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- 1. Pooja Sharma,Gurpreet Singh, Amandeep Kaur / International Journal of EngineeringResearch and Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.458-461458 | P a g eDifferent Techniques Of Edge Detection In Digital ImageProcessingPooja Sharma1,Gurpreet Singh2,Amandeep Kaur31‟2Student M.Tech ECE Punjabi University Patiala3Assistant Professor Department of ECE Punjabi University PatialaABSTRACTEdge detection is a process that detectsthe presence and location of edges constituted bysharp changes in intensity of an image. Edgesdefine the boundaries between regions in animage, which helps with segmentation and objectrecognition. Edge detection of an imagesignificantly reduces the amount of data andfilters out useless information, while preservingthe important structural properties in an image.The general method of edge detection is to studythe changes of a single image pixel in an area, usethe variation of the edge neighboring first orderor second-order to detect the edge. In this paperafter a brief introduction, overview of differentedge detection techniques like differentialoperator method such as sobel operator,prewitt’stechnique,Canny technique and morphologicaledge detection technique are given.1.IntroductionThe edge detection methods based ondifference operation are used widely in imageprocessing. It could detect the variation of graylevels, but it is sensitive to noise. Edge detection isan important task in image processing. It is a maintool in pattern recognition, image segmentation, andscene analysis. An edge detector is basically a highpass filter that can be applied to extract the edgepoints in an image. An edge in an image is a contouracross which the brightness of the image changesabruptly. In image processing, an edge is ofteninterpreted as one class of singularities. In afunction, singularities can be characterized easily asdiscontinuities where the gradient approachesinfinity. However, image data is discrete, so edgesin an image often are defined as the local maxima ofthe gradient[1]-[2]. Edge widely exists betweenobjects and backgrounds, objects and objects,primitives and primitives. The edge of an object isreflected in the discontinuity of the gray. Therefore,the general method of edge detection is to study thechanges of a single image pixel in a gray area, usethe variation of the edge neighboring first order orsecond-order to detect the edge. This method is usedto refer as local operator edge detection method.Edge detection is mainly the measurement, detectionand location of the changes in image gray. Imageedge is the most basic features of the image. Whenwe observe the objects, the clearest part we seefirstly is edge and line. According to thecomposition of the edge and line, we can know theobject structure. Therefore, edge extraction is animportant technique in graphics processing andfeature extraction. The basic idea of edge detectionis as follows: First, use edge enhancement operatorto highlight the local edge of the image. Then,define the pixel "edge strength" and set the thresholdto extract the edge point set. However, because ofthe noise and the blurring image, the edge detectedmay not be continuous[3].This paper discuses various techniques for EdgeDetection. Edge detection detects outlines of anobject and boundaries between objects and thebackground in the image. Edge is a boundarybetween two homogeneous regions. Edge detectionrefers to the process of identifying and locatingsharp discontinuities in an image.2.Different techniques of edge detection2.1.Differential operator methodDifferential operator can outstand greychange. There are some points where grey change isbigger. And the value calculated in those points ishigher applying derivative operator . So thesedifferential values may be regarded as relevant„edge intensity‟ and gatherthe points set of the edge through setting thresholdsfor these differential values[4]. Differential operatoris a classic edge detection method, which is basedon the gray change of image for each pixel in theirareas, using the edge close to the first-order orsecond order directional derivative to detect theedge. Differential operator edge detection isaccomplished by the convolution. The position offirst-order derivative in the image from light to darkor from dark to light has a downward or upwardstep, the changes of the gray value is relativelysmall in other locations, and the maximum ofmagnitude corresponds to the location of the edge.Both the theoretical of the application basis on: thefeature of first-order differential operator obtainsextreme in the step edge for the first-orderderivative in image and will be 0 in the roof-likeedge; otherwise, the second-order differentialoperator has the opposite values. First-orderoperator including: Sobel operator, Prewitt operator,Roberts operator, etc. Second order operatorincluding: LOG operator, Canny operator, etc. First-
