Histogram Features Extraction for Edge Detection Approach

An edge is where an image’s intensity values rapidly change from low to high-intensity values or vice versa. The edge itself is at the midpoint of this change. Edge detection remains a challenge in computer vision despite recent advances. It cannot be applied to an image with excessive brightness and contrast. This paper produces a new method based on the standard deviation histogram feature to reduce the onerousness. The proposed method aims to prepare the input image for the edge detection approaches by performing a histogram feature extraction. The main characteristics of the proposed approach are simplicity and functionality. The authors utilize twenty MATLAB standard images as well as ADNI brain images. The authors use the Canny edge detection method to defect edges from the proposed method. The authors use edge detection evaluation metrics such as Figure of Merit (FOM), Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) measures for evaluating and justifying edge quality. The experimental results show that the proposed method performs better in visual and statistical edge quality than both classical and fractional-order edge detection methods.