Bonfring International Journal of Advances in Image Processing, Vol. 4, No. 1, December 2014 1 Abstract--- Detecting the edges of objects within images is a critical task for quality image processing. This paper proposes an edge detection operator based on the combination of fuzzy gradient morphology and Sobel operator. When we use traditional detection operators to detect the edge of an object in an image, we get lots of noisy Points. In this paper, we demonstrate a technique in which we preprocess an image with Sobel operator and then apply gradient morphology. This method effectively removes the noise and gives good detail image edge detection. We evaluate the method quantitatively and compare it to classical morphological method. Our fuzzy based edge segmentation method performs better than the classical edge detectors. Since the proposed methods are based on fuzzy morphological operations, these are efficient and enhanced. Keywords--- Fuzzy, Mathematical Morphology, Segmentation, Sobel Operator I. INTRODUCTION ODAY edge detection is a very important area in the field of image processing and computer vision. It also plays an important role for image segmentation and object recognition. Edge detection is the process to detect the important features of image. Here, features mean the properties of image like discontinuities in physical and geometric characteristics of an image or abrupt variation in its intensity, Wang et al.,[1]. The quality of edges is affected by the presence of objects in similar illumination, noise and density of edges,Nadarnejat [14]. The variation in characteristics can lead to the variation in gray level of the image. Edge detection is therefore considered as an important step for facilitating higher level image analysis and processing, Mittal and Batra [2] and Gonzalez et al., [3]. Conventionally, edge was detected using gradient algorithms like Sobel , Prewitt andLaplacian of Gaussian operator,Huertas and Medioni [4], all of which belong to the high pass filtering methods. Another important gradient based edge detection method is Canny algorithm which solves an optimization problem to detect the edges, Canny [5]. The tradeoff between detection and location of edge pixels make a problem in accuracy,Wang et al., [1]. By changing threshold values, edge detection rate increases, but the accuracy of edge Dillip Ranjan Nayak, Asst. Professor, Computer Science & Engineering, Govt. College of Engineering, Kalahandi, Bhawanipatna , Odisha, India. E- mail: dillip678in@yahoo.co.in DOI: 10.9756/BIJAIP.10357 location decreases. Because of noise, low contrast and some other factors, edge detection methods cannot give satisfactory results, Marr and Hildreth [6]. There are some edge detection algorithms in frequency domain, Musevi-Niya andAghagolzadeh[7]where over or under edge based segmentation occurs. As the performance of classical edge detectors degrades with noise, morphological edge detector has been studied, Lee et al., [8].It was introduced by Matheron as a technique for analyzing geometric structure of metallic solids. It was extended to image analysis,Serra [9].Mathematical morphology is a new mathematical theory which can be used to process and analyze the images, Maragos [10],Richard [11] and Jean [12]. It provides an alternative approach to image processing based on shape,concept stemmed from set theory, Serra [9]. In the mathematical morphology theory, images are treated as sets, and morphological transformations which are derived from Minkowski addition and subtraction are defined to extract features in images. A fundamental advantage of mathematical morphology applied to image is that it is intuitive since it works directly on spatial domain. The idea of fuzzy logic is to extend the binary (TRUE or FALSE) computer model with some uncertainty or blur.Many of our sensory impressions are qualitative and imprecise and, therefore, unsuitable for accurate measurements. For example, a pixel is perceived as “dark”, “bright” or even “very bright”, but not as pixels with the gray scale value “231”. Fuzzy quantities are based mathematically on the fuzzy set theory, in which the belongingness of an element to a set of elements is not restricted to the absolute states TRUE (1) or FALSE (0), but continuously defined within the entire interval [0..1]. In general mathematical morphology, operation definitions are similar to set theory and set operation definitions. For this reason fuzzy set theory is easily applied to the mathematical morphology, Nadarnejat [15]. Mathematical morphology is a collection of operations which produces useful outcomes in image processing area. It is completely based on set theory. For this reason all of the operations in morphology are defined on the simple set operation rules to apply them on image pixels. The basic mathematical morphological operators are dilation and erosion and the other morphological operations are the synthesization of the two basic operations.The objective of this paper is to present the hybrid approach for edge detection. Under this technique, edge detection is performed in two phases. In first phase, sobel algorithm is applied for image smoothing and in second phase fuzzy morphology is applied to detect actual edges. Fuzzy morphology is a wonderful tool for edge detection. Fuzzy mathematical morphology is beneficial for detection of the Edge Detection Using Fuzzy Double Gradient Morphology Dillip Ranjan Nayak T ISSN: 2277-503X| © 2014 Bonfring