Study and comparison of various edge detection

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

An edge detection is the process of identifying and locating the discontinuities in an image. Hence, the process of an edge detection is one of the step-in image analyses and it is the key for solving many complex problems. Edge detection is a basic tool which can be used for the image processing applications to obtain the information from frames to extraction the feature and performing the segmentation process of an object. The edge detection used for object recognition, segmentation of an image, data compression and so on. Edge detection is one of the familiar methods for transforming an original image into edge image which can gain the benefits from changing the grey tones in an image. In this research paper, three edge detection algorithms namely Prewitt edge detection, Robert edge detection algorithm and Sobel edge detection algorithm are used to extract edges from the two type of images which is used to detect the edge of an image. Performance factors are analysed namely accuracy and speed are used to find out which algorithm works better. From the experimental results, it is observed that the Sobel edge detection algorithm works better than other two edge detection algorithms.

2002

Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper introduces a new classification of most important and commonly used edge detection algorithms, namely ISEF, Canny, Marr-Hildreth, Sobel, Kirsch, Lapla1 and Lapla2. Five categories are included in our classification, and then advantages and disadvantages of some available algorithms within this category are discussed. A representative group containing the above seven algorithms are the implemented in C++ and compared subjectively, using 30 images out of 100 images. Two sets of images resulting from the application of those algorithms are then presented. It is shown that under noisy conditions, ISEF, Canny, Marr-Hildreth, Kirsch, Sobel, Lapla2, Lapla1 exhibit better performance, respectively.

In the field of image processing, edge detection is an important step for extracting relevant and meaningful information from digital images. The main goal of edge detection techniques is to obtain and detect thin edges of the objects present in the image, so that the result is more suitable for further processing and analysis such as boundary detection, image segmentation, motion detection/estimation, texture analysis, object identification, feature detection, implementing various transformations and so on. We tested six edge detection algorithms that use different methods for detecting edges and compared their results under a variety of situations to determine a generally preferable technique under different sets of conditions. This data could then be used to create a multi-edge-detector system, which analyses the scene and runs the edge detector best suited for the current set of data. For each of these edge detectors we considered two different ways of implementation, the one using intensity only and the other coupling to it, the colour information. We also considered one additional edge detector which takes a different philosophy to edge detection. Rather than trying to find the ideal edge detector to apply to traditional photographs, it would be more efficient to merely change the method of photography to one which is more conducive to edge detection. It makes use of a camera that takes multiple images in rapid succession under different lighting conditions. It has been observed that the Canny"s edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions Canny, LoG(Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively. It has been observed that Canny"s edge detection algorithm is computationally more expensive compared to LoG(Laplacian of Gaussian), Sobel, Prewitt and Robert"s cross operator.

2012

Edge detection is one of the most commonly used operations in image analysis and is also an essential pre-processing step in image segmentation. An edge is the boundary between an object and the background, and indicates the boundary between adjacent parts of image and overlapping objects. Here we are reviewing several techniques for edge detection like Sobel operator technique, Prewitt technique, Fuzzy Edge Detection technique, Canny technique, Roberts technique and Morphology based multi structure elements edge detection technique.

—This paper presents an effective comparison between various edge detection techniques. Edges represent the object boundaries and this way they are crucial for filtering of unnecessary data.This is the reason behind edge detection being an essential component in many computer image processing subfields such as classification, feature extraction, pattern recognition etc.We compare Morphological Gradient Edge Detector, Sobel edge detector and Laplacian of Gaussian edge detector. It is found that Sobel filter exhibits better results thanothers in terms of PSNR values.

SEG Technical Program Expanded Abstracts 2003, 2003

Detection of edge is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. Edge is a basic feature of image. The image edges include rich information that is very significant for obtaining the image characteristics by object recognition. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. So, edge detection is a vital step in image analysis and it is the key of solving many complex problems. This paper, describes edge detection algorithms for image segmentation using various computing approaches which have got great fruits. Experimental results prove that Canny operator is better than Prewitt and Sobel for the selected image. Subjective and Objective methods are used to evaluate the different edge operators. The performance of Canny, Sobel and Prewitt Edge Detection are evaluated for detection of edges in digital images.

International Journal of Multimedia and Ubiquitous Engineering

One of the important objectives of image processing is to interpret the content of image efficiently and finds the meaningful and significant information from it. The much awareness has been received from various researchers in the field of image interpretation. One of the most severe step in image interpretation is to mine the edges information from the image appropriately. Edges are the fundamental features of the image and can be formed from the outlines of the object. Edge detection is generally used in image analysis and processing. There are several types of algorithm to detect the edges. In this paper, the comprehensive analysis is done on the several edge detection techniques such as Prewitt, Sobel, Canny, Roberts and Laplacian of Gaussian. It is experimentally observed that Canny edge detector is working well than others. This work is implemented on Matlab R2015a.

Journal of Electrical & Electronic Systems, 2015

Edge is the basic feature of image. Edges form the outline of an object. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. So edge detection is one of the most commonly used operations in image analysis and there are probably more algorithm for detecting edges. In this paper various edge detectors like Canny, Sobel, Roberts and Prewitt are compared. These operators are more susceptible to noise and do not give satisfactory result for face outline. For overcoming this disadvantage morphological method is studied and the result of edge detection using morphological method is compared with Canny edge detector, Sobel edge detector, Roberts edge detector and Prewitt edge detector.Wood and Glass Images are taken up as a special conditions for wider number of applications.

The aim of the paper is overview of methods for edge detection in image and their feature comparison. Each method was tested and compared on the set of artificial images. The noise and blur filter were added to these images in the next step. Also the real images were used. There are no objective criteria for edge detecting methods evaluation, so we designed our own ones. One of the detecting method was programmed with mistaken algorithm which led to discovering that edge detectors can be used not only in industrial application but also in graphical software.