Abstract
Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. It's also the most important parts of image processing, especially in determining the image quality. There are many different techniques to evaluate the quality of the image. The most commonly used technique is pixel based difference measures which include peak signal to noise ratio (PSNR), signal to noise ratio (SNR), mean square error (MSE), similarity structure index mean (SSIM) and normalized absolute error (NAE).... etc. This paper study and detect the edges using extended difference of Gaussian filter applied on many of different images with different sizes, then measure the quality images using the PSNR, MSE, NAE and the time in seconds.
Highlights
Machine vision has developed into a critical field embracing a wide range of applications, comprehensive robot assembly [1], traffic monitoring and control [2], biometric measurement [3], surveillance [4], analysis of remotely sensed images [5], automated inspection [6], vehicle guidance [7] and signature verification [8]
In order to test our method in this paper and compare it with the other classical methods, many of different test images with different sizes and resolutions are detected by canny, log, zerocross, sobel, prewitt and Roberts and XDOG method
We can observe from table 2 and figure 9 XDOG approach is the perfect compared to the other classical methods where it has the lowest values
Summary
Machine vision has developed into a critical field embracing a wide range of applications, comprehensive robot assembly [1], traffic monitoring and control [2], biometric measurement [3], surveillance [4], analysis of remotely sensed images [5], automated inspection [6], vehicle guidance [7] and signature verification [8]. The early stages of vision, handling distinguish highlights in images that are important to estimate the Properties and structure of elements in the scene [9] It usually happens in the limit between two unique areas in the image
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