Study and Comparison of Various Techniques of Image Edge Detection

Transcription

1 Gureet Sngh et al Int. Journal of Engneerng Research Applcatons RESEARCH ARTICLE OPEN ACCESS Study Comparson of Varous Technques of Image Edge Detecton Gureet Sngh*, Er. Harnder sngh** *(Department of Electroncs Communcaton Engneerng, Punab Unversty, Patala, Punab, Inda.) ** (Department of Electroncs Communcaton Engneerng, Punab Unversty, Patala, Punab, Inda.) ABSTRACT An edge may be defned as a set of connected pxels that forms a boundary between two dsonts regons. Image Edge detecton reduces the amount of data flters out useless nformaton, whle preservng the mportant structural propertes n an mage. Edge detecton plays an mportant role n dgtal mage processng practcal aspects of our daly lfe. In ths paper we studed varous edge detecton technques as Prewtt, Robert, Sobel, LoG Canny operators. On comparng them we conclude that canny edge detector performs better than all other edge detectors on varous aspects such as t s adaptve n nature, performs better for nosy mage, gves sharp edges, probablty of detectng false edges. Keywords Canny detector, edge, edge detector, Prewtt operator, Roberts operator, Sobal opertor I(, ) I. INTRODUCTION Edge detecton s a basc tool used n mage processng study, for feature detecton extracton, whch am to dentfy ponts n a mage where brghtness of dgtal mage changes sharply fnd dscontnutes. The purpose of mage edge detecton s sgnfcantly reducng the amount of data n an mage data preserves the structural propertes for mage processng. Edge detecton s dffcult to apply n nosy mages, snce both the nose edges contan -frequency content. Attempts to reduce the nose from mage result n blurred dstorted edges. Operators used on nosy mages are typcally much larger n scope, so they can enough data to dscount localzed nosy mage pxels. Therefore, the obectve s to compare varous edge detecton technques analyze the performance n terms of examples. II. THEORETICAL CONCEPTS: There are so many ways to perform edge detecton. However, dfferent methods of edge detecton may be grouped nto two categores: 2.1 Frst Order Dervatve based Edge Detecton (Gradent method): It s based on the use of a frst order dervatve or can say gradent based. The magntude of gradent computed gves edge strength the gradent drecton that s always perpendcular to the drecton of mage edge. If I (, ) be the nput mage, then mage gradent s calculated by folng formula; I(, ) I(, ) I(, ) Where: I (, ) s the gradent n drecton. s the gradent n drecton. The gradent magntude can be calculated by the formula: G G G 2.2 Second Order Dervatve Based Edge Detecton (Laplacan based Edge Detecton): Ths method search for zero crossngs n the second dervatve of the mage to fnd out edges. An mage edge has the one-dmensonal shape of a ramp fnd out the dervatve of the mage can lght ts locaton. Ths method s characterstc of the gradent flter famly of edge detecton flters. A pxel locaton s only declared an edge locaton, f the value of ts gradent exceeds some threshold. As mentoned earler, edges have er pxel ntensty values than those are surroundng t. So once a threshold s set, the gradent value wth the threshold value can be compared an edges can be detected whenever the threshold s exceeded. Furthermore, when the frst dervatve s at a maxmum peak, the second dervatve s zero. As a result, another alternatve to fndng the locaton of an mage edge s to locate zeros n the second dervatve of mage. 908 P a g e

