{"title":"Improved Algorithm about Subpixel Edge Detection Based on Zernike Moments and Three-Grayscale Pattern","authors":"Xiaoyue Zheng, Yuanwei Bi","doi":"10.1109/CISP.2009.5303388","DOIUrl":null,"url":null,"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","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"122 11","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 2nd International Congress on Image and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP.2009.5303388","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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