Evaluation of the best Edge Filters in Image Processing Based on the Color Fabric Texture

Abstract

With the development and complexity of life, the need to improve images appeared, especially when used in the fields of life, including industry and its branches, which affect the life of the citizen, such as the manufacture of fabrics. Which requires precision in the production of these fabrics from the colors and pattern of the fabric. Edge identification is the first step in many digital image-processing applications. Edge identification greatly decreases the data quantity, undesirable filters or unimportant data and provides the important data into the image. There are some issues such as false edge identification, noise issues, low contrast and other edge issues. This paper presents a practical study to compare different edge detectors to determine which edge detector achieves better results, which in turn reflects the best pattern in the fabric. These detectors are Canny, Roberts, Laplace and Gabor. A database of thirty color JPG images collected from the Internet was arranged and a quality scale was used to compare filter detectors. The system MATLAB2020 was used to program the proposed work. The results enhancement was measured by the quality coefficient. This coefficient estimated as follows for Roberts filter (44.27-51.09). Gabor filter (43.46-44.48) and Laplace filter (44.71-5.40). Finally, the quality coefficient for Canny filter equals (44.46-52.05). Therefore, it turns out that the Gabor filter is the best of these filters in defining the edges that were used in defining the pattern.