2 Gureet Sngh et al Int. Journal of Engneerng Research Applcatons (A) deal step edge, (b) frst order dervaton (c) second order dervaton. Ths approach uses zero-crossng operator whch acts by locatng zeros of the second dervatves of mage I(, ). The dfferental operator s used n the so-called zero-crossng edge detectors, I I 2 I Thresholdng allocates a range of pxel values to obect of nterest. It works best wth greyscale mages that utlze the whole range of greyscale. For the mage I(,), the threshold mage g(, ) s defned as, 1f g(, ) 0f I, I, Where T s the threshold value. III. T T EDGE DETECTION TECHNIQUES Robert, Sobel, Prewtt are classfed as classcal operators whch are smple easy to operate but ly senstve to nose. Classcal operators canny operator are under the category of frst order dervatve based edge detecton (Gradent method). Marr-Hldreth edge detector s a gradent based operator whch uses the Laplacan to take the second dervatve of an mage. 3.1 Roberts Operator: It s a gradent based operator. It computes the sum of the squares of the dfference between dagonally adacent mage pxels through dscrete dfferentaton then calculate approxmate gradent of an mage. The nput mage s convolved wth default kernels of operator gradent magntude drectons are computed. It uses folng 2 x2 two kernels, D The advantage of ths operator s smplcty but havng small kernel t s ly senstve to nose not much compatble wth today s technology. 2.2 Sobal Operator: Sobel operator s a dscrete dfferentaton operator used to calculate an approxmaton of the gradent of an mage ntensty functon for edge detecton. At each pxel of an mage, t gves ether the correspondng gradent vector or normal to the vector. ths convolves the nput mage wth kernel computes the gradent magntude drecton. It uses folng 3x3 two kernels, D As compared to Roberts operator have s computaton ablty but as t has large kernel so t s less senstve to nose as compared to Roberts operator. As havng larger mask, errors due to nose are reduced by local averagng wthn the neghborhood of the mask. F chart of general algorthm for classcal operators START Read the mage convolve wth flter Convolve the resultant mage wth chosen mask n -axs Convolve the resultant mage wth chosen mask n -axs Set the threshold value T For a pxel say M(,) Compute the gradent magntude say G Is G>T Mark pxel as an edge END No Consder the next neghbor pxel. 909 P a g e

3 Gureet Sngh et al Int. Journal of Engneerng Research Applcatons 2.3 Prewtt Operator: The functon of Prewtt edge detector s almost same as of sobel detector but have dfferent kernels: D Prewtt edge operator gves better performance than that of Sobel operator Roberts operator. 3.4 Canny Operator: Canny edge detector s a advanced algorthm derved from the prevous work of Marr Hldreth. It s an optmal edge detecton technque as provde good results n detecton, n clear response n localzaton. It s wdely used n current mage processng technques used n everywhere wth further mprovements. F chart of canny edge detecton algorthm START: Read the nput mage. SMOOTHING: Removng nose by Gaussan flter. COMPUTE GRADIENTS: Edge should be marked where the gradents of the mage has large. NON MAXIMUM SUPRESSION: Only local maxma should be marked as edge. THRESHOLDING: Fnal edges are determned by suppressng all not connected edges to strong one. END: Input mage resulted nto edge extracted mage. Canny edge detecton algorthm STEP I: Nose reducton by smoothng Nose contaned mage s smoothed by convolvng the nput mage I (, ) wth Gaussan flter G. Mathematcally, the smooth resultant mage s gven by, F(, ) G*I(, ) Prewtt operators are smpler to operator as compared to sobel operator but more senstve to nose. STEP II: Fndng gradents In ths step we detect the edges where the change n greyscale ntensty s maxmum. Requred areas are determned wth the help of gradent of an mage. Generally, Sobel operator s used to determne the gradent at each pxel of smoothed mage. Sobel operators n drectons are gven be, D These sobel masks are convolved wth smoothed mage gves gradents n drectons. G D *F(, ) G D *F(, ) Therefore edge strength or magntude of gradent of a pxel s gven by, G G G The drecton of gradent s gven by, G arctan G G G are the gradents n the drectons respectvely. STEP III: Non maxmum suppressons: Non maxmum suppresson s carred out to preserves all local maxma n gradent mage, deletng everythng else, ths results n thn edges. For a pxel M (, ): Frstly round the gradent drecton nearly 45, then compare the gradent magntude of the pxels n postve negatve gradent drectons.e f gradent drecton s east then compare gradent of the pxel wth west drecton say E (, ) W (, ) respectvely. If the edge strength of mage pxel M (, ) s larger than that of E (, ) W (, ), then preserves the value of gradent mark M (, ) as edge pxel, f not then suppressed. STEP IV: Hysteress thresholdng: The output of non-maxma suppresson stll contans the local maxma created by nose n mage. Instead choosng a sngle threshold, for avodng the problem of streakng two thresholds t t are used. For a pxel M(, ) havng gradent magntude G folng condtons exsts to detect pxel as edge: 910 P a g e

