Edge detection is an indispensable part of image processing. In this paper, a novel edge detection method based on multiple features and fuzzy reasoning is proposed, in which the limitations of gradient-based edge detection methods and present fuzzy edge detection algorithms can be overcome. The new method selects trapezoid fuzzy membership functions, defines multiple features for each pixel fr...

Cellular Automata can be successfully applied in image processing. In this paper, we propose a new edge detection algorithm, based on cellular automata to extract edges of different types of images, using a totalistic transition rule. The metaheuristic PSO is used to find out the optimal and appropriate transition rules set of cellular automata for edge detection task. This combination increase...

Edge detection is a fundamental task in computer vision. In this assignment, I compared six different algorithms for edge detection, including Sobel, Canny [2], BEL [3], gPb [1], Sketch Token [5], and Structured Forest [4]. The Berkeley Segmentation Dataset (BSDS300) [6] and benchmark are used to compare the edge detectors. For image processing methods, Canny and Sobel, the precisions are below...

This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The propo...

EEcient edge detection algorithms such as Canny's fail near curve singularities. Moreover, the standard linking algorithms used on top of these detectors often fail because of instabilities in the tracking process (due to multiple responses to the same edge and interference of nearby edges). We propose a hierarchical approach to edge detection based on a graph stabilization method that allows b...

This paper proposed an edge detection using Skewed and Elongated Basis functions (SEBF). In an image, edges present at any directions and any scale. Common edge detection algorithms are single scale, and limited in directions. The proposed method efficiently removes these difficulties by using SEBF that are having multiscale and non separable properties. The two dimensional non separable proper...

A new method for evaluating edge detection algorithms is presented and applied to measure the relative performance of algorithms by Canny, Nalwa-Binford, Iverson-Zucker, Bergholm, and Rothwell. The basic measure of performance is a visual rating score which indicates the perceived quality of the edges for identifying an object. The process of evaluating edge detection algorithms with this perfo...