- 2. Pooja Sharma,Gurpreet Singh, Amandeep Kaur / International Journal of EngineeringResearch and Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.458-461459 | P a g eorder derivative corresponds to a gradient, first-order derivative operator is the gradient operator[5].2.1.1 .Sobel Edge Detection OperatorThe Sobel edge detection operationextracts all of edges in an image, regardless ofdirection. Sobel operation has the advantage ofproviding both a differencing and smoothing effect.It is implemented as the sum of two directional edgeenhancement operations. The resulting imageappears as an unidirectional outline of the objects inthe original image. Constant brightness regionsbecome black, while changing brightness regionsbecome highlighted. Derivative may beimplemented in digital form in several ways.However, the Sobel operators have the advantage ofproviding both a differencing and a smoothingeffect. Because derivatives enhance noise, thesmoothing effect is particularly attractive feature ofthe Sobel operators[7] .Fig.1The operator consists of a pair of 3×3 convolutionkernels as shown in Fig. 1. One kernel is simply theother rotated by 90°. The kernels can be appliedseparately to the input image, to produce separatemeasurements of the gradient component in eachorientation ( Gx and Gy) The gradient magnitude isgiven by:Typically, an approximate magnitude is computedusing:which is much faster to compute.[6]-[7]2.1.2.Robert’s cross operatorThe Roberts Cross operator performs a simple,quick to compute, 2-D spatial gradient measurementon an image. Pixel values at each point in the outputrepresent the estimated absolute magnitude of thespatial gradient of the input image at that point. Theoperator consists of a pair of 2×2 convolutionkernels as shown in Figure. One kernel is simply theother rotated by 90°. This is very similar to theSobel operator.Fig.2These kernels are designed to respond maximally toedges running at 45° to the pixel grid, one kernel foreach of the two perpendicular orientations. Thekernels can be applied separately to the input image,to produce separate measurements of the gradientcomponent in each orientation ( Gx and Gy). Thesecan then be combined together to find the absolutemagnitude of the gradient at each point and theorientation of that gradient. The gradient magnitudeis given by:although typically, an approximate magnitude iscomputed using:which is much faster to compute [7].2.1.3.Laplacian of GaussianThe Laplacian is a 2-D isotropic measureof the 2nd spatial derivative of an image. TheLaplacian of an image highlights regions of rapidintensity change and is therefore often used for edgedetection. The Laplacian is often applied to animage that has first been smoothed with somethingapproximating a Gaussian Smoothing filter in orderto reduce its sensitivity to noise. The operatornormally takes a single gray level image as inputand produces another gray level image as output.The Laplacian L(x,y) of an image with pixelintensity values I(x,y) is given by:Since the input image is represented as a set ofdiscrete pixels, we have to find a discreteconvolution kernel that can approximate the secondderivatives in the definition of the Laplacian. Threecommonly used small kernels are shown in Fig.3.Fig. 3Three commonly used discreteapproximations to the Laplacian filter. Becausethese kernels are approximating a second derivativemeasurement on the image, they are very sensitiveto noise. To counter this, the image is oftenGaussian Smoothed before applying the Laplacianfilter. This pre-processing step reduces the highfrequency noise components prior to thedifferentiation step. In fact, since the convolutionoperation is associative, we can convolve theGaussian smoothing filter with the Laplacian filterfirst of all, and then convolve this hybrid filter withthe image to achieve the required result[7].The 2-DLoG function centered on zero and with Gaussianstandard deviation as the form:
- 3. Pooja Sharma,Gurpreet Singh, Amandeep Kaur / International Journal of EngineeringResearch and Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.458-461460 | P a g e2.1.4. Prewitt operatorPrewitt operator is a discretedifferentiation operator, computing anapproximation of the gradient of the image intensityfunction. At each point in the image, the result ofthe Prewitt operator is either the correspondinggradient vector or the normal of this vector. ThePrewitt operator is based on convolving the imagewith a small, separable, and integer valued filter inhorizontal and vertical direction and is thereforerelatively inexpensive in terms of computations.Fig.4This operator is similar to Sobel operator[7].2.1.5 . Canny OperatorThe Canny edge detector is widelyconsidered to be the standard edge detection methodin the industry. It was first created by John Cannyfor his Masters thesis at MIT in 1983. Canny sawthe edge detection problem as a signal processingoptimization problem, so he developed an objectivefunction to be optimized [8]. The solution to thisproblem was a rather complex exponential function,but Canny found several ways to approximate andoptimize the edge-searching problem. The steps inthe Canny edge detector are as follows:1. Smooth the image with a two dimensionalGaussian. In most cases the computation of a twodimensional Gaussian is costly, so it isapproximated by two one dimensional Gaussians,one in the x direction and the other in the ydirection.2. Take the gradient of the image. This showschanges in intensity, which indicates the presence ofedges. This actually gives two results, the gradientin the x direction and the gradient in the y direction.3. Non-maximal suppression- Edges will occur atpoints the where the gradient is at a maximum.Therefore, all points not at a maximum should besuppressed. In order to do this, the magnitude anddirection of the gradient