Color Image Edge Detection Using Fuzzy Membership Functions
E. Boopathi Kumar, V. Thiagarasu · Zenodo (CERN European Organization for Nuclear Research) · 2017
Digital image processing is widely used in many research oriented fields. Edge detection method is one of the important techniques in Image Segmentation, which is used to find out the objects in the input image in exact manner. An edge is the boundary between an object and background and it indicates the boundary between overlapping objects. One of the most commonly used operation analysis is edge detection, which is used for enhancing and detecting edges in the image. It removes useless data, noise and frequencies while preserving the important structural properties in an image. Fuzzy Logic techniques have been used in image understanding applications such as detection of edges, feature extraction, classification, and clustering. Fuzzy logic possess the ability to mimic the human mind to employ modes of reasoning that are approximate rather than exact form effectively. This paper discuss about RGB color model and fuzzy membership functions method and particularly explain about the usage of fuzzy membership functions which are used to create different combination of mask with some sort of rules based on RGB channel extraction to scan the separated channel image and include Threshold and filtering concepts for further to produce the output image in well enhanced way.