4 Gureet Sngh et al Int. Journal of Engneerng Research Applcatons If G t than dscard the edge. 4.1 Image Lna: If G than t G keep the edge. t any of ts neghbors If t n a 3 3 regon around t have gradent magntudes greater than t keep the edge. If none of pxel (x, y) s neghbors have gradent magntudes but at least one falls between t t search the 5 5 regon to see f any of these pxels have a magntude greater than t. If so, keep the edge. Orgnal Sobal Roberts Else, dscard the edge. 3.5 Laplacan of Gaussan or Marr Hldrth Operator: The Marr-Hldreth edge detector was a very popular edge operator before the Canny proposed hs algorthm. It s a gradent based operator whch uses the Laplacan to take out the second dervatve of an mage. It works on zero crossng method. LOG uses both Gaussan laplacan operator so that Gaussan operator reduces the nose laplacan operator detects the sharp edges n an mage. Where The Gaussan functon s defned by the formula: 1 G(, ) exp LoG G(, ) G(, ) 4.2 Image Butterfly: Orgnal Sobal Roberts 2 exp The Marr Hldreth operator, however, suffers from two man lmtatons. It gves responses that do not correspond to edges, so-called "false edges", localzaton error may be severe at curved edges. 4.3 Image Brd: IV. FIGURES AND TABLES Dfferent edge detecton technques are compared by usng dfferent mages. The performance of these technques may also evaluated n terms of SNR factor. Orgnal Sobal Roberts 911 P a g e

5 Gureet Sngh et al Int. Journal of Engneerng Research Applcatons 4.4 Performance evaluaton: Flters Sobal Roberts SNR(4.1) SNR(4.2) SNR(4.3) V. CONCLUSION In ths paper we have studed evaluate dfferent edge detecton technques. We have seen that canny edge detector gves result better as compared to all other technques. It s more mmune to nose, much adaptve n nature, provdes good localzaton detects sharper edges as compared to all others technques. Thus t s consdered as optmal edge detecton technque hence lot of work mprovement on ths algorthm has been done also further mprovements are possble as an mproved canny algorthm. Improved sobal algorthm for mage fuson[9]. From the results obtaned, t can be concluded that the canny flter proved to be very effectve for edge enhancement purposes. It s observed that for the three mages used, there are less false edges n the canny flter. The results obtaned by canny flter are better n terms of ntensty of edges than the Sobel log flters. Prewtt havng SNR but t lost most of edges. Canny gves good edge detecton but poor SNR. So further need for mprovement towards senstvty to nose, adaptvely n nature, localzaton detecton sharper edges. Improved canny s proposed already but the challenge s here to propose a such type of flter whch s easy to mplement wth certan changes reduces false edges wth mprovement SNR rato. Journal of Image Processng (IJIP), Volume (3) : Issue (1) [3] Hul Zhao, Guofeng Qn Xngan Wang (2010), Improvement of Canny Algorthm Based on Pavement Edge Detecton, 3rd Internatonal Congress on Image Sgnal Processng (CISP2010). [4] Muthukrshnan.R M.Radha (2011), Edge detecton technques for mage segmentaton, Internatonal Journal of Computer Scence & Informaton Technology (IJCSIT) Vol 3, No 6, Dec [5] S. Lakshm & V.Sankaranarayanan (2010), A Study of edge detecton technques for segmentaton computng approaches, Computer Aded Soft Computng Technques for Imagng Bomedcal Applcatons, [6] P. Thakare (2011), A Study of Image Segmentaton Edge Detecton Technques, Internatonal Journal on Computer Scence Engneerng, Vol 3, No.2, [7] U. Sehgal (2011), Edge detecton technques n dgtal mage processng usng Fuzzy Logc, Internatonal Journal of Research n IT Management, Vol.1, Issue 3, [8] K. J. Pthadya, C. K. Mod & J. D. Chauhan (2011), Selectng the Most Favourable Edge Detecton Technque for Lqud Level Inspecton n Bottles, Internatonal Journal of Computer Informaton Systems Industral Management Applcatons (IJCISIM) ISSN: Vol.3, pp , [9] C. Deng, W. Ma & Y. Yn (2011), An Edge Detecton Approach of Image Fuson Based on Improved Sobel Operator, 4th Internatonal Congress on Image Sgnal Processng, pp [10] E. Argyle., Technques for edge detecton, Proc. IEEE, vol. 59, pp , REFERENCES [1] Canny, J. F (1986), A computatonal approach to edge detecton, IEEE Transacton on Pattern Analyss Machne Intellgence, 8, [2] Raman Man Dr. Hmanshu Aggarwal, Study Comparson of Varous Image Edge Detecton Technques, Internatonal 912 P a g e