Test based on a normal score for efficient edge detection in image

The current paper proposes an efficient method for edge detection in original and noisy images using Waerden's statistic. Edges represent a significant amount of information on an image. For example, edges reveal the location of objects, their shape and size, and something about their texture. Since edges represent where the intensity of an image moves from a low value to a high value or vice versa, edge detection is often the first step in image segmentation. As a field of image analysis, image segmentation groups pixels into regions to determine the image composition. Therefore, the current paper describes the nonparametric Wilcoxon test and parametric T test based on statistical hypothesis testing for edge detection. Here, the threshold is determined by specifying a significance level, whereas Bovik, Huang, and Munson considered a range of possible test statistic values for the threshold. In the current study, the test statistic is calculated based on pixel gray levels obtained using an edge-height parameter and compared with the threshold determined by a significance level. Experiments were conducted to evaluate the performance of these methods in both original and noisy images. As a result, the Wilcoxon and T test was found to be sensitive to a noisy image, whereas the proposed Waerden test was robust in both noisy and noise-free images under α=0.0005. Furthermore, when compared with Sobel, LoG, and Canny operators, the proposed Waerden test was also more effective in both noisy and noise-free images.