方法在图像的二阶导数中寻找零交叉点来寻找边缘。在此基础上,提出了Roberts算子、Laplacian算子和Canny算子。罗伯茨算子直接计算图像差值,无法抑制噪声;拉普拉斯算子检测屋顶边缘效果好,但对噪声敏感,精度低。Canny算子是一个多阶段的过程(1)。首先对图像进行高斯卷积平滑处理。然后对平滑后的图像应用简单的二维一阶导数算子,突出显示高一阶空间导数的图像区域。在梯度幅度图像中,边缘产生脊。然后是算法
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