Edge detection is one of the most commonly used procedures in digital image processing. In the last 30-40 years, many methods and algorithms for edge detection have been proposed. This article presents an overview of edge detection methods, the methods are divided according to the applied basic principles. Next, the measures and image database used for edge detectors performance quantification are described. Ordinary users as well as authors proposing new edge detectors often use Matlab function without understanding it in details. Therefore, one section is devoted to some of Matlab function parameters that affect the final result. Finally, the latest trends in edge detection are listed. Picture Lena and two images from Berkeley segmentation data set (BSDS500) are used for edge detection methods comparison.
Image processing includes many various procedures and one of the mostly used is edge detection. Sometimes is desirable to improve precision of edge detector to sub-pixel range. In our paper we deal with precise localization of edge which is moving during the exposure time. For such the edges we tested three edge detectors with sub-pixel precision in 1-D images: algorithm that uses function erf to approximate real image samples and two edge detectors based on gray level and spatial moments of the image function. This paper presents results of simulations with noisy images, we have chosen the standard deviation of error of edge position as the precision criterion.
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