is computed at each pixel.Then for each pixel check if the magnitude of thegradient is greater at one pixels distance away ineither the positive or the negative directionperpendicular to the gradient. If the pixel is notgreater than both, suppress it.Fig.54. Edge Thresholding- The method of thresholdingused by the Canny Edge Detector is referred to as"hysteresis". It makes use of both a high thresholdand a low threshold. If a pixel has a value above thehigh threshold, it is set as an edge pixel. If a pixelhas a value above the low threshold and is theneighbour of an edge pixel, it is set as an edge pixelas well. If a pixel has a value above the lowthreshold but is not the neighbor of an edge pixel, itis not set as an edge pixel. If a pixel has a valuebelow the low threshold, it is never set as an edgepixel[9]-[10].2.2.Mathematical Morphology for EdgeDetectionThe morphological basic idea is: use a certain formof structuring elements to measure and extract theimage correspond shape, achieve the image analysisand identification purposes. Mathematicalmorphology can be used to simplify the image data,maintain the basic shape of the image features, atthe same time remove the image has nothing to dowith the part of the research purposes.Morphological operations could enhance contrast,eliminate noise, refinement and skeleton extraction,region filling and object extraction, boundaryextraction and so on. The classical differentialoperator to detect the image most have a certainwidth , can not be directly used to measure.Morphology often used to edge detection.The mathematical basis of mathematicalmorphology and language is set theory, there arefour of basic operations: inflate, decay, open andclosure. Based on these basic operations can also becombined into a variety of morphological methodsto calculation[5].The basic morphological operations,namely erosion, dilation, opening, closing etc. areused for detecting, modifying, manipulating thefeatures present in the image based on their shapes.The shape and the size of SE play crucial roles insuch type of processing and are therefore chosenaccording to the need and purpose of the associatedapplication[11]. The key of Image edge detectionbased on morphology is how to combinedmorphological edge detection operator use themorphological structure of various basic operations,and how to select the structural elements to bettersolve the edge detection accuracy and thecoordination of anti-noise performance[12]. Binary
- 4. Pooja Sharma,Gurpreet Singh, Amandeep Kaur / International Journal of EngineeringResearch and Applications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.458-461461 | P a g eimage of the object recognition rely on to determinewhether the adjacent pixels in the image, MATLABoften use two kinds of connections: 4-connect and8-connect. In the 4-connect agreed approach, onlythe vertical direction of the four adjacent Pixels arethe pixels may be connected; but in the 8-connectagreed approach, users expect access to the 8 pixelsadjacent pixels are the pixels may be connected[5].Mathematical morphological method can extractmuch better the edge of an object than othermethods.ConclusionIn this paper many edge detection methodslike Sobel operator technique, Roberts technique,Prewitt technique, Canny technique, andMorphology based edge detection technique arediscussed. Choosing a suitable method for edgedetection is based on the some environmentalconditions. Each technique have its own advantagesand disadvantages .But mathematical morphology isbetter technique than differential method. Thisreview paper will be helpful for the researchers inunderstanding the concept of edge detection who arenew in this field.Reference[1]. J. J. Benedetto,M.W. Frazier, 1994,“Wavelets Mathematics and Applications,”CRC Press, Inc.[2] K. A. Stevens, 1980, “Surface perceptionfrom local analysis of texture andcontour,”Artificial Intell. Lab., Mass. Instr.Technol., Cambridge, Tech. Rep. AI-TR-512.[3]. Wenshuo Gao, Lei Yang, XiaoguangZhang, Huizhong Liu, “An Improved SobelEdge Detection”, 2010 IEEE, 978-1-4244-5540-9/10[4] Wang Zhengyao, "Edge detection of digitalimage, Master paper ", Xi‟an: i‟an JiaotongUniversity,2003.[5]. You-yi Zheng, Ji-lai Rao, Lei Wu, “EdgeDetection Methods in Digital ImageProcessing” The 5thIEEE InternationalConference on Computer Science &Education, 978-1-4244-6005-2/10[6]. YAHIA S. AL-HALABI, HESHAMJONDI ABD, “NEW WAVELET-BASEDTECHNIQUES FOR EDGEDETECTION”, Journal of Theoretical andApplied Information Technology ,2005 -2010 JATIT & LLS.[7]. Srikanth Rangarajan, “Algorithms ForEdge Detection”[8]. R. Owens, "Lecture 6", Computer VisionIT412, 10/29/1997.[9]. Ehsan Nadernejad. Hamid Hassanpour,Sara Sharifzadeh,“Edge DetectionTechniques:Evaluations and Comparisons”Applied Mathematical Sciences, Vol. 2,2008, no. 31, 1507 – 1520.[10]. S. Price, "Edges: The Canny EdgeDetector", July 4, 1996.[11]. C.NagaRaju , S.NagaMani, G.rakeshPrasad, S.Sunitha, “Morphological EdgeDetection Algorithm Based on Multi-Structure Elements of DifferentDirections”, International Journal ofInformation and CommunicationTechnology Research ©2010-11 IJICTJournal, Volume 1 No. 1, May 2011.[12]. Hui Yang, Jiwu Zhang. “MathematicalMorphology in Edge DetectionApplication,” Journal of LiaoningUniversity (Natural Science Edition),vol.32, no.1, pp. 50-53, 2005.

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