Threshold Based Edge Detection Algorithm

Edge detection is one of the most commonly used operations in image processing and pattern recognition. Edge detecting in an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. In this paper, edge detection methods such as Sobel, Prewitt, Robert, Canny, Laplacian of Gaussian (LOG), Expectation-Maximization (EM) algorithm, OSTU and Genetic algorithms are also used for segmenting. A new edge detection technique is proposed which detects the sharp and accurate edges that are not possible with the existing techniques. This implemented edge detection technique will be improved by combining it with other types of filters namely Weiner, STD, Hormonic, Geometric filters to remove the noise from the image. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results with different threshold values for given input image which ranges between 0 and 1. When the threshold value is 0.68 it is noticed that the sharp and accurate edges